JP2001296354A - Apparatus for managing resource - Google Patents

Abstract

PROBLEM TO BE SOLVED: To solve a problem such that an operating condition of sensor (1-m)and tracking filters (1-n) cannot be adjusted to conform to targets (1-n) and a desired tracking performance cannot be obtained when allocation of the sensors (1-m) to the targets (1-n) is sequentially determined. SOLUTION: A state of the present tracking for each target is evaluated from information related to an operating state of a sensor group 12 and a tracking filter group 14, etc. Based on the evaluation result, the operating condition of the sensor group 12 and the tracking filter group 14 is adjusted.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a multi-target tracking system using a plurality of sensors for efficiently operating a group of sensors and a group of tracking filters included therein to save resources and obtain high tracking performance. Related to a resource management device.

[0002]

2. Description of the Related Art FIG. 9 is a block diagram showing a conventional resource management apparatus disclosed in, for example, Japanese Patent Application Laid-Open No. 9-257923. In the figure, reference numeral 1 denotes a target group composed of n targets. For example, the aircraft is the target (1) to the target (n). Reference numeral 2 denotes a sensor group including m sensors (for example, radar devices) that observe targets (1) to (n);
When receiving observation information from the sensors (1) to (m) constituting the sensor group 2, the observation information is arranged for each target, and the observation information is matched with the corresponding tracking filter (1) to tracking filter ( When the observation information fusion device 4 receives the observation information from the observation information fusion device 3, the tracking filter composed of n tracking filters for executing the tracking calculation of the targets (1) to (n) is output. Group.

When an observation information is input from the observation information fusion unit 3 and tracking information is input from the tracking filter group 4, an observation necessity evaluator 5 judges the necessity of observing the targets (1) to (n). , 6 receive the observation information from the observation information fusion device 3 and the tracking information from the tracking filter group 4 to obtain the position relationship between the target (1) to the target (n) and the sensor (1) to the sensor (m). , An observation effect judging unit for judging the effectiveness of the sensors (1) to (m) with respect to the observation of the targets (1) to (n); and 7, the sensor (1) to the targets (1) to (n) A rule storage unit 8 for storing an allocation rule of the sensor (m) executes an allocation process in accordance with the allocation rule, and makes a determination result of the observation necessity evaluator 5 and an observation effect determiner 6
Is an allocator that determines the assignment of the sensors (1) to (m) to the targets (1) to (n) based on the determination result and the tracking information of the tracking filter group 4.

Next, the operation will be described. The tracking filters (1) to (n) constituting the tracking filter group 4 correspond to the targets (1) to (n) to be observed and exist in the same number as the targets (1) to (n). I do.

The tracking filters (1) to (n) constituting the tracking filter group 4 correspond to the corresponding targets (1) to
When the observation information of the target (n) is received from the sensors (1) to (m), the position and speed of the targets (1) to (n) are estimated and predicted, and the targets (1) to (n) are executed. ) Is updated.

The observation necessity evaluator 5 includes an observation information fusion device 3
When the observation information is input from the tracking filter group 4 and the tracking information is input, the necessity of observation of the targets (1) to (n) is determined. That is, based on all or a part of the expected value of the tracking error of the target, the position of the target, the distance to the target, the traveling direction of the target, the target speed, the movement of the target, the importance of the target itself, and the like, ) -Evaluation of the necessity of observation of the target (n) is executed, and an evaluation value for each target is calculated and output.

The observation effect determiner 6 receives the observation information from the observation information fusion unit 3 and the tracking information from the tracking filter group 4, and receives the targets (1) to (n) and the sensors (1) to (4).
The validity of the sensors (1) to (m) with respect to the observation of the targets (1) to (n) is determined from the positional relationship of the sensors (m) and the like. That is, the distance between the target (1) to the target (n), the performance of the sensor (1) to the sensor (m), the difference in the estimated error variance before and after the observation, and the like, and the sensor An evaluation value is determined for each combination of (1) to (m).

[0008] The allocator 8 receives the evaluation value output from the observation necessity evaluator 5, the evaluation value output from the observation effect determiner 6, and the tracking information output from the tracking filter group 4. Therefore, by executing the assignment process,
The assignment of the sensors (1) to (m) to the targets (1) to (n) is determined. The allocation information of the allocator 8 is input to the sensor group 2, and new observation information is obtained.

[0009]

Since the conventional resource management device is configured as described above, the assignment of the sensors (1) to (m) to the targets (1) to (n) is determined sequentially. However, the operating conditions of the sensors (1) to (m) and the tracking filters (1) to (n) cannot be adjusted according to the target, and it is difficult to obtain a desired tracking performance. There were challenges. In addition, since resources are not managed for the purpose of energy saving and sensors are assigned to targets that do not need to be observed, the amount of calculation is reduced if the waste of energy occurs or the number of targets or sensors is large. There was a problem that exceeded the ability of.

[0010] The present invention has been made to solve the above-described problems, and has as its object to obtain a resource management device capable of efficiently operating a sensor group and a tracking filter group to obtain high tracking performance. And

[0011]

A resource management device according to the present invention evaluates a current tracking state of each target from information on an operation state of a sensor group, and determines an operation condition of the sensor group based on the evaluation result. It is intended to be adjusted.

The resource management device according to the present invention evaluates the current tracking state of each target from the operation state of the tracking filter group constituting the tracking means, and adjusts the operation conditions of the tracking filter group based on the evaluation result. It is like that.

The resource management apparatus according to the present invention evaluates the current tracking state of each target from information on the operation states of the sensor group and the tracking filter group, and operates the sensor group and the tracking filter group based on the evaluation result. The conditions are adjusted.

A resource management device according to the present invention extracts information relating to a surrounding situation surrounding each target, and evaluates a current tracking state of each target in consideration of the information.

