JP2000065924A - Target correlation-integrating apparatus - Google Patents

Abstract

PROBLEM TO BE SOLVED: To improve a reliability on correlation integration results of targets by combining an apparatus conforming to a target moving straight and an apparatus conforming to a target moving in a curve so as to maintain the target not only when the target moves straight, but when the target curves and surely controlling the apparatuses. SOLUTION: The apparatus includes a judging device 9 for judging whether a target moves straight or in a curve, a straight movement hypothesis- generating, reducing and reliability-calculating device and a curve movement hypothesis-generating, reducing and reliability-calculating device 10 for processing to generate, reduce a hypothesis and calculate a reliability on the hypothesis on the basis of the result judged by the judging device 9. The integrating apparatus judges from a previous motion route of a management target whether the management target makes a straight motion or a curve motion henceforth, sorts each management target to a process appropriate for the straight motion when the straight motion is estimated and a process appropriate for the curve motion when the curve motion is estimated, and carries out a process of generating, reducing a hypothesis and calculating a reliability on the hypothesis appropriate for the motion of each management target.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a target correlation integration for performing correlation determination processing as to whether targets observed by a plurality of observation devices are the same target or different targets, and integrating the targets when they are determined to be the same target. It concerns the device.

FIG. 19 is a diagram for explaining the background of the present invention. In FIG. 19, reference numeral 1 denotes a first target observation device that outputs the position and orientation of a detection result from an unnecessary signal such as a target and clutter as target observation information, and 2 is different from the first target observation device. The n-th target observation device arranged at the position, 38 is a target to be observed by each target observation device, 39 is a target observed by the first target observation device, and 40 is an n-th target observation device. The target observed by the target observation device is a target correlation integration device 41 that performs correlation integration of the target observed by the observation device.

In FIG. 19, when there are a plurality of observation devices (a first observation device and an n-th observation device), and when the plurality of observation devices observe the same target, the accuracy of each observation device and the installation location of each observation device are determined. As a result, the target specifications such as the position and speed of the target captured by each observation device do not always have the same value.
Therefore, a correlation determination process is performed to determine whether the targets captured by each observation device are the same or different, and only when it is determined in the determination that each target captured by each observation device is the same target, A target correlation integration device that integrates and processes the specifications of the target and treats them as the same target is required. In particular, the present invention separates the case where the target is expected to go straight and the case where the target is expected to turn in order to integrate the more likely targets, and applies the optimal algorithm to each,
Furthermore, by considering the terrain and weather conditions which are obstacles to the movement of the target as one of the movement environment elements of the target, the correlation determination of the target more realistically is performed, and the information is integrated and processed. This is related to target correlation integration incorporating a device.

[0004]

2. Description of the Related Art FIG. 21 is a processing procedure showing a target correlation integration method of a conventional target correlation integration apparatus, and FIG. 20 is an example of a configuration diagram of a target correlation integration apparatus corresponding to the conventional target correlation integration method of FIG. .

[0005] In Fig. 20, reference numeral 1 denotes a first target observation device that outputs the position and orientation of a detection result from unnecessary signals such as a target and clutter as target observation information, and 2 denotes the first target observation device. A target observation information converter 3 that converts target observation information into a coordinate system for performing correlation integration and performs pre-processing for correlation integration; 4 is a motion specification correlation determination and correlation group generator for determining whether or not the target observation information is correlated with the prediction information of the management target and generating a correlation group of the observation target and the management target. And 5
The hypothesis generation that generates a combination of the correlation between the observation target and the management target having a correlation assuming that the management target goes straight, and the reliability of each hypothesis based on the hypothesis, the target observation information, and the management observation target information. Hypothesis reliability calculation to calculate,
A straight-forward hypothesis generation / reduction and reliability calculator for performing hypothesis reduction based on the hypothesis reliability in the hypothesis that is more likely, and 6 at the current time from the prediction information of the management target and the target observation information of the observation target 7 is a target motion specification smoother that calculates smoothing information such as the position and velocity of the target motion specification, and 7 is a target motion specification smoother that determines that the observation target and the management target match in the determined hypothesis. A target integrator 8 integrates the targets, and 8 is a target motion specification predictor that calculates prediction information such as the position and speed of the target motion data at the time when the target is next observed.

In FIG. 21, the conventional target correlation integration method uses an initial value of target smoothing information such as a smoothed value of a target position / velocity and a smoothed error covariance matrix based on the Kalman filter theory based on the observed position of the observed target in processing 17. The target prediction information covariance obtained by inputting the target observation information of the new target in the processing 18 and estimating the prediction value of the target position / velocity and the error of the prediction value at the current time of the target already managed in the processing 19 in the processing 18 The target prediction information such as a matrix is calculated, the motion data such as the target observation position is input as the target observation information in the processing 20, and the observation information is already determined in the processing 21 in order to determine whether the observation target has a correlation with the management target. Using the prediction error covariance matrix to determine whether or not to fall within the target existence prediction range centered on the predicted position ahead of the managed target The management targets are grouped, and a hypothesis is generated for the observation targets determined to be correlated in the process 22 and for each management target which observation target corresponds to which management target, a new target, or an unnecessary signal. , Processing 2
In step 3, the reliability of the hypothesis generated in step 22 is calculated based on the target prediction information of the management target and the target observation information of the observation target, and the hypothesis is reduced in step 24 according to the hypothesis reliability calculated in step 23. In step 25, target smoothing information such as a smoothed value of a target position / velocity and a smoothing error covariance matrix are calculated based on the Kalman filter theory. In step 26, the target is determined for all hypotheses in which the observation target and the management target match. The integration is performed, and this series of steps is repeated until the correlation integration is completed in the process 27.

In the description of FIG. 21, the managed target and the managed target are recognized as targets by the target integrator.
It means the target managed in the target correlation integration device in the future.

FIG. 22 shows a method of determining the correlation between the observation target and the management target in the process 21. In FIG. 22, x ^{i (+)} is the smooth position of the management target i, x ^{i (−)} is the predicted position of the management target i, Zi is the observation position of the management target i, and the range represented by the ellipse in the figure is the target. This represents the existence prediction range. In the process 21, in order to determine whether the management target and the observation target are correlated, first, x
Create a target predicted existence range centered on ^{i (-)} . The target prediction existence range is a prediction error range of the management target, and indicates the existence range of the management target at the prediction time. At this time, the prediction error covariance is used as the prediction error range. Therefore, if the observation target exists within this range,
This means that the observation target may be the same target as the management target that creates the target prediction existence range. The fact that there is a correlation means that the possibility exists.
For example, as shown in FIG. 22, the observation target 2 is within the target prediction existence range of the management target 1, and the observation target 2 has a correlation with the management target 1. On the other hand, the other observation targets do not exist within the target prediction existence range of the management target 1, and thus have no correlation with the management target 1. This operation is performed for all observation targets and management targets, and finally a combination of which observation targets and which management targets have a correlation is determined. This is to perform grouping. For example, as shown in FIG. 22, the management target 1 and the observation target 2 form one group, and the management target 2 and the observation targets 1 and 3 form another group. . At this time, the observation targets 4 and 5 are registered as a group having no correlation with any management target.

[0009]

In the conventional target correlation integrating device, when generating a hypothesis to determine what kind of target the observed target is, it is already managed by the target correlation integrating device. Since the management target has been predicted and calculated as performing a constant velocity linear motion, if the observed target is a curving target, the curving target falls within a target existence prediction range centered on the predicted position of the management target. Were not observed, making it difficult to maintain the target. Further, even if the advancing target is observed within the target existence prediction range and the target can be maintained, the reliability as the target is not so good, and other observation targets are more likely to be within the target existence prediction range. In some cases, the correlation between the observation target and the management target was correlated with the wrong one, such as being observed in a highly reliable part.

SUMMARY OF THE INVENTION The present invention has been made to solve the above-described problems, and provides an apparatus and a curve corresponding to straight traveling so that the target can be maintained not only when the target is curved but also when the target is curved. The purpose is to increase the reliability of the target correlation integration result by incorporating a corresponding device and controlling it appropriately.

Also, in order to determine a more reliable target, the topography and weather information are taken into consideration. In consideration of the possibility that the topography and weather conditions may become obstacles to the movement of the management target, the management target It is possible to improve the accuracy of correlation by judging which observation target should be correlated with the target. At the same time, it is possible to limit the number of observation targets to be correlated with the management target. Capability can be improved.

