CN115220002B - Multi-target data association tracking method and related device for fixed single station - Google Patents
Multi-target data association tracking method and related device for fixed single station Download PDFInfo
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Abstract
The embodiment of the application discloses a multi-target data association tracking method for a fixed single station, which is used for more accurately carrying out data association on observation values of multi-target objects. The method comprises the following steps: acquiring a first actual observation value set of at least two target objects at a target moment; determining an association center value of each target object according to a target time period of the target moment; determining a second actual observation value set of each target object from the first actual observation value sets according to the association center value based on the joint maximum likelihood estimation model; and determining the motion state information of each target object at the target moment according to the second actual observation value set based on the single-target tracking model, wherein the motion state information is used for determining the track of each target object in the target time period.
Description
Technical Field
The application relates to the technical field of radar data processing, in particular to a multi-target data association tracking method and a related device for a fixed single station.
Background
In passive positioning systems, multiple observations may be obtained from one detection due to the presence of environmental noise and random disturbances, and of these observations it is not known which are from the tracked targets and which are false observations; and when a plurality of target objects need to be observed, some observed values far away from the predicted value of the motion state of the target object also do not determine which target object belongs to, which requires that the observed values are associated with the target objects, and is called data association.
The method for associating the data comprises the steps of predicting the motion state of a target object at the next moment according to the observed value at the previous moment, comparing the obtained predicted value with the actual value, calculating the data with smaller difference through maximum joint likelihood estimation, and determining the associated pair data corresponding to the actual value and the target object.
However, the positioning error is too large at the initial stage of the track, and the filtering cannot achieve a stable convergence effect, so that if the predicted value of the track measurement is used for correlation with the real measurement, the correlation error is easy to occur, and the final error correlation of the whole track is seriously caused.
Disclosure of Invention
The embodiment of the application mainly aims to provide a multi-target data association tracking method and a related device for a fixed single station, aiming at improving the accuracy of data association in multi-target tracking.
In a first aspect, an embodiment of the present application provides a method for multi-target data association tracking of a fixed single station, where the method includes the following steps: acquiring a first actual observation value set of at least two target objects at a target moment; determining an association center value of each target object according to a target time period of the target moment; determining a second actual observation value set of each target object from the first actual observation value sets according to the association center value based on a joint maximum likelihood estimation model; and determining motion state information of each target object at the target moment according to the second actual observation value set based on a single-target tracking model, wherein the motion state information is used for determining a track of each target object in the target time period.
In a second aspect, embodiments of the present application provide a passive positioning system comprising a memory, a processor, a program stored on the memory and executable on the processor, and a data bus for enabling a connected communication between the processor and the memory, the program when executed by the processor