The resource management apparatus according to the present invention extracts information relating to the estimated value of the motion data of each target, and evaluates the current tracking state of each target in consideration of the extracted information.

A resource management device according to the present invention evaluates the degree of difficulty in maintaining tracking of each target as the current tracking state of each target and evaluates the accuracy of estimating the motion data of each target. It is.

The resource management apparatus according to the present invention evaluates the degree of difficulty in keeping track of each target in consideration of the proximity of the targets, the approach to the clutter area, or the number of consecutive missing detections of each target. It was done.

According to the resource management apparatus of the present invention, the speed of each target, the mobility of each target, the estimation error of the motion data of each target,
The estimation accuracy of the motion data of each target is evaluated in consideration of the degree of adaptation to the motion model or the distance of each sensor constituting the sensor group to each target.

The resource management device according to the present invention adjusts, as operating conditions of the sensor group, the assignment of each sensor constituting the sensor group to each target, the observation time of each sensor, or the operating parameter of each sensor, and a tracking filter. As the operating condition of the group, the type of each filter constituting the tracking filter group or the operating parameter of each filter is adjusted.

The resource management device according to the present invention adjusts the operating conditions of the sensor group and the tracking filter group in consideration of the load of the sensor group and the tracking filter group.

The resource management apparatus according to the present invention predicts the future tracking state of each target and evaluates the future tracking state of each target.

The resource management device according to the present invention is designed to evaluate the necessity of tracking each target.

The resource management device according to the present invention provides each resource in consideration of the traveling direction of each target, the distance to each target, the speed of each target, the estimated arrival time of each target in a specific area, or the identification result of each target. This is to evaluate the necessity of tracking the target.

In the resource management device according to the present invention, when adjusting the operating conditions of the sensor group and the tracking filter group, the parameters of the sensor group and the tracking filter group are set within an allowable range prepared in advance. is there.

[0025]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS One embodiment of the present invention will be described below. Embodiment 1 FIG. FIG. 1 is a configuration diagram showing a resource management device according to a first embodiment of the present invention.
This is a target group composed of a plurality of targets. For example, an aircraft or a flying object other than an aircraft is the target (1) to the target (n). Aircraft also include relatively high-speed jets, relatively low-speed helicopters, and intermediate propellers depending on the speed. In addition, large aircraft such as jumbo jet aircraft and small aircraft such as Cessna aircraft are included depending on the size.

Reference numeral 12 denotes a sensor group composed of m sensors for observing the targets (1) to (n), and a radar device or the like corresponds to the sensor group. A radar device emits a radio wave and receives a reflected wave to detect a target such as an aircraft or a ship. In radar observations, reflected waves from other than the target, such as the ground surface, sea surface,
There are reflected waves from drift ice and rain clouds. Such reflected waves from unnecessary objects are called clutter. In addition, a movement sensor such as an in-vehicle radar and an on-board radar is also applicable. 13 receives observation information from the sensors (1) to (m) constituting the sensor group 12, arranges the observation information for each target, and converts the observation information into a corresponding tracking filter (1) to tracking filter. Upon receiving the observation information from the observation information fusion device 13, the observation information fusion device 14 outputs to (n) a tracking composed of n tracking filters that execute the tracking calculation of the targets (1) to (n). Filter group (tracking means).

Reference numeral 15 denotes a sensor state extractor for extracting information relating to the operating states of the sensors (1) to (m) constituting the sensor group 12. Reference numeral 16 denotes information relating to the surrounding situation surrounding each target (for example, terrain, An observation information extractor for extracting clutter observation information); a tracking information extractor for extracting information relating to estimated values of motion parameters (for example, position and velocity) of targets (1) to (n); Tracking filter group 14
, A filter state extractor that extracts information related to the operation states of the tracking filters (1) to (n),
Reference numeral 9 denotes a target state evaluation unit that evaluates the current tracking states of the targets (1) to (n) from the extraction results of the sensor state extractor 15, the observation information extractor 16, the tracking information extractor 17, and the filter state extractor 18. It is. Note that the sensor state extractor 1
5, the observation information extractor 16, the tracking information extractor 17, the filter state extractor 18, and the target state evaluation unit 19 constitute a tracking state evaluation unit.

Reference numeral 20 denotes a tracking database that stores knowledge on the application method of various tracking algorithms according to the target tracking state. Reference numeral 21 denotes a tracking database based on the evaluation value output by the target state evaluation unit 19 while referring to the tracking database 20. , A resource allocation method creating unit that creates an allocation method for the sensor group 12 and the tracking filter group 14 for the targets (1) to (n) and evaluates the degree of improvement in tracking performance of the allocation method.

Reference numeral 22 denotes a resource for storing knowledge on the functions and performances of the sensors (1) to (m) constituting the sensor group 12 and the ability of the computer to operate the tracking filters (1) to (n). Reference numeral 23 denotes a resource management calculation unit for calculating an optimal resource allocation method with reference to the resource database 22. Reference numeral 24 denotes a resource management calculation unit 2.
3, a filter group indicator that outputs the operating conditions of the tracking filters (1) to (n) constituting the tracking filter group 14 in accordance with the distribution method calculated in Step 3; This is a sensor group indicator that outputs operating conditions of the sensors (1) to (m) constituting the sensor group 12 according to the method. It should be noted that the tracking database 20, the resource allocation method creating unit 21,
The resource database 22, the resource management calculation unit 23, the filter group indicator 24, and the sensor group indicator 25 constitute an adjusting unit.