[0012]

According to a first aspect of the present invention, there is provided a target correlation integrating apparatus, which determines whether the management target performs a straight-line movement or a curved movement in the future based on a past movement path of the management target. If it is expected, processing suitable for straight-line movement is performed, and if turning is expected, each management target is allocated to processing suitable for bending movement, and hypothesis generation and reduction suitable for movement of each management target and A straight-forward hypothesis generation for performing hypothesis reliability calculation processing based on a target straight-line / music determination unit for performing the determination and a result determined by the determination unit and performing hypothesis generation / reduction processing and hypothesis reliability calculation processing Achieved by providing a reduction and reliability calculator and a transposition hypothesis generation / reduction and reliability calculator.

Further, the target correlation integration device according to the second invention considers a predetermined terrain and weather condition as an obstacle in the movement of the target, and sets the obstacle on the course of the target from the past movement path of the management target. If there is a terrain, the target takes a motion to avoid the terrain, and if it is a weather condition such as clouds, it does not always avoid it, so a traveling path with a predetermined ambiguity is set, and the traveling path is set. It is determined whether the management target performs a straight-line movement or a curved movement in the future, and if straight-ahead is expected, processing suitable for a straight-line movement is performed. Appropriate processing is distributed for each management goal, and hypothesis generation and
A terrain and weather information generator for creating predetermined terrain and weather information, and a target straight / curvature determiner for considering the terrain and weather information for the purpose of performing reduction and hypothesis reliability calculation processing The straight-forward hypothesis generation / reduction and reliability calculator for performing hypothesis generation / reduction and hypothesis reliability calculation processing based on the result determined by the determiner,
This is achieved by providing a reduction and reliability calculator.

Further, the target correlation integration device according to the third aspect of the present invention performs hypothesis generation / reduction and hypothesis reliability calculation processing when the management target goes straight and turns, and the past motion path of the management target. It calculates the possibility of straight running and the possibility of turning as motion predictability from, and weights the motion predictability to the hypothesis reliability to select the hypothesis with the highest reliability. Hypothesis generation
A straight-forward hypothesis generation / reduction and reliability calculator that performs reduction and hypothesis reliability calculation processing, and a hypothesis generation / reduction and reliability calculator that performs hypothesis generation / reduction and hypothesis reliability calculation processing corresponding to a turn This is achieved by providing a target straight / turn selector for making the selection.

The target correlation integrating device according to a fourth aspect of the present invention performs hypothesis generation / reduction and hypothesis reliability calculation processing when the management target goes straight and turns, and the past motion path of the management target. Calculate the possibility of straight ahead and the possibility of turning as motion predictability from, calculate by weighting the motion predictability to the hypothesis reliability, and use the result as a hypothesis of management target and observation target A straight-forward hypothesis generation / reduction and reliability calculator that performs hypothesis generation / reduction and hypothesis reliability calculation processing for straight ahead, and a curved hypothesis generation that performs hypothesis generation / reduction and hypothesis reliability calculation processing for advancing Achieved by having a target rectilinear / curvature fuser that fuses the hypotheses with the reduction and reliability calculator.

Further, the target correlation integrating device according to the fifth invention performs hypothesis generation / reduction and hypothesis reliability calculation processing when the management target goes straight and turns, and the result of each hypothesis is determined by a predetermined topography. In consideration of the fact that there is actually a mountain, etc., compared to the weather conditions and the hypothesis is unreliable, the motion prediction is based on the past motion path of the management target to predict the possibility of going straight and turning. Calculate as the possibility, weight the motion prediction possibility to the hypothesis reliability and select the hypothesis with the highest reliability, and perform straight-forward hypothesis generation / reduction and hypothesis reliability calculation processing Hypothesis generation / reduction and reliability calculator
This is attained by providing a trajectory hypothesis generation / reduction and reliability calculator that performs reduction and hypothesis reliability calculation processing, and a target straight / turnover selector that performs the above selection.

Further, the target correlation integrating device according to the sixth invention performs hypothesis generation / reduction and hypothesis reliability calculation processing when the management target goes straight and turns, and the result of each hypothesis is a predetermined topography. Considering that there is actually a mountain etc. compared to the weather conditions and the hypothesis is unreliable, it is possible to predict the possibility of going straight and turning from the past movement path of the management target. The hypothesis reliability is calculated by weighting the motion predictability to the hypothesis reliability, and the result is used as a hypothesis for the management target and the observation target. The straight-forward hypothesis generation / reduction and reliability calculator that performs the calculation process and the hypothesis generation / reduction and reliability calculator that performs the hypothesis generation / reduction and hypothesis reliability calculation process corresponding to the advancing are combined with the hypothesis. Go straight to target / It is achieved by providing the advance fusion device.

[0018]

DESCRIPTION OF THE PREFERRED EMBODIMENTS Embodiment 1 1 and 2 are a configuration diagram and a processing flow diagram, respectively, showing Embodiment 1 of the present invention. In FIG. 1, reference numeral 1 denotes a first target observation device that outputs the position and orientation of a detection result from an unnecessary signal such as a target and clutter as target observation information, and 2 is different from the first target observation device. N-th position
3 is a target observation information converter for converting target observation information into a coordinate system for performing correlation integration and performing pre-processing for correlation integration, and 4 is a target observation information converter for managing the target observation information as a management target. A motion specification correlation determination and correlation group generator that determines whether or not there is a correlation with the prediction information and generates a correlation group between the observation target and the management target. As between the observation target and the management target having a correlation, a hypothesis generation for generating a combination of correlations and a hypothesis reliability calculation for calculating the reliability of each hypothesis based on the hypothesis and the target observation information and the management observation target information, A straight-forward hypothesis generation / reduction and reliability calculator for performing hypothesis reduction based on the hypothesis reliability based on the hypothesis reliability, and the current time based on the prediction information of the management target and the target observation information of the observation target. In 7 is a target motion specification smoother for calculating smoothing information such as the position / velocity of the target motion data. Reference numeral 7 denotes a target motion specification smoother which is determined in the determined hypothesis that the observation target and the management target match. A target integrator 8 for integrating targets, a target motion specifier 8 for calculating prediction information such as the position / velocity of the target motion data at the time when the target is next observed, and 9 a target motion specifier. A target straight / turning determiner for determining whether the management target is a straight-ahead target or a turning target. Reference numeral 10 denotes a combination of a correlation between an observation target and a management target having a correlation when it is determined to be a turning target. Hypothesis generation and hypothesis reliability calculation for calculating the reliability of each hypothesis based on the hypothesis, target observation information and management observation target information, and a hypothesis that is more reliable among the hypotheses based on the hypothesis reliability. The hypothesis generation / reduction and the It is a degree calculator.

In FIG. 1 and FIG. 2, when target observation information is input from any of the predetermined target observation devices, the target observation information is subjected to coordinate conversion into a coordinate system for performing correlation integration.
Based on the Kalman filter theory based on the target observation information such as the target position, calculate the smoothed value of the target position / velocity and the initial value of the smoothed error covariance matrix (device 3, processing 17), and when the target is detected first , The target observation information is input as a new target (device 3, processing 18). For the already managed target, target prediction information at the next observation time is calculated (apparatus 8, processing 19), and the observation information of the target captured at that time is input (apparatus 1 or apparatus 2, processing 20). In order to determine the correlation between which observation target may correspond to which management target, the correlation determination of which observation target falls within the target predicted existence range centered on the predicted position of the management target and its A correlation group is determined (apparatus 4, processing 21) in which the observation target and the management target in which the correlation determination result is duplicated are set as one group, and the future movement of the management target is performed based on the past movement path of the management target. It is determined whether the motion is a curving movement (device 9, processing 28), and for each of the correlation groups, it is highly likely that a predetermined observation target is a management target in the group, a new target, or an unnecessary signal. The Kano hypothesis generating a hypothesis for the hypothesis and curvilinear progression for straight based on the determination result (device 5 or device 10, the processing 22
Or process 29), and calculate the reliability of the generated hypothesis based on the strength of correlation between the observation target, the management target, the new target, and the unnecessary signal based on the determination result (device 5 or device 10, process 23 or process 30) Then, in accordance with a predetermined hypothesis reduction method, the hypothesis is reduced (apparatus 5 or apparatus 10, processing 24 or processing 31) based on the hypothesis reliability calculated in processing 23 or processing 30, and processing 24 is performed. Or process 3
In the hypothesis reduced in step 1, the target smoothing information is calculated based on the Kalman filter theory for the pair of the observation target and the management target determined in the hypothesis (apparatus 6, processing 25),
The observation target and the management target are integrated to register the target information (apparatus 7, processing 26). This series of processing is repeated until the correlation integration ends (apparatus 3, processing 27).