FIG. 2 is a block diagram showing the target state evaluator 19. In the figure, reference numeral 31 denotes a tracking maintenance possibility evaluator for evaluating the current tracking maintenance possibility (degree of difficulty) of each target, and 32 represents each target. Tracking accuracy evaluator for evaluating the current tracking accuracy (estimation accuracy of motion parameters), and 33 is a tracking maintenance possibility evaluator 31
Is a target state evaluator that evaluates the tracking performance for each target by fusing the evaluation result of the tracking accuracy evaluator 32 with the evaluation result of the target.

FIG. 3 is a block diagram showing the resource allocation method creating section 21. In the figure, reference numeral 41 denotes a resource allocation for each target based on the tracking state of each target which is an evaluation value output by the target state evaluating section 19. This is a resource allocation device that calculates an allocation method. Here, the resources are the sensors (1) to (m) forming the sensor group 12 and the tracking filters (1) to (n) forming the tracking filter group 14, and the distribution method is as follows. These are sensors (1) to sensor (m) assigned to each target, observation time, sensor operation parameters, types of tracking filters (1) to tracking filters (n), and operation parameters. Reference numeral 42 denotes an allocation effect evaluator for evaluating the effect (improvement degree) of the allocation method output from the resource allocation unit 41 on the tracking performance.

FIG. 4 is a diagram showing the configuration of the resource management calculation unit 23. In the figure, reference numeral 51 denotes a resource allocation method created by the resource allocation method creation unit 21 and tracking when the operation state of the tracking filter group 14 is received. A tracking function monitor 52 monitors whether or not the distribution related to the filter group 14 can be executed in the current tracking filter group 14. Reference numeral 52 denotes a resource distribution method created by the resource distribution method creation unit 21 and an operation state of the sensor group 12. Upon receiving the information, the observation function monitor 53 monitors whether or not the distribution related to the sensor group 12 is executable in the current sensor group 12. The monitoring function monitor 53 transmits the resource distribution method and the evaluation value of the distribution effect from the resource distribution method creation unit 21. Then, the load amount applied to the tracking filter group 14 by each resource allocation method output from the tracking function monitor 51 and each resource allocation method output from the observation function monitor 52 are sensed. And consideration of the load applied to the group 12, the optimal allocation calculator to determine the optimal resource allocation method of the tracking filter group 14 and the sensors 12.

Next, the operation will be described. Sensor group 12
Observes targets (1) to (n) according to the operating conditions output by the sensor group indicator 25, and outputs the observation information. Here, the operating conditions output by the sensor group indicator 25 include which sensor (1) to sensor (m), which target (1) to target (n), which operation parameter,
It is when to observe.

When the observation information fusion device 13 receives the observation information from the sensors (1) to (m) constituting the sensor group 12, the observation information fusion device 13 refers to the operating conditions output from the sensor group indicator 25 and refers to the observation information. Are arranged for each target, and the observation information of each target is output to the tracking filters (1) to (n) having a corresponding relationship with each target. When a plurality of sensors are simultaneously assigned to one target, an average value of the observation information output by the plurality of sensors or another calculated value is obtained and output to one tracking filter. . As described above, the observation information fusion device 13 includes a plurality of sensors (1) to (m) and a plurality of tracking filters (1) to
It functions to connect the tracking filter (n).

The tracking filters (1) to (n) constituting the tracking filter group 14 are the filter group indicator 2
The type of the filter is changed according to the operating conditions output by 4 and the operating parameters of the filter are set. Then, the tracking filters (1) to (n)
Performs estimation and prediction of the positions and velocities of the targets (1) to (n) by executing the tracking calculation of the corresponding targets (1) to (n), and calculates the targets (1) to ( n)
Update tracking information for.

The sensor state extractor 15 is provided with a sensor (1) to a sensor (1) to make it possible to understand the arrangement of the sensors (1) to (m) constituting the sensor group 12 and the overlapping state of the observation range.
The information on the operation state of the sensor (m) is extracted, and the information is output to the target state evaluation unit 19. Observation information extractor 16
In order to make it possible to grasp the surrounding conditions surrounding each target (for example, clutter distribution and topographical conditions), information on the surrounding conditions surrounding each target is obtained from all observation information output by the observation information fusion device 13. And outputs the information to the target state evaluation unit 19.

The tracking information extractor 17 includes a tracking filter group 1
From the tracking information of the targets (1) to (n) output by
It extracts information (including information about an estimation error) on the estimated values of the motion parameters (for example, position and speed) of the targets (1) to (n), and outputs the information to the target state evaluation unit 19. The filter state extractor 18 extracts information related to the operation states of the tracking filters (1) to (n) included in the tracking filter group 14, and outputs the information to the target state evaluation unit 19. In addition, as the information related to the operation state,
Parameter settings for adjusting the gain of the tracking filter,
There is a model fitting degree in a tracking filter of a multiple motion model.

Upon receiving various types of information from the sensor state extractor 15, the observation information extractor 16, the tracking information extractor 17, and the filter state extractor 18, the target state evaluator 19 obtains targets (1) to (5) from the various kinds of information. The tracking state of the target (n) is evaluated at present, and the evaluation value is output. That is, the tracking maintenance possibility evaluator 31 of the target state evaluation unit 19
Sets a large evaluation value for a target in which tracking results between targets are likely to be switched or clutter is likely to occur. For example, the following items are evaluated.

1. Proximity between Goals The estimated positions of Goals (1) to Goals (n) are input, and the evaluation values of the targets that are predicted to be likely to be replaced when the goals are close to each other are increased. 2. Passing to clutter region The estimated positions of the targets (1) to (n) and the clutter region are input, and the evaluation value is increased when it is determined that the target is passing through the clutter region.