FIG. 13 shows a method of determining whether the future exercise of the management target in the process 28 is a straight-line exercise or a curved exercise. In FIG. 13, reference numeral 42 denotes the past N of the management target.
The sampling position is indicated by a black circle, and the reference numeral 43 is indicated by a white circle as the predicted position of the management target. Past N of management target
When the sampling trajectory is aligned on a straight line within a certain error range, it is determined that the management target moves straight ahead, and the past sampling trajectory of the management target has a certain error range on a curvature curve. When it can be said that the management target moves in the future with the curvature, the movement of the management target tends to go straight or curve according to the trajectory of the past N samplings of the management target. Is determined.

Embodiment 2 FIG. 3 and 4 are a configuration diagram and a processing flow diagram, respectively, showing Embodiment 2 of the present invention. In FIG. 3, reference numeral 1 denotes a first target observation device that outputs the position and orientation of a detection result from unnecessary signals such as a target and clutter as target observation information, and 2 is different from the first target observation device. A target observation information converter 3 for converting the target observation information into a coordinate system for performing correlation integration and performing pre-processing for correlation integration; Determines whether the target observation information is correlated with the prediction information of the management target,
A motion specification correlation determination and correlation group generator for generating a correlation group between the observation target and the management target. Reference numeral 5 denotes a combination of the correlation between the observation target and the management target having a correlation assuming that the management target goes straight. Hypothesis generation to generate and hypothesis reliability calculation to calculate the reliability of each hypothesis based on the hypothesis and the target observation information and management observation target information, based on the hypothesis reliability the more likely hypothesis in the hypothesis A straight-forward hypothesis generation / reduction and reliability calculator for performing hypothesis reduction, a target 6 for calculating smooth information such as the position and velocity of the target motion data at the current time from the prediction information of the management target and the target observation information of the observation target. Reference numeral 7 denotes a motion parameter smoother, which is a target integrator for integrating targets with respect to those determined that the observation target and the management target match in the determined hypothesis;
Is a target motion specification predictor that calculates prediction information such as the position and velocity of the target motion data at the time when the target is observed next, and 10 has a correlation when it is determined to be a curve A hypothesis generation for generating a combination of correlations between the observation target and the management target, a hypothesis reliability calculation for calculating the reliability of each hypothesis based on the hypothesis, the target observation information and the management observation target information, and Is a curve hypothesis generation / reduction and reliability calculator for reducing a hypothesis based on the hypothesis reliability based on the hypothesis reliability, and 11 is a terrain for generating surrounding terrain and weather information observed by the target observation device. A weather information generator 12 is a target straight / turning determination unit that takes into account the terrain and weather information that determines whether the management target is a straight-ahead target or a turning target by using the terrain and weather information.

In FIG. 3 and FIG. 4, topography and weather information of a predetermined target observation device and an observation range of the target observation device are prepared (device 11, processing 32), and the information is obtained from any one of the predetermined target observation devices. When the target observation information is input, the target observation information is subjected to coordinate transformation into a coordinate system for performing correlation integration,
Based on the Kalman filter theory based on the target observation information such as the target position, calculate the smoothed value of the target position / velocity and the initial value of the smoothed error covariance matrix (device 3, processing 17), and when the target is detected first , The target observation information is input as a new target (device 3, processing 18). For the already managed target, target prediction information at the next observation time is calculated (apparatus 8, processing 19), and the observation information of the target captured at that time is input (apparatus 1 or apparatus 2, processing 20). In order to determine the correlation between which observation target may correspond to which management target, the correlation determination of which observation target falls within the target predicted existence range centered on the predicted position of the management target and its A correlation group is determined (apparatus 4, process 21) in which the observation target and the management target in which the correlation determination result is duplicated are set as one group, and the past movement path of the management target is determined in consideration of the terrain and weather information. It is determined whether the future movement of the management target is a straight movement or a curving movement (device 12, processing 33), and for each of the correlation groups, it is highly likely that the predetermined observation target is the management target in the group that is high. New goals for the or unwanted signals of one of the hypotheses generate hypotheses for hypotheses and curvilinear progression for straight based on the determination result (device 5 or device 10, the process 22 or process 29)
Then, the reliability of the generated hypothesis is calculated based on the strength of the correlation between the observation target and the management target, the new target, and the unnecessary signal based on the determination result (apparatus 5 or apparatus 10, processing 23 or processing 30), According to the hypothesis reduction method decided in advance,
The hypothesis is reduced based on the hypothesis reliability calculated in the processing 23 or the processing 30 (the apparatus 5 or the apparatus 10, the processing 24 or the processing 31), and the hypothesis reduced in the processing 24 or the processing 31 is determined in the hypothesis. Based on the set of the observation target and the management target, the target smoothing information is calculated based on the Kalman filter theory (device 6, processing 25), and the observation target and the management target are integrated to register the target information (device 7, processing 26). I do. This series of processing is completed by correlation integration (apparatus 3, processing 2
Repeat until 7).

FIG. 14 shows a method for judging whether the future exercise of the management target in the process 33 is a linear exercise or a curved exercise. In FIG. 14, reference numeral 42 denotes the past N of the management target.
A black circle indicates the sampling position, a white circle indicates the predicted position of the management target, and a weather obstacle such as terrain and cumulonimbus. When the trajectories of the past N samplings of the management target are aligned on a straight line within a certain error range, the management target is determined to move straight ahead in the future. To determine the curvature as moving with a certain curvature to avoid, in the case of atmospheric turbulence clouds and the like through which the obstacles can pass through, it is determined that the movement is straight ahead,
According to the characteristics of the obstacle on the predicted route, the effectiveness of the predicted route is determined, and when the route cannot be physically achieved, the route is treated as invalid, so that the management target tends to go straight. Or a tendency to turn.

Embodiment 3 FIG. 5 and 6 are a configuration diagram and a processing flow diagram, respectively, showing Embodiment 3 of the present invention. In FIG. 5, reference numeral 1 denotes a first target observation device that outputs the position and orientation of a detection result from unnecessary signals such as a target and clutter as target observation information, and 2 is different from the first target observation device. A target observation information converter 3 for converting the target observation information into a coordinate system for performing correlation integration and performing pre-processing for correlation integration; Determines whether the target observation information is correlated with the prediction information of the management target,
A motion specification correlation determination and correlation group generator for generating a correlation group between the observation target and the management target. Reference numeral 5 denotes a combination of the correlation between the observation target and the management target having a correlation assuming that the management target goes straight. Hypothesis generation to generate and a hypothesis reliability difference calculation to calculate the reliability of each hypothesis based on the hypothesis and the target observation information and the management observation target information, and the hypothesis reliability with the more likely hypothesis in the hypothesis A straight-forward hypothesis generation / reduction and reliability calculator that performs hypothesis reduction based on the prediction information of the management target and the target observation information of the observation target, and calculates smooth information such as the position / velocity of the target motion data at the current time. Is a target motion specification smoother to be performed, 7 is a target integrator that integrates the targets with respect to those determined that the observation target and the management target match in the determined hypothesis,
Reference numeral 8 denotes a target motion specification predictor that calculates prediction information such as the position and velocity of the target motion data at the time when the target is observed next, and 10 denotes a correlation when it is determined that the vehicle is turning. Between a certain observation target and a management target, a hypothesis generation for generating a combination of correlations, a hypothesis reliability calculation for calculating the reliability of each hypothesis based on the hypothesis and the target observation information and the management observation target information, and A hypothesis generation / reduction / reliability calculator for performing hypothesis reduction based on the hypothesis reliability among the more probable hypotheses; 13 is the straight-forward hypothesis generation / reduction / reliability calculator and the transposition hypothesis This is a target straight / turning selector that selects the result of the generation / reduction and reliability calculator by determining whether the management target is the straight ahead target or the turning target.