3. Number of consecutive missed detections Even if an attempt is made to observe a target, the sensor cannot capture the target, and the observation information cannot be obtained. This is referred to as missed detection. If continuous missed detection occurs, the target may be lost. Therefore, the number of consecutive times of loss detection is counted for each target, and the evaluation value is increased according to the count value. It should be noted that the tracking maintenance possibility evaluator 31 weights and averages the above evaluation values to obtain a tracking maintenance possibility evaluation value.

Tracking accuracy evaluator 32 of target state evaluation section 19
Sets a large evaluation value for a target whose tracking accuracy tends to deteriorate. For example, the following items are evaluated. 1. Target speed If the magnitude of the target speed is determined to be larger than the estimated speed values of the targets (1) to (n), the evaluation value is increased. 2. Target Mobility By inputting the residual (difference between the predicted position and the observation position) between the targets (1) to (n), it is determined that the target has high mobility due to repeated turning motions and rising and falling. In such cases, the evaluation value is increased.

3. Estimation Error of Target Motion Specifications When the estimation error is determined to be large based on the estimation errors output from the tracking filters (1) to (n) that constitute the tracking filter group 14, the evaluation value is increased. 4. Fitness to Motion Model Referring to the fitness of the motion model extracted by the filter state extractor 18 from the tracking filter of the multiple motion model,
When it is determined that the fitness of the plurality of exercise models is high and the target exercise is ambiguous, the evaluation value is increased. 5. Distance between target and sensor When the estimated position of target (1) to target (n) and the arrangement of sensor (1) to sensor (m) are input and it is determined that the target is located far away from the sensor. To increase the evaluation value. The tracking accuracy evaluator 32 weights and averages the above evaluation values to obtain a tracking accuracy evaluation value.

In a normal Kalman filter type tracking filter using one determined motion model, a tracking maintaining performance and a tracking accuracy are simultaneously secured for a target having high mobility such as a linear motion at a constant speed or a turning motion. Is difficult to do. Therefore, the tracking filter of the multiple motion model aims to improve the tracking performance of a target with high mobility by setting a plurality of motion models in parallel. In this method, a tracking result obtained by a Kalman filter based on each motion model is integrated by weighting with the fitness of each motion model. The target state evaluator 33 of the target state evaluator 19 calculates a target state evaluation value for the targets (1) to (n) by taking a weighted average of the tracking maintainability evaluator 31 and the tracking accuracy evaluator 32. .

The target state evaluation value has a small value when the tracking performance for the target is high, and has a large value when the tracking performance is low. Note that the target state evaluation unit 19 may output the evaluation value as the calculation result as it is. In this case, if a parameter setting method based on each evaluation value is stored in the tracking database 20, a resource allocation method having a high allocation effect will be calculated in the resource allocation method creating unit 21. Further, the target state evaluation unit 19 outputs a part of the input from the sensor state extractor 15, the observation information extractor 16, the tracking information extractor 17, and the filter state extractor 18 to the resource allocation scheme creating unit 21 as it is.

Next, upon receiving the evaluation value from the target state evaluator 19, the resource allocation method generator 21 refers to the tracking database 20 and, based on the evaluation value output from the target state evaluator 19, sets the target ( 1) Calculate and output a plurality of sets of distribution methods and distribution effect evaluation values of the sensor group 12 and the tracking filter group 14 for the target (n).

That is, the resource allocator 41 of the resource allocation method creating unit 21 sets the target (1) to the target (n) based on the tracking state of each target, which is the evaluation value output by the target state evaluation unit 19.
, The observation time, the observation conditions (accuracy, detection probability, false alarm probability, resolution), the type of the tracking filter, and all or a part of the operation parameters of the tracking filter are determined as the distribution method. If the sensor can be moved, the destination of the sensor to a place where the target can be easily observed is also included in the distribution method. The allocation effect evaluator 42 of the resource allocation method creator 21 refers to the target state evaluation value output from the target state evaluator 19 and the tracking database 20, and sets the target (1) to
The degree of improvement of the tracking performance of the target (n) is calculated, and the degree of improvement of the tracking performance is weighted by the magnitude of the target state evaluation value to calculate the distribution effect evaluation value.

Next, the resource management calculation unit 23 receives a plurality of allocation methods and allocation effect evaluation values of each resource from the resource allocation method creation unit 21, and receives the sensor group 12 and the tracking filter group 1.
4, the resource database 22
, An optimal distribution method is selected in consideration of the distribution effect of each distribution method and the load exerted on the sensor group 12 and the tracking filter group 14 by the distribution method.

That is, the tracking function monitor 51 of the resource management calculation unit 23 receives a plurality of allocation methods for each resource from the resource allocation method creation unit 21 and receives the current tracking filters (1) to (1) from the tracking filter group 14. When receiving the operation state of (n), it monitors whether or not the portion related to the tracking filter group 14 among the plurality of distribution methods can be executed in the current tracking filter group 14. The distribution method including the unexecutable distribution is deleted, and the executable processing method is referred to the resource database 22 to calculate the tracking processing load applied to the tracking filter group 14 at the time of execution. Here, the tracking processing load is, for example, a calculation amount when performing tracking calculation.

The observation function monitor 52 of the resource management calculation unit 23
Receives a plurality of allocation schemes for each resource from the resource allocation scheme creation unit 21 and receives the current operating states of the sensors (1) to (m) from the sensor group 12; It is monitored whether or not the portion related to 12 can be executed in the current sensor group 12. The allocation method including the unexecutable allocation is deleted, and the executable load method is referred to the resource database 22 to calculate the observation load applied to the sensor group 12 at the time of execution. here,
The observation load is, for example, the amount of energy consumed by the observation beam of the sensor.

The optimal distribution computer 53 of the resource management calculation unit 23
Calculates the common part between the executable distribution method output from the tracking function monitor 51 and the executable distribution method output from the observation function monitor 52, and makes this common part a candidate for adopting the distribution method. Then, from among the candidates for adopting this distribution method, an optimal distribution method that increases the distribution effect evaluation value while minimizing the observation load and the tracking processing load is determined.

When the resource management calculation unit 23 determines the optimal distribution method, the filter group indicator 24 determines whether the tracking filters (1) to (n) that constitute the tracking filter group 14 according to the optimal distribution method. Outputs operating conditions (operation instructions). When the resource management calculation unit 23 determines an optimal distribution method, the sensor group indicator 25 operates the sensors (1) to (m) constituting the sensor group 12 according to the optimal distribution method. )
Is output.

As is apparent from the above, the first embodiment
According to the above, the current tracking state of each target is evaluated from information on the operation state of the sensor group 12 and the tracking filter group 14, and the operating conditions of the sensor group 12 and the tracking filter group 14 are adjusted based on the evaluation result. So that
There is an effect that a high tracking performance can be obtained by efficiently operating the sensor group 12 and the tracking filter group 14.

Further, according to the first embodiment, the target evaluation value in which the tracking performance is degraded is increased, and the distribution effect evaluation value of the distribution method in which the tracking performance is greatly improved is increased. As a result, a distribution method that is advantageous for a target with large deterioration of the target is selected, so that the tracking performance of the entire target is kept high. Furthermore, according to the first embodiment, since the target state evaluation unit 19 evaluates the target state using both the evaluation criteria of the tracking maintainability and the tracking accuracy, the target that is likely to lose tracking and the tracking accuracy deteriorate. Resources are easily allocated to the target being set, and as a result, an effect is obtained in which a resource allocation method is applied such that the overall tracking performance of the target group 11 is improved.

According to the first embodiment, the tracking maintenance possibility evaluator 31 of the target state evaluation unit 19 determines whether the targets approach each other,
Since the tracking maintenance possibility evaluation value is calculated based on the passage of the clutter area and the number of consecutive loss detections, as a result of accurately grasping the situation where the tracking maintenance is difficult, the resource allocation method is calculated,
The effect of improving the overall tracking maintenance performance of the target group 11 is obtained. Also, according to the first embodiment, the tracking accuracy evaluator 32 of the target state evaluation unit 19 determines whether the target speed, the target mobility, the target estimation error, the fitness to the motion model, the distance between the target and the sensor are sufficient. Since the tracking accuracy evaluation value is calculated on the basis of the above, the situation in which the tracking accuracy is degraded is accurately grasped and the resource allocation method is calculated. As a result, the effect of improving the overall tracking accuracy of the target group 11 is obtained.

According to the first embodiment, the resource allocator 41 for calculating the method of allocating resources to the target and the distribution effect evaluator 42 for evaluating the degree of improvement in tracking performance by the distribution method are provided. In addition to the sensor assignment, an effective combination of the observation time, the sensor operation parameter, the type of the tracking filter, and the appropriate combination of the operation parameters of the tracking filter is calculated, so that effective tracking can be performed. According to the first embodiment, the resource management calculation unit 23 considers not only the distribution effect evaluation value of each distribution method but also the load applied to the sensor group 12 and the tracking filter group 14 by each distribution method. Since the optimal distribution method is determined, it is possible to obtain high tracking performance while saving resources. In addition, there is an effect that it is possible to avoid the risk of selecting a distribution method that exceeds the capabilities of the sensor group 12 and the tracking filter group 14.

Embodiment 2 FIG. 5 is a configuration diagram showing a resource management apparatus according to Embodiment 2 of the present invention. In the figure, the same reference numerals as those in FIG. 1 denote the same or corresponding parts, and a description thereof will be omitted. Reference numeral 61 denotes a predictor for predicting the future tracking state of each target, and 62 evaluates the same items as those of the target state evaluation unit 19, and evaluates the future tracking state of each target based on the prediction result of the predictor 61. It is a target state evaluation unit.
The estimator 61 and the target state evaluation unit 62 constitute a tracking state evaluation unit.

FIG. 6 is a block diagram showing the target state evaluation section 62. In the figure, the same reference numerals as those in FIG. 2 denote the same or corresponding parts, and a description thereof will be omitted. 63 is a tracking sustainability evaluator that evaluates the same items as the tracking maintenance possibility evaluator 31 and evaluates items such as proximity prediction between targets and arrival prediction to a clutter region. 64 is a tracking accuracy evaluator 32.
This is a tracking accuracy evaluator that evaluates the same items as, and also evaluates items such as the prediction error of the target motion data and the degree of fit to the motion model.

Next, the operation will be described. Since the configuration other than the predictor 61 and the target state evaluation unit 62 is the same as that of the first embodiment, only the operations of the predictor 61 and the target state evaluation unit 62 will be described. The predictor 61 receives the estimated values of the motion parameters of each target and the operation state of the tracking filter group 14 from the target state evaluation unit 62, predicts the future tracking state of each target, and calculates an arbitrary time later. Calculate the predicted value.

The target state evaluator 62 is used in the first embodiment.
, The future tracking state of each target is evaluated with reference to the prediction result of the predictor 61 in addition to the items evaluated by the target state evaluation unit 19. That is, the tracking maintenance possibility evaluator 63 of the target state evaluation unit 62 adds, for example, the following items to the evaluation target in addition to the items evaluated by the tracking maintenance possibility evaluator 31 in the first embodiment.

1. Proximity prediction between targets The evaluation value is increased when it is determined that certain targets will approach each other in the near future from the predicted position of each target. 2. Prediction of arrival at clutter region The evaluation value is increased when it is determined that a certain target enters the clutter region in the near future based on the predicted position of each target. It should be noted that the tracking maintenance possibility evaluator 63 weights and averages the above evaluation values to obtain a tracking maintenance possibility evaluation value.