In FIG. 5 and FIG. 6, when target observation information is input from any of the predetermined target observation devices, the target observation information is subjected to coordinate conversion into a coordinate system for performing correlation integration, and the target position and the like are converted. Based on Kalman filter theory, based on the target observation information, calculate the smoothed value of the target position / velocity and the initial value of the smoothed error covariance matrix (device 3, processing 17). The target observation information is input (device 3, processing 18). For the already managed target, target prediction information at the next observation time is calculated (apparatus 8, processing 19), and the observation information of the target captured at that time is input (apparatus 1 or apparatus 2, processing 20). In order to determine the correlation between which observation target may correspond to which management target, the correlation determination of which observation target falls within the target predicted existence range centered on the predicted position of the management target and its A correlation group is determined in which the observation target and the management target for which the correlation determination results are duplicated are set as one group (apparatus 4, processing 21), and a predetermined observation target is determined for each of the correlation groups by any management target in the group. A hypothesis of whether there is a high possibility, a new target, or an unnecessary signal is taken into consideration when the management target goes straight and when turning with a predetermined turning acceleration. (Apparatus 5 or apparatus 10, processing 22 or processing 29), and the reliability of the generated hypothesis is calculated based on the correlation strength between the observation target, the management target, the new target, and the unnecessary signal (apparatus 5 or apparatus 10). , Processing 23 or processing 30)
Then, processing 2 is performed according to a predetermined hypothesis reduction method.
3 or hypothesis reduction based on the hypothesis reliability calculated in process 30 (device 5 or device 10, process 24 or process 31)
Performing, the future exercise of the management target from the past exercise path of the management target is likely to be a straight movement, the possibility of a curving movement is high, the case where it is assumed that the straight movement in combination with the hypothesis reliability Either the hypothesis or the hypothesis in the case of assuming a turn is selected (apparatus 13, processing 34), and processing 24 or processing 3 is performed.
In the hypothesis reduced in step 1, the target smoothing information is calculated based on the Kalman filter theory for the pair of the observation target and the management target determined in the hypothesis (apparatus 6, processing 25),
The observation target and the management target are integrated to register the target information (apparatus 7, processing 26). This series of processing is repeated until the correlation integration ends (apparatus 3, processing 27).

FIG. 15 shows a method of selecting whether the future motion of the management target in the process 34 is a straight-line motion or a curved motion. In FIG. 15, reference numeral 42 denotes the past N of the management target.
The sampling position is indicated by a black circle, and the reference numeral 43 is indicated by a white circle as the predicted position of the management target. Past N of management target
Assuming an expected course based on the trajectory of the sampling, calculate the ratio of the possibility that the target may go straight or turn, and calculate the ratio of the straight running and the straight running hypothesis reliability in the case of straight running. In the case of a turn, the ratio of the turn and the turn hypothesis reliability are integrated, and the larger value of the integration result is selected.

Embodiment 4 7 and 8 are a configuration diagram and a processing flow diagram, respectively, showing Embodiment 4 of the present invention. In FIG. 7, reference numeral 1 denotes a first target observation device that outputs the position and orientation of a detection result from an unnecessary signal such as a target and clutter as target observation information, and 2 is different from the first target observation device. A target observation information converter 3 for converting the target observation information into a coordinate system for performing correlation integration and performing pre-processing for correlation integration; Determines whether the target observation information is correlated with the prediction information of the management target,
A motion specification correlation determination and correlation group generator for generating a correlation group between the observation target and the management target. Reference numeral 5 denotes a combination of the correlation between the observation target and the management target having a correlation assuming that the management target goes straight. Hypothesis generation to generate and hypothesis reliability calculation to calculate the reliability of each hypothesis based on the hypothesis and the target observation information and management observation target information, based on the hypothesis reliability the more likely hypothesis in the hypothesis A straight-forward hypothesis generation / reduction and reliability calculator for performing hypothesis reduction, a target 6 for calculating smooth information such as the position and velocity of the target motion data at the current time from the prediction information of the management target and the target observation information of the observation target. Reference numeral 7 denotes a motion parameter smoother, which is a target integrator for integrating targets with respect to those determined that the observation target and the management target match in the determined hypothesis;
Is a target motion specification predictor that calculates prediction information such as the position and velocity of the target motion data at the time when the target is observed next, and 10 has a correlation when it is determined to be a curve A hypothesis generation for generating a combination of correlations between the observation target and the management target, a hypothesis reliability calculation for calculating the reliability of each hypothesis based on the hypothesis, the target observation information and the management observation target information, and A hypothesis generation / reduction and reliability calculator for performing hypothesis reduction on the basis of the hypothesis reliability, and 14 is the straight-forward hypothesis generation / reduction and reliability calculator and the transposition hypothesis generation. A target straight / turning fuser that determines whether the management target is a straight-running target or a turning target based on the result of the reduction and reliability calculator, and fuses the results in accordance with the ratio of the determination.

7 and 8, when target observation information is input from any of the predetermined target observation devices, the target observation information is subjected to coordinate transformation into a coordinate system for performing correlation integration, and the target position and the like are converted. Based on Kalman filter theory, based on the target observation information, calculate the smoothed value of the target position / velocity and the initial value of the smoothed error covariance matrix (device 3, processing 17). The target observation information is input (device 3, processing 18). For the already managed target, target prediction information at the next observation time is calculated (apparatus 8, processing 19), and the observation information of the target captured at that time is input (apparatus 1 or apparatus 2, processing 20). In order to determine the correlation between which observation target may correspond to which management target, the correlation determination of which observation target falls within the target predicted existence range centered on the predicted position of the management target and its A correlation group is determined in which the observation target and the management target for which the correlation determination results are duplicated are set as one group (apparatus 4, processing 21), and a predetermined observation target is determined for each of the correlation groups by any management target in the group. A hypothesis of whether there is a high possibility, a new target, or an unnecessary signal is taken into consideration when the management target goes straight and when turning with a predetermined turning acceleration. (Apparatus 5 or apparatus 10, processing 22 or processing 29), and the reliability of the generated hypothesis is calculated based on the correlation strength between the observation target, the management target, the new target, and the unnecessary signal (apparatus 5 or apparatus 10). , Processing 23 or processing 30)
Then, processing 2 is performed according to a predetermined hypothesis reduction method.
3 or hypothesis reduction based on the hypothesis reliability calculated in process 30 (device 5 or device 10, process 24 or process 31)
To predict the future movement path from the past movement path of the management target, calculate the possibility of straight-line movement and the possibility of curve movement, and combine each possibility with the above-mentioned hypothesis reliability to fuse hypotheses (Apparatus 14, processing 35), and in the hypothesis reduced in processing 24 or processing 31, target smoothing information is calculated based on the Kalman filter theory for the set of the observation target and the management target determined in the hypothesis (device 6). , Processing 25)
Then, the observation target and the management target are integrated to register the target information (apparatus 7, processing 26). This series of processing is repeated until the correlation integration ends (apparatus 3, processing 27).

FIG. 16 shows a method of merging the straight-forward hypothesis and the tune-forward hypothesis of the management target in the process 35. In FIG. 16, reference numeral 42 denotes a position of the past N samplings of the management target by a black circle, and reference numeral 43 denotes a predicted position of the management target by a white circle. Assuming an expected course based on the trajectory of the past N samplings of the management target, calculate the ratio of the possibility that the target may go straight or turn, and calculate the ratio of the straight ahead in the case of going straight And the straight forward hypothesis reliability, and in the case of a turn, the integration is performed by integrating the ratio of the turn, the ratio of the turn, and the reliability of the turn hypothesis, and fusing according to the magnitude of the integration result. . In the case of fusion, the postulated hypothesis reliability is a value obtained by adding the above integrated value, and the post-fusion management target position is obtained by internally dividing the predicted position of straight-ahead and turning into the straight-forward hypothesis reliability and the turning-hypothesis reliability. Position.