Tracking accuracy evaluator 64 of target state evaluation section 62
In addition to the items evaluated by the tracking accuracy evaluator 32 in the first embodiment, for example, the following items are added to the evaluation target. 1. Prediction error of target motion parameters The future spread of the estimation error is predicted, and the evaluation value is increased when it is determined that the estimation error will not be captured in the sensor observation beam in the near future. 2. Fitness for Motion Model The fitness of the motion model is predicted, and the evaluation value is increased when it is determined that the target motion will be ambiguous in the near future. The tracking accuracy evaluator 64 weights and averages the above evaluation values to obtain a tracking accuracy evaluation value.

The target state evaluator 33 of the target state evaluation section 62
Calculates a weighted average of the tracking maintenance possibility evaluator 63 and the tracking accuracy evaluator 64 to calculate target state evaluation values for the targets (1) to (n).

As is clear from the above, the second embodiment
According to the first embodiment, the target state evaluation unit 62 is configured to evaluate not only the current state of the target but also the future state. Therefore, unlike the first embodiment in which the current target state is evaluated, Even if the evaluation value of the target state is low, the effect that the resource management based on the tracking state of the future target can be obtained by paying attention to the target whose evaluation value increases in the future from an early stage.

Embodiment 3 FIG. 7 shows a target state evaluation unit 62.
In the figure, the same reference numerals as those in FIG. 6 denote the same or corresponding parts, and a description thereof will not be repeated. 65 is a tracking necessity evaluator that evaluates the necessity of tracking each target;
Is a target state evaluator that combines the evaluation result of the tracking maintenance possibility evaluator 63, the evaluation result of the tracking accuracy evaluator 64, and the evaluation result of the tracking necessity evaluator 65 to evaluate the tracking performance for each target. .

Next, the operation will be described. The third embodiment is different from the second embodiment in that the necessity of tracking each target is added to the evaluation target. Hereinafter, portions different from the second embodiment will be described.

The tracking necessity evaluator 6 of the target state evaluator 62
5 receives various kinds of information from the sensor state extractor 15, the observation information extractor 16, the tracking information extractor 17, and the filter state extractor 18, and receives the traveling direction of each target, the distance to each target, and the speed of each target. Then, the necessity of tracking each target is evaluated in consideration of the estimated arrival time of each target with respect to the specific area and all or a part of the identification result of each target. For example, the following items are evaluated.

1. Going direction of the target If it is determined that the target approaching the sensor (1) to the sensor (m) or a specific area is important and the target moving away is not important, the sensor (1) to the sensor (m) or Increase the need to track targets approaching a particular area. 2. Target Distance If it is determined that a sensor (1) -sensor (m) or a target far from a particular area is not important, the need to track such targets is reduced. 3. Speed of the target If it is determined that a high speed among the sensors (1) to (m) or a target approaching a specific area is important, it is necessary to track the high speed target. Make it higher.

4. Estimated time to reach a specific area Using the estimated position and speed of the target, calculate the time to reach a specific area, and if it is determined that a target with a short arrival time is important, Increase the need to track targets. 5. Tracking target identification result When the size, characteristics, type, etc. of the targets (1) to (n) can be identified by some means, the tracking is performed when it is determined from the identification result that the target should be noted. Increase the need.

The target state evaluator 66 uses the tracking necessity evaluation value of the tracking necessity evaluator 65 in addition to the evaluation results of the tracking maintenance possibility evaluator 63 and the tracking accuracy evaluator 64 as criteria for target state evaluation. Then, a target state evaluation value for each target is calculated by taking a weighted average of these three inputs. In addition, the index for the tracking necessity evaluator 65 to evaluate the tracking necessity of the target is not limited to these, and can be used to evaluate the target in some sense as to whether the tracking necessity is high or low. If so, an index other than the above may be used.

As is apparent from the above, the third embodiment
According to the first and second embodiments, since the tracking necessity of each target obtained from the tracking necessity evaluator 65 is also used as the evaluation criterion of the target state, the resource management is performed on the assumption that the values of the targets are all equal. Unlike the case 2, it is possible to objectively evaluate the tracking necessity of each target. Therefore, a target that needs to be tracked with particular attention is selected, and resource management is performed so that the target is not particularly lost. The effect that can be performed is obtained.

Embodiment 4 FIG. 8 is a configuration diagram showing the resource allocation method creating unit 21. In the figure, the same reference numerals as those in FIG. 71
Is the operational parameters of the sensor group 12 and the tracking filter group 14
A parameter setting range calculator 72 for determining the setting range of the operation parameters of the sensor group 12 and the tracking filter group 14
Is a resource allocator that calculates a resource allocation method for each target so that the parameter falls within a set range.

Next, the operation will be described. The fourth embodiment is different from the first to third embodiments in that the resource allocation method for each target is calculated so that the parameters of the sensor group 12 and the tracking filter group 14 fall within the set ranges. Hereinafter, portions different from the first to third embodiments will be described.

When the target state evaluation unit 62 outputs the target state evaluation value, the parameter setting range calculator 71 of the resource allocation method creation unit 21 sets all or some of the elements of the allocation method calculated by the resource allocation unit 72. Determine the range. For example, regarding the observation time of the target, attention is paid to the estimation error of the target. Predict the magnitude of the estimation error several seconds after the current time,
The time at which the magnitude exceeds a predetermined threshold is defined as the upper limit Tmax of the observation time. The distribution method related to the target observation time calculates a set range in which the next observation time is within the time Tmax. With respect to other operation parameters, how the operation parameters affect the observation and tracking of the target is calculated with reference to the tracking database 20, and the tracking accuracy, the tracking maintenance performance, and the like are set to a predetermined performance. The setting range is determined so as not to be below.