Embodiment 5 9 and 10 are a configuration diagram and a processing flow diagram, respectively, showing Embodiment 5 of the present invention. In FIG. 9, reference numeral 1 denotes a first target observation device that outputs the position and orientation of a detection result from an unnecessary signal such as a target and clutter as target observation information, and 2 is different from the first target observation device. A target observation information converter 3 for converting the target observation information into a coordinate system for performing correlation integration and performing pre-processing for correlation integration; Is a motion specification correlation determination and correlation group generator that determines whether or not the target observation information is correlated with the prediction information of the management target and generates a correlation group of the observation target and the management target, 5 is a hypothesis generation for generating a combination of correlation between the observation target and the management target having a correlation assuming that the management target goes straight, and the reliability of each hypothesis based on the hypothesis, the target observation information, and the management observation target information. Calculate Hypothesis reliability calculation and the hypothesis reliability performing hypothesis reduced based on straight hypothesis generation and reduction and the reliability calculator more probable hypothesis in the hypothesis, 6
Is a target motion specification smoother for calculating smoothing information such as the position and velocity of the target motion data at the current time from the prediction information of the management target and the target observation information of the observation target. Is a goal integrator that integrates the targets with respect to those for which the observation target and the management target are determined to be coincident with each other. Reference numeral 8 denotes the position / velocity of the target motion data at the next time the target is observed. A target motion specification predictor for calculating prediction information, 10 is a hypothesis generation for generating a combination of correlation between an observation target and a management target having a correlation when it is determined to be a curve, and Hypothesis reliability calculation for calculating the reliability of each hypothesis based on the target observation information and the management observation target information, and a curve hypothesis generation for performing hypothesis reduction based on the hypothesis reliability based on the hypothesis that is more reliable among the hypotheses・ With reduction and reliability calculator Reference numeral 11 denotes a terrain and weather information generator that generates surrounding terrain and weather information observed by the target observation device, and 15 denotes the straight-forward hypothesis generation / reduction and reliability calculator using the terrain and weather information. And a target straight / turn selector that takes into account terrain and weather information for selecting the result of the turn hypothesis generation / reduction and the reliability calculator based on whether the management target is a straight target or a turn target.

In FIGS. 9 and 10, a predetermined target observation device and topographical and weather information of the observation range of the target observation device are prepared (device 11, processing 32), and the information is obtained from one of the predetermined target observation devices. When the target observation information is input, the target observation information is subjected to coordinate transformation into a coordinate system for correlation integration, and based on the target observation information such as the target position, the smoothed value and the smoothed value of the target position / velocity based on the Kalman filter theory. The initial value of the error covariance matrix is calculated (apparatus 3, processing 17). When a target is detected first, target observation information is input as a new target (apparatus 3, processing 18). For the already managed target, target prediction information at the next observation time is calculated (apparatus 8, processing 19), and the observation information of the target captured at that time is input (apparatus 1 or apparatus 2, processing 20). In order to determine the correlation between which observation target may correspond to which management target, the correlation determination of which observation target falls within the target predicted existence range centered on the predicted position of the management target and its A correlation group is determined in which the observation target and the management target for which the correlation determination results are duplicated are set as one group (apparatus 4, processing 21), and a predetermined observation target is determined for each of the correlation groups by any management target in the group. A hypothesis of whether there is a high possibility, a new target, or an unnecessary signal is taken into consideration when the management target goes straight and when turning with a predetermined turning acceleration. (Apparatus 5 or apparatus 10, processing 22 or processing 29), and the reliability of the generated hypothesis is calculated based on the correlation strength between the observation target, the management target, the new target, and the unnecessary signal (apparatus 5 or apparatus 10). , Processing 23 or processing 30)
Then, processing 2 is performed according to a predetermined hypothesis reduction method.
3 or hypothesis reduction based on the hypothesis reliability calculated in process 30 (device 5 or device 10, process 24 or process 31)
Performing the above-mentioned hypothesis reliability on whether the future motion of the management target is likely to be a straight-line motion or the possibility of a curving motion is high from the past motion path of the management target in consideration of the terrain and weather information. Is selected in combination with the hypothesis when the vehicle is assumed to go straight and the hypothesis when the vehicle is assumed to be curved (device 15, processing 36), and is determined in the hypothesis reduced in the processing 24 or 31 in the hypothesis. Based on the set of the observation target and the management target, the target smoothing information is calculated based on the Kalman filter theory (device 6, processing 25), and the observation target and the management target are integrated to register the target information (device 7, processing 26). I do.
This series of processing is repeated until the correlation integration ends (apparatus 3, processing 27).

FIG. 17 shows a method of selecting whether the future movement of the management target in the process 36 is a straight-line movement or a curved road. In FIG. 17, reference numeral 42 denotes the past N of the management target.
The sampling position is indicated by a black circle, the reference numeral 43 is indicated by a white circle as a predicted position of the management target, and the reference numeral 44 is a meteorological obstacle such as terrain and cumulonimbus. Assuming an expected course based on the trajectory of the past N samplings of the management target, calculate the ratio of the possibility that the target may go straight or turn, and calculate the ratio of the straight ahead in the case of going straight And the probability of obstacles and the recurrence hypothesis reliability are integrated, and in the case of a turn, the ratio of the turn and the obstacle probability and the turn hypothesis reliability are integrated, and a larger value of the integration result is selected. Do. As for the obstacle probability, topographical and weather information is regarded as obstacle information, and the degree of the obstacle is set as the obstacle probability. The degree of the obstacle is defined as 0 when a mountain or the like cannot physically pass through, and an obstacle that can pass through such as a cloud with weak atmospheric turbulence is defined as a value close to 1.

Embodiment 6 FIG. FIGS. 11 and 12 are a configuration diagram and a processing flow diagram, respectively, showing Embodiment 6 of the present invention. In FIG. 11, reference numeral 1 denotes a first target observation device that outputs the position and orientation of detection results from unnecessary signals such as a target and clutter as target observation information, and 2 denotes the first target observation device.
Is an n-th target observation device arranged at a position different from that of the target observation device, and 3 is a target observation device that converts target observation information into a coordinate system for performing correlation integration and performs preprocessing for correlation integration. 4 is an information converter, which determines whether or not the target observation information is correlated with the prediction information of the management target, and determines a motion specification correlation to generate a correlation group of the observation target and the management target; A correlation group generator 5 is a hypothesis generation for generating a combination of correlation between the observation target and the management target having a correlation assuming that the management target goes straight, and the hypothesis, the target observation information, and the management observation target information. A hypothesis reliability calculation to calculate the reliability of each hypothesis based on, a straight-forward hypothesis generation / reduction and reliability calculator that performs hypothesis reduction based on the hypothesis reliability based on the hypothesis reliability,
Reference numeral 6 denotes a target motion specification smoother which calculates smoothing information such as the position and velocity of the target motion specification at the current time from the prediction information of the management target and the target observation information of the observation target, and 7 denotes the determined hypothesis. A target integrator that integrates targets for which the observation target and the management target are determined to be coincident with each other. Reference numeral 8 denotes the position / velocity of the motion data of the target at the next time the target is observed. Is a target motion specification predictor that calculates prediction information of a hypothesis, and 10 is a hypothesis generation for generating a combination of correlation between an observation target and a management target having a correlation when it is determined to be a curve, and the hypothesis. Hypothesis reliability calculation to calculate the reliability of each hypothesis based on the target observation information and the management observation target information, and a hypothesis that performs hypothesis reduction based on the hypothesis reliability based on the hypothesis reliability. Generation / reduction and reliability calculator Reference numeral 11 denotes a terrain and weather information generator for generating surrounding terrain and weather information observed by the target observation device, and 16 denotes the straight-forward hypothesis generation / reduction and reliability calculator using the terrain and weather information. The management hypothesis generation / reduction and the results of the reliability calculator are used to judge whether the management target is a straight-ahead target or a turn-around target, and the target straight / This is a fusion device.