The resource allocation unit 72 of the resource allocation method creating unit 21
When the parameter setting range calculator 71 determines the parameter setting range, the observation sensor, observation time, observation condition (accuracy, detection probability, false alarm probability, resolution) for the target (1) to target (n), tracking filter And all or some of the distribution parameters of the operation parameters of the tracking filter are determined. This distribution method is determined so as not to exceed the setting range of each parameter output from the parameter setting range calculator 71.

As is apparent from the above, the fourth embodiment
According to the first to third embodiments, since the resource allocator 72 calculates the resource allocation method so as not to exceed the setting range of each parameter, the first to third embodiments calculate the allocation method without limiting the parameter range. Unlike this, it is possible to narrow down the number of distribution methods to be calculated and calculate only the distribution method that is close to the optimal distribution. For this reason, the resource management calculation part 23 can obtain the effect of reducing the amount of calculation for selecting the optimal distribution method.

[0076]

As described above, according to the present invention, the current tracking state of each target is evaluated from the information on the operating state of the sensor group, and the operating condition of the sensor group is adjusted based on the evaluation result. With such a configuration, there is an effect that the sensor group can be operated efficiently and high tracking performance can be obtained.

According to the present invention, the current tracking state of each target is evaluated from the operating state of the tracking filter group constituting the tracking means, and the operating conditions of the tracking filter group are adjusted based on the evaluation result. Therefore, there is an effect that the tracking filter group can be operated efficiently and high tracking performance can be obtained.

According to the present invention, the current tracking state of each target is evaluated from the information on the operation states of the sensor group and the tracking filter group, and the operating conditions of the sensor group and the tracking filter group are adjusted based on the evaluation result. So that
There is an effect that high tracking performance can be obtained by efficiently operating the sensor group and the tracking filter group.

According to the present invention, since information relating to the surrounding situation surrounding each target is extracted and the current tracking state of each target is evaluated in consideration of the information, tracking maintenance becomes difficult. The resource allocation method is calculated by accurately grasping the situation, and as a result, the tracking maintenance performance of each target is improved.

According to the present invention, the information relating to the estimated value of the motion data of each target is extracted, and the current tracking state of each target is evaluated in consideration of the extracted information. In this case, the resource allocation method is calculated by accurately grasping the situation in which the target is performed, and as a result, the tracking accuracy of each target is improved.

According to the present invention, as the current tracking state of each target, the difficulty level of tracking maintenance of each target is evaluated, and the estimation accuracy of the motion parameters of each target is evaluated. There is an effect that the situation in which the accuracy is deteriorated can be accurately grasped.

According to the present invention, the proximity status between targets,
Since it is configured to evaluate the degree of difficulty in keeping track of each target in consideration of the situation of entering the clutter area or the number of consecutive detections of loss of each target, it is necessary to accurately evaluate the degree of difficulty in keeping track of each target. There is an effect that can be.

According to the present invention, the speed of each target, the mobility of each target, the estimation error of the motion specifications of each target, the degree of fit to the motion model, or the distance of each sensor constituting the sensor group to each target are determined. Since the configuration is such that the estimation accuracy of the motion parameters of each target is evaluated in consideration of the above, there is an effect that the estimation accuracy of the motion parameters of each target can be accurately evaluated.

According to the present invention, the operation conditions of the tracking filter group are adjusted by adjusting the assignment of each sensor constituting the sensor group to each target, the observation time of each sensor, or the operation parameter of each sensor as the operating conditions of the sensor group. As the condition, the type of each filter constituting the tracking filter group or the operation parameter of each filter was adjusted, so that
There is an effect that the operating conditions of the sensor group and the tracking filter group can be accurately adjusted.

According to the present invention, since the operating conditions of the sensor group and the tracking filter group are adjusted in consideration of the load of the sensor group and the tracking filter group, high tracking performance can be achieved while saving resources. There is an effect that can be obtained. Further, there is an effect that it is possible to avoid the risk of selecting a distribution method that exceeds the capabilities of the sensor group and the tracking filter group.

According to the present invention, since the future tracking state of each target is predicted and the future tracking state of each target is evaluated, even if the evaluation value of the target state is low now,
There is an effect that it is possible to pay attention to a target whose evaluation value will increase in the future from an early stage and to perform resource management based on a tracking state of the future target.

According to the present invention, since the necessity of tracking each target is evaluated, the necessity of tracking each target can be objectively evaluated. As a result, tracking is performed with particular attention. There is an effect that it is possible to select a target that needs to be performed, and to perform resource management so that the target is not particularly overlooked.

According to the present invention, tracking of each target is performed in consideration of the traveling direction of each target, the distance to each target, the speed of each target, the predicted arrival time of each target in a specific area, or the identification result of each target. Since the configuration is such that the necessity of the target is evaluated, the necessity of tracking each target can be objectively evaluated.

According to the present invention, when the operating conditions of the sensor group and the tracking filter group are adjusted, the parameters of the sensor group and the tracking filter group are configured to fall within a predetermined allowable range. It is possible to calculate only the distribution scheme close to the optimal distribution by narrowing down the number of calculations, and as a result, it is possible to reduce the amount of calculation for selecting the optimal distribution scheme.

[Brief description of the drawings]

FIG. 1 is a configuration diagram showing a resource management device according to a first embodiment of the present invention.

FIG. 2 is a configuration diagram illustrating a target state evaluation unit.

FIG. 3 is a configuration diagram illustrating a resource allocation scheme creating unit.

FIG. 4 is a configuration diagram illustrating a resource management calculation unit.

FIG. 5 is a configuration diagram showing a resource management device according to a second embodiment of the present invention.

FIG. 6 is a configuration diagram illustrating a target state evaluation unit.

FIG. 7 is a configuration diagram illustrating a target state evaluation unit.

FIG. 8 is a configuration diagram illustrating a resource allocation scheme creation unit.

FIG. 9 is a configuration diagram showing a conventional resource management device.

[Explanation of symbols]

11 target group, 12 sensor group, 13 observation information fusion device, 14 tracking filter group (tracking means), 15 sensor state extractor (tracking state evaluation means), 16 observation information extractor (tracking state evaluation means), 17 tracking information Extractor (tracking state evaluation means), 18 filter state extractor (tracking state evaluation means), 19 target state evaluation unit (tracking state evaluation means), 20 tracking database (adjustment means), 21 resource allocation method creation unit (adjustment means) ), 22 resource database (adjustment means), 23 resource management calculation section (adjustment means),
24 filter group indicator (adjustment means), 25 sensor group indicator (adjustment means), 31 tracking maintenance possibility evaluator, 3
2 tracking accuracy evaluator, 33 target state evaluator, 41 resource allocator, 42 allocation effect evaluator, 51 tracking function monitor, 52 observation function monitor, 53 optimal allocation calculator, 6
1 Predictor (Tracking State Evaluator), 62 Target State Evaluator (Tracking State Evaluator), 63 Tracking Sustainability Evaluator, 64 Tracking Accuracy Evaluator, 65 Tracking Necessity Evaluator,
66 Target state evaluator, 71 Parameter setting range calculator, 72 Resource distributor.

Continued on the front page (72) Inventor Shingo Tsujimichi 2-3-2 Marunouchi, Chiyoda-ku, Tokyo Mitsubishi Electric Corporation (72) Inventor Yoshio Kosuge 2-3-2 Marunouchi, Chiyoda-ku, Tokyo Mitsubishi Electric F term in the company (reference) 5J070 AC01 AC06 AD05 AE04 AE05 AE06 AF01 AF03 AF06 AH19 AJ13 AK22 BB05 BB06 BB13 BB14 BB16 BB20

Claims (14)

[Claims] When receiving observation results of a group of sensors observing a plurality of targets, a tracking means for performing tracking calculation of each target, and information relating to an operation state of the sensor group are extracted, and each target is extracted from the information. A resource management device comprising: a tracking state evaluation unit that evaluates a current tracking state of the sensor group; and an adjustment unit that adjusts operating conditions of the sensor group based on an evaluation result of the tracking state evaluation unit. 2. Receiving observation results of a sensor group observing a plurality of targets, extracting tracking information for executing tracking calculation for each target and information relating to the operation state of a tracking filter group constituting the tracking means. A resource management apparatus comprising: a tracking state evaluation unit that evaluates a current tracking state of each target from the information; and an adjustment unit that adjusts operating conditions of the tracking filter group based on an evaluation result of the tracking state evaluation unit. . 3. Receiving an observation result of a sensor group observing a plurality of targets, a tracking means for executing tracking calculation of each target, an operation state of the sensor group, and an operation of a tracking filter group constituting the tracking means. Extracting information related to the state, a tracking state evaluation unit for evaluating the current tracking state of each target from the information, and operating conditions of the sensor group and the tracking filter group based on the evaluation result of the tracking state evaluation unit. A resource management device comprising: adjusting means for adjusting. 4. The tracking state evaluating means according to claim 3, wherein the tracking state evaluation means extracts information relating to a surrounding situation surrounding each target, and evaluates a current tracking state of each target in consideration of the information. Resource management device. 5. The tracking state evaluation means extracts information relating to an estimated value of the motion data of each target, and evaluates the current tracking state of each target in consideration of the extracted information. Alternatively, the resource management device according to claim 4. 6. The tracking state evaluation means evaluates the degree of difficulty in maintaining the tracking of each target as the current tracking state of each target, and evaluates the estimation accuracy of the motion parameters of each target. The resource management device according to claim 5. 7. The tracking state evaluation means evaluates the degree of difficulty in maintaining tracking of each target in consideration of a proximity state between targets, a state of approach to a clutter area, or a continuous number of times of loss detection of each target. The resource management device according to claim 6, wherein 8. A tracking state evaluation means, comprising: a speed of each target, a mobility of each target, an estimation error of motion specifications of each target, a degree of adaptation to a motion model, or a target of each sensor constituting a sensor group with respect to each target. 7. The resource management device according to claim 6, wherein the estimation accuracy of the motion parameters of each target is evaluated in consideration of the distance. 9. The adjusting means adjusts, as operating conditions of the sensor group, allocation of each sensor constituting the sensor group to each target, observation time of each sensor or operation parameters of each sensor, and operates the tracking filter group. 7. The resource management device according to claim 6, wherein the condition is to adjust a type of each filter constituting the tracking filter group or an operation parameter of each filter. 10. The resource management device according to claim 6, wherein the adjusting unit adjusts the operation conditions of the sensor group and the tracking filter group in consideration of the load of the sensor group and the tracking filter group. . 11. The resource management device according to claim 6, wherein the tracking state evaluation means predicts a future tracking state of each target and evaluates a future tracking state of each target. 12. The resource management device according to claim 6, wherein the tracking state evaluation means evaluates the need for tracking each target. 13. A tracking state evaluating means, wherein each target is considered in consideration of a traveling direction of each target, a distance to each target, a speed of each target, a predicted arrival time of each target in a specific area, or an identification result of each target. 13. The resource management device according to claim 12, wherein the need for tracking is evaluated. 14. An apparatus according to claim 6, wherein said adjusting means adjusts the operating conditions of the sensor group and the tracking filter group so that the parameters of the sensor group and the tracking filter group fall within a predetermined allowable range. Resource management device as described.