In FIGS. 11 and 12, a predetermined target observing device and topographical and weather information of the observation range of the target observing device are prepared (device 11, processing 32), and the information is obtained from one of the predetermined target observing devices. When the target observation information is input, the target observation information is subjected to coordinate transformation into a coordinate system for correlation integration, and based on the target observation information such as the target position, the smoothed value and the smoothed value of the target position / velocity based on the Kalman filter theory. The initial value of the error covariance matrix is calculated (apparatus 3, processing 17). When a target is detected first, target observation information is input as a new target (apparatus 3, processing 18). For the already managed target, target prediction information at the next observation time is calculated (apparatus 8, processing 19), and the observation information of the target captured at that time is input (apparatus 1 or apparatus 2, processing 20). In order to determine the correlation between which observation target may correspond to which management target, the correlation determination of which observation target falls within the target predicted existence range centered on the predicted position of the management target and its A correlation group is determined in which the observation target and the management target for which the correlation determination results are duplicated are set as one group (apparatus 4, processing 21), and a predetermined observation target is determined for each of the correlation groups by any management target in the group. A hypothesis of whether there is a high possibility, a new target, or an unnecessary signal is taken into consideration when the management target goes straight and when turning with a predetermined turning acceleration. (Apparatus 5 or apparatus 10, processing 22 or processing 29), and the reliability of the generated hypothesis is calculated based on the correlation strength between the observation target, the management target, the new target, and the unnecessary signal (apparatus 5 or apparatus 10). , Processing 23 or processing 30)
Then, processing 2 is performed according to a predetermined hypothesis reduction method.
3 or hypothesis reduction based on the hypothesis reliability calculated in process 30 (device 5 or device 10, process 24 or process 31)
To predict the future movement path from the past movement path of the management target, calculate the possibility of straight-line movement and the possibility of curve movement in consideration of the terrain and weather information, and straight-forward and curve Each possibility is combined with each of the above-mentioned hypothesis reliability levels to fuse the hypotheses (apparatus 16, processing 37), and in the hypothesis reduced in processing 24 or processing 31, the set of the observation target and the management target determined in the hypothesis. , The target smoothing information is calculated based on the Kalman filter theory (apparatus 6, processing 25)
Then, the observation target and the management target are integrated to register the target information (apparatus 7, processing 26). This series of processing is repeated until the correlation integration ends (apparatus 3, processing 27).

FIG. 18 shows a method of merging the straight-forward hypothesis and the tune-forward hypothesis of the management target in the process 37. In FIG. 18, reference numeral 42 denotes a position of the past N samplings of the management target in a black circle, reference numeral 43 denotes a predicted position of the management target in a white circle, and reference numeral 44 denotes a weather obstacle such as terrain and cumulonimbus. Assuming an expected course based on the trajectory of the past N samplings of the management target, calculate the ratio of the possibility that the target may go straight or turn, and calculate the ratio of the straight ahead in the case of going straight And the probability of obstacles and the recurrence hypothesis reliability are integrated, and in the case of a turn, the ratio of the turn, the obstacle probability, and the turn hypothesis reliability are integrated, and the two are fused according to the magnitude of the integration result. Done by In the case of fusion, the postulated hypothesis reliability is a value obtained by adding the above integrated value, and the post-fusion management target position is obtained by internally dividing the predicted position of straight-ahead and turning into the straight-forward hypothesis reliability and the turning-hypothesis reliability. Position. As for the obstacle probability, topographical and weather information is regarded as obstacle information, and the degree of the obstacle is set as the obstacle probability. The degree of the obstacle is defined as 0 when a mountain or the like cannot physically pass through, and an obstacle that can pass through such as a cloud with weak atmospheric turbulence is defined as a value close to 1.

[0036]

According to the first aspect of the present invention, the conventional apparatus is provided with a target straight / curvature determination unit and a curved hypothesis generation / reduction and reliability calculator, so that it is possible to support straight ahead and curved ahead. Assuming the movement of the target in advance, it is possible to respond to only one of them, and in some cases it was difficult to track the management target, so that it can respond to both,
It is possible to automatically determine whether the next hypothesis generation is to be used for straight-line or curve-turning based on the target integration specifications of past management targets, thereby improving the tracking accuracy of the target. This makes it possible to limit the number of observation targets to be correlated with the management target, thereby preventing an increase in the number of hypotheses and improving the processing capacity.

According to the second aspect of the present invention, a terrain and weather information generator, a target straight-line / curvature determiner considering the terrain and weather information, and a curve hypothesis generation / reduction and reliability calculator are added to the conventional apparatus. By providing the target, it is assumed that the target movement is assumed in advance as straight-line correspondence and turn-around correspondence, and only one of them can be dealt with, and in some cases it is difficult to track the management target. And make it possible to respond to both, and from the target integrated specifications of the past management goals, more precisely determine whether to use the straight-ahead or curved-ahead method to generate the next hypothesis, taking into account terrain and weather information. In addition, it is possible to make a judgment automatically and improve the tracking accuracy of the target, and it is possible to limit the number of observation targets to be correlated with the management target, thereby preventing an increase in the number of hypotheses and improving the processing capacity thing It can become.

According to the third aspect of the present invention, the conventional apparatus is provided with a curve hypothesis generation / reduction and reliability calculator and a target straight-line / curve judgment unit, so that the conventional apparatus can handle straight-line and curve-up. Assuming the target movement in advance, it was possible to respond to only one of them, and in some cases it was difficult to track the management target. The result of the hypothesis assumed and the result of the hypothesis assumed that the movement of the target is a curve are automatically determined based on the target integrated specifications of the past management goals whether the straight-line or curve-response result is more effective. And the tracking accuracy of the target can be improved.

According to the fourth aspect of the present invention, the conventional apparatus is provided with a curve hypothesis generation / reduction and reliability calculator and a target straight-line / curve fusion unit, so that the conventional apparatus can handle the straight-line and the curve. Assuming the target movement in advance, it was possible to respond to only one of them, and in some cases it was difficult to track the management target. What ratio is effective to use the result of the hypothesis and the result of the hypothesis assuming that the movement of the target is a curvature, based on the target integration parameters of the past management goals, using the results of the straight-line correspondence and the curvature correspondence Is automatically determined, the results of the respective hypotheses can be fused, and the tracking accuracy of the target can be improved.

According to the fifth aspect of the present invention, a terrain and weather information generator, a curve hypothesis generation / reduction and reliability calculator, and a target straight / turn selector in consideration of the terrain and weather information are added to the conventional apparatus. By providing the target, it is assumed that the target movement is assumed in advance as straight-line correspondence and turn-around correspondence, and only one of them can be dealt with, and in some cases it is difficult to track the management target. , And the results of the hypothesis assuming that the target motion is going straight and the result of the hypothesis assuming that the target motion is going straight are either straight-moving or turning-moving from the target integrated specifications of the past management goals. Which of the results is more effective can be determined more accurately and automatically in consideration of the terrain and weather information, and the tracking accuracy of the target can be improved.

According to the sixth aspect of the present invention, a terrain and weather information generator, a curve hypothesis generation / reduction and reliability calculator, and a target straight / curvature fusion unit considering the terrain and weather information are added to the conventional apparatus. By providing the target, it is assumed that the target movement is assumed in advance as straight-line correspondence and turn-around correspondence, and only one of them can be dealt with, and in some cases it is difficult to track the management target. , And the results of the hypothesis assuming that the movement of the target is straight ahead and the result of the hypothesis assuming that the movement of the target is curved are based on the target integration specifications of the past management objectives. It is possible to judge more accurately and automatically in consideration of topographical and weather information on what ratio it is effective to use the results of, and to merge the results of each hypothesis, tracking accuracy of the target Can be improved.

[Brief description of the drawings]

FIG. 1 is a diagram showing a configuration of a first embodiment of a target correlation integrating apparatus according to the present invention.

FIG. 2 is a diagram showing a processing flow of the first embodiment of the target correlation integrating apparatus according to the present invention;

FIG. 3 is a diagram showing a configuration of a target correlation integration apparatus according to a second embodiment of the present invention;

FIG. 4 is a diagram showing a processing flow of a target correlation integrating apparatus according to a second embodiment of the present invention;

FIG. 5 is a diagram showing a configuration of a target correlation integration apparatus according to a third embodiment of the present invention.

FIG. 6 is a diagram showing a processing flow of a third embodiment of the target correlation integrating apparatus according to the present invention;

FIG. 7 is a diagram showing a configuration of a fourth embodiment of the target correlation integrating apparatus according to the present invention.

FIG. 8 is a diagram showing a processing flow of a fourth embodiment of the target correlation integrating apparatus according to the present invention.

FIG. 9 is a diagram showing a configuration of a fifth embodiment of the target correlation integration apparatus according to the present invention.

FIG. 10 is a diagram showing a processing flow of a fifth embodiment of the target correlation integrating apparatus according to the present invention.

FIG. 11 is a diagram showing a configuration of a sixth embodiment of the target correlation integrating apparatus according to the present invention.

FIG. 12 is a diagram showing a processing flow of a sixth embodiment of the target correlation integrating apparatus according to the present invention.

FIG. 13 is a diagram illustrating an example of determination of straight traveling and turning of a management target.

FIG. 14 is a diagram illustrating an example of determination of straight ahead and turning of a management target using terrain and weather information.

FIG. 15 is a diagram showing an example of selection of straight ahead and turning of a management target.

FIG. 16 is a diagram showing an example of fusion of a straight-forward hypothesis and a tune-forward hypothesis of a management target.

FIG. 17 is a diagram illustrating an example of selection of straight ahead and turning of a management target using topographical and weather information.

FIG. 18 is a diagram illustrating an example of a fusion of a straight-forward hypothesis and a curved-movement hypothesis of a management target using terrain and weather information.

FIG. 19 is a diagram illustrating the background of the present invention.

FIG. 20 is a diagram showing a configuration of a conventional target correlation integration device.

FIG. 21 is a diagram showing a processing flow of a conventional target correlation integration device.

FIG. 22 is a diagram illustrating a method of determining a correlation between an observation target and a management target.

[Explanation of symbols]

1 first target observation device, 2 nth target observation device, 3
Target observation information converter, 4 motion specification correlation judgment and correlation group generator, 5 linear hypothesis generation / reduction and reliability calculator, 6 target motion specification smoother, 7 target integrator, 8
Target motion specification predictor, 9 Target straight / curvature judgment device, 10
Curve hypothesis generation / reduction and reliability calculator, 11 terrain and weather information generator, 12 target straight / curvature determiner considering terrain and weather information, 13 target straight / curvature selector,
14 target straight / curve fusion device, 15 target straight / curvature selector in consideration of terrain and weather information, 16 target straight / curvature fusion device in consideration of terrain and weather information, 17 initial setting of target smoothing information, 18 Input target observation information for new target, 1
9 Target prediction information calculation, 20 Target observation information input, 21
Determination of correlation group between observation target and management target, 22 generation of straight-forward hypothesis, calculation of reliability of straight-forward hypothesis, reduction of 24 straight-forward hypothesis, calculation of target smoothing information, determination of integration of observation target and management target, determination of end of correlation integration, 28 Target straight / turning judgment, 29 Turn hypothesis generation, 30 Turn hypothesis reliability calculation, 31 Turn hypothesis reduction, 32 Topographic and weather information input, 33 Target straight / trajectory using topography and weather information
Turn determination, 34 target straight / turn selection, 35 target straight / turn fusion, 36 target straight / turn selection using terrain and weather information, 37 target straight / turn fusion using terrain and weather information, 38 Targets observed by each observation device, 39 Targets observed by the first target observation device, 40
Target observed by the nth target observation device, 41 target correlation integration device, 42 positions of past N samplings of management target,
43 Predicted position of management target, 44 Obstacle on topography and weather, 45 Smooth position of management target i, 46 Predicted position of management target i, 47 Observation position of observation target i, 48 Target predicted existence range.

Claims (6)

[Claims] 1. A plurality of target observation devices that output target observation information from a target and position information of the observation device, a target observation information converter that converts the observation information from the target observation device into a correlated integrated coordinate system, A target motion data predictor that calculates prediction data at the current observation time from the target integrated data of the management target recognized as the target by the target integrator, and the management target prediction information from the target motion data predictor and the target motion data. A motion specification correlation determination for calculating a target existence prediction range from the target observation information from the target observation information converter, performing the correlation determination, and generating correlation group information describing the correlation content based on the correlation determination result. And a correlation group generator, and integrates the observation targets divided for each correlation group by the motion specification correlation determination and the correlation group information from the correlation group generator into target integration specifications of the management objectives so far. In consideration of the above, a target straight / turning determiner for determining whether the target should be taken as a straight ahead target or a turning target, and the target observed by the target observation device by the target straight / turning determiner goes straight. Generation of a hypothesis as to which management target the observation target can become when the target is determined, and the reliability of the hypothesis based on the previous hypothesis information, the motion specification correlation determination and correlation from the correlation group generator. Calculate the reliability of each hypothesis considering the group information,
A straight-forward hypothesis generation / reduction and reliability calculator for reducing the hypothesis in consideration of the reliability of the hypothesis according to a preset hypothesis reduction condition, and observation by the target observation device by the target straight / curvature determination unit A curved hypothesis generation / reduction / reliability calculator that performs the same processing as the straight-forward hypothesis generation / reduction / reliability calculator on the curvature target when the target is determined to be a curvature target; A target motion specification smoother that calculates a smoothing parameter by calculating a smoothing gain matrix from the management target prediction information and the target observation information, the straight-forward hypothesis generation / reduction and reliability calculator, and the curvature hypothesis generation / reduction and The combination of the management target and the observation target is determined based on the content of the hypothesis and the reliability by the reliability calculator, and the smoothed motion calculated by the target motion specification smoother is determined for the determined combination of the management target and the observation target. Former For the target integrated specifications, the observation target information for the observation target having no combination partner is used as the target integration specification, and for the management target having no combination partner, the prediction calculated by the target motion specification predictor. A target correlation integration device comprising: a target integrator that generates a management target using the specifications as target integration specifications. 2. A plurality of target observation devices that output target observation information from a target and position information of the observation device, a target observation information converter that converts the observation information from the target observation device into a correlated integrated coordinate system, A target motion data predictor that calculates prediction data at the current observation time from the target integrated data of the management target recognized as the target by the target integrator, and the management target prediction information from the target motion data predictor and the target motion data. A motion specification correlation determination for calculating a target existence prediction range from the target observation information from the target observation information converter, performing the correlation determination, and generating correlation group information describing the correlation content based on the correlation determination result. And a correlation group generator, a terrain and weather information generator for generating predetermined terrain and weather information, and a correlation group based on the motion group correlation determination and correlation group information from the correlation group generator. The observation targets divided for each map are the target integration specifications of the management targets up to now, the management target prediction information from the target motion specification predictor and the terrain of the management target prediction position from the terrain and weather information generator, and A target straight / curvature determination device that considers terrain and weather information that determines whether it should be considered as a straight ahead target or a turning target in consideration of weather information, and a target straight / turning determination device that considers the terrain and weather information. Generation of a hypothesis as to which management target the observation target can be when the target observed by the target observing device is determined to be a straight-ahead target by the curve judgment device, and the reliability of the hypothesis to the previous hypothesis information. Based on the motion specification correlation determination and the correlation group information from the correlation group generator, the reliability of each hypothesis is calculated, and the reliability of the hypothesis is considered based on a preset hypothesis reduction condition. A straight-forward hypothesis generation / reduction and reliability calculator for reducing the hypothesis; and a target for observing the target observed by the target observing device by the target straight-ahead / curvature determiner. A straightforward hypothesis generation / reduction and reliability calculator that performs the same processing as the straight-forward hypothesis generation / reduction and reliability calculator, and calculates a smoothing gain matrix from the management target prediction information and the target observation information to perform smoothing The target motion specification smoother for calculating the element, and the content of the hypothesis and reliability of the straight-forward hypothesis generation / reduction and reliability calculator and the hypothesis generation / reduction / reliability calculator described above and the management target and observation target. The combination is determined, and for the combination of the determined management target and the observation target, the smoothed parameters calculated by the target motion specification smoother are used as the target integrated parameters. Is a target integrator that uses the observation target information as a target integration parameter, and generates a management target using the prediction parameters calculated by the target motion specification predictor as a target integration parameter for a management target having no combination partner. And a target correlation integrating device. 3. A plurality of target observation devices that output target observation information from a target and position information of the observation device, a target observation information converter that converts the observation information from the target observation device into a correlated integrated coordinate system, A target motion parameter estimator that calculates prediction parameters at the current observation time from the previous target integration parameter of the target integration parameter of the management target recognized as the target by the target integrator, and a target motion parameter predictor from the target motion parameter predictor. A target existence prediction range is calculated from the management target prediction information and the target observation information from the target observation information converter, the correlation is determined, and the generation of correlation group information describing the correlation content based on the correlation determination result is performed. Which management target is the movement target correlation determination and correlation group generator to perform, and the observation target divided for each correlation group based on the correlation group information from the movement specification correlation determination and correlation group generator. It is assumed that the management goal is a straight-forward goal, and hypothesis information is generated in consideration of the motion specification correlation determination and correlation group information from the correlation group generator, and the exercise is performed based on the hypothesis information. The reliability of each hypothesis is calculated in consideration of the specification correlation determination and the correlation group information from the correlation group generator, and the hypothesis is reduced in consideration of the hypothesis information and the hypothesis reliability according to a preset hypothesis reduction condition. The straight-forward hypothesis generation / reduction and reliability calculator, and the hypothesis about which management target can be the observation target divided for each correlation group by the motion specification correlation determination and the correlation group information from the correlation group generator. It is assumed that the management goal is a goal to be curved, and the same as the straight-forward hypothesis generation / reduction / reliability calculator described above. A calculation unit and the target integration specifications of the management objectives so far and the rectilinear hypothesis generation / reduction / reliability calculator, and the observation target based on the reliability from the curved hypothesis generation / reduction / reliability calculator. A target straight / curvature determiner that determines which management target corresponds to, a target motion specification smoother that calculates a smooth gain matrix from the management target prediction information and the target observation information and calculates a smooth specification. In response to the determination result by the target straight / turnover determiner, the combination of the management target and the observation target is determined based on the content and reliability of the corresponding hypothesis, and the determined combination of the management target and the observation target is determined. The smoothed parameters calculated by the target motion specification smoother are used as target integrated specifications. For an observation target having no combination partner, the observation target information is used as the target integrated specification, and no combination partner exists. Target correlation integrating device being characterized in that a target integrator for generating management target prediction specifications calculated by the desired motion specifications predictor as the target integrated specifications for management objectives. 4. A plurality of target observation devices that output target observation information from a target and position information of the observation device, a target observation information converter that converts the observation information from the target observation device into a correlated integrated coordinate system, A target motion parameter estimator that calculates prediction parameters at the current observation time from the previous target integration parameter of the target integration parameter of the management target recognized as the target by the target integrator, and a target motion parameter predictor from the target motion parameter predictor. A target existence prediction range is calculated from the management target prediction information and the target observation information from the target observation information converter, the correlation is determined, and the generation of correlation group information describing the correlation content based on the correlation determination result is performed. Which management target is the movement target correlation determination and correlation group generator to perform, and the observation target divided for each correlation group based on the correlation group information from the movement specification correlation determination and correlation group generator. It is assumed that the management goal is a straight-forward goal, and hypothesis information is generated in consideration of the motion specification correlation determination and correlation group information from the correlation group generator, and the exercise is performed based on the hypothesis information. The reliability of each hypothesis is calculated in consideration of the specification correlation determination and the correlation group information from the correlation group generator, and the hypothesis is reduced in consideration of the hypothesis information and the hypothesis reliability according to a preset hypothesis reduction condition. The straight-forward hypothesis generation / reduction and reliability calculator, and the hypothesis about which management target can be the observation target divided for each correlation group by the motion specification correlation determination and the correlation group information from the correlation group generator. It is assumed that the management goal is a goal to be curved, and the same as the straight-forward hypothesis generation / reduction / reliability calculator described above. A calculation unit and the target integration specifications of the management objectives so far and the rectilinear hypothesis generation / reduction / reliability calculator, and the observation target based on the reliability from the curved hypothesis generation / reduction / reliability calculator. Going straight ahead / weighting which management goal corresponds to
A fusion unit, a target motion specification smoother for calculating a smoothing parameter by calculating a smoothing gain matrix from the management target prediction information and the target observation information, and a weighting result obtained by the target straight / advance fusion unit. The combination of the management target and the observation target is determined based on the content and reliability of the corresponding hypothesis, and for the determined combination of the management target and the observation target, the smoothing parameters calculated by the target motion specification smoother are used. For the target integrated specifications, the observation target information for the observation target having no combination partner is used as the target integration specification, and for the management target having no combination partner, the prediction calculated by the target motion specification predictor. A target correlation integration device comprising: a target integrator that generates a management target using the specifications as target integration specifications. 5. A plurality of target observation devices that output target observation information from a target and position information of the observation device, a target observation information converter that converts the observation information from the target observation device into a correlated integrated coordinate system, A target motion parameter estimator that calculates prediction parameters at the current observation time from the previous target integration parameter of the target integration parameter of the management target recognized as the target by the target integrator, and a target motion parameter predictor from the target motion parameter predictor. A target existence prediction range is calculated from the management target prediction information and the target observation information from the target observation information converter, the correlation is determined, and the generation of correlation group information describing the correlation content based on the correlation determination result is performed. Which management target is the movement target correlation determination and correlation group generator to perform, and the observation target divided for each correlation group based on the correlation group information from the movement specification correlation determination and correlation group generator. It is assumed that the management goal is a straight-forward goal, and hypothesis information is generated in consideration of the motion specification correlation determination and correlation group information from the correlation group generator, and the exercise is performed based on the hypothesis information. The reliability of each hypothesis is calculated in consideration of the specification correlation determination and the correlation group information from the correlation group generator, and the hypothesis is reduced in consideration of the hypothesis information and the hypothesis reliability according to a preset hypothesis reduction condition. The straight-forward hypothesis generation / reduction and reliability calculator, and the hypothesis about which management target can be the observation target divided for each correlation group by the motion specification correlation determination and the correlation group information from the correlation group generator. It is assumed that the management goal is a goal to be curved, and the same as the straight-forward hypothesis generation / reduction / reliability calculator described above. A calculator, a terrain and weather information generator for generating predetermined terrain and weather information, target integrated specifications of the management targets up to now, the straight-forward hypothesis generation / reduction, and a reliability calculator and the curvature hypothesis generation / Based on the hypothesis reliability from the reduction and reliability calculator and the terrain and weather information from the terrain and weather information generator, the terrain and weather information for determining which management target the observation target corresponds to was considered. A target straight / curvature determiner; a target motion specification smoother for calculating a smooth gain parameter by calculating a smoothing gain matrix from the management target prediction information and the target observation information; and a determination by the target straight / curvature determiner Based on the result, the combination of the management target and the observation target is determined based on the content and reliability of the corresponding hypothesis, and the target motion specification smoother calculates the determined combination of the management target and the observation target. The obtained smoothed specifications are used as target integrated specifications. For an observation target having no combination partner, the observation target information is used as target integration specifications. For a management target having no combination partner, the target motion specification prediction is performed. And a target integrator that generates a management target using the prediction data calculated by the calculator as target integrated data. 6. A plurality of target observation devices that output target observation information from a target and position information of the observation device, a target observation information converter that converts the observation information from the target observation device into a correlated integrated coordinate system, A target motion parameter estimator that calculates prediction parameters at the current observation time from the previous target integration parameter of the target integration parameter of the management target recognized as the target by the target integrator, and a target motion parameter predictor from the target motion parameter predictor. A target existence prediction range is calculated from the management target prediction information and the target observation information from the target observation information converter, the correlation is determined, and the generation of correlation group information describing the correlation content based on the correlation determination result is performed. Which management target is the movement target correlation determination and correlation group generator to perform, and the observation target divided for each correlation group based on the correlation group information from the movement specification correlation determination and correlation group generator. It is assumed that the management goal is a straight-forward goal, and hypothesis information is generated in consideration of the motion specification correlation determination and correlation group information from the correlation group generator, and the exercise is performed based on the hypothesis information. The reliability of each hypothesis is calculated in consideration of the specification correlation determination and the correlation group information from the correlation group generator, and the hypothesis is reduced in consideration of the hypothesis information and the hypothesis reliability according to a preset hypothesis reduction condition. The straight-forward hypothesis generation / reduction and reliability calculator, and the hypothesis about which management target can be the observation target divided for each correlation group by the motion specification correlation determination and the correlation group information from the correlation group generator. It is assumed that the management goal is a goal to be curved, and the same as the straight-forward hypothesis generation / reduction / reliability calculator described above. A calculator, a terrain and weather information generator for generating predetermined terrain and weather information, target integrated specifications of the management targets up to now, the straight-forward hypothesis generation / reduction, and a reliability calculator and the curvature hypothesis generation / A target straight / curve fusion unit that weights which management target the observation target corresponds to based on the reliability from the reduction and reliability calculator and the terrain and weather information from the terrain and weather information generator; A target motion specification smoother for calculating a smoothing parameter by calculating a smoothing gain matrix from the management target prediction information and the target observation information, and a corresponding hypothesis based on a weighting result obtained by the target straight / curvature fusion unit. The combination of the management target and the observation target is determined based on the content and the reliability, and for the determined combination of the management target and the observation target, the smoothing parameters calculated by the target motion specification smoother are subjected to the target integration. For the observation target where there is no combination partner, the observation target information is used as the target integrated specification. For the management target where there is no combination partner, the prediction specification calculated by the target motion specification predictor. And a goal integrator that generates a management goal with the goal integration specifications as a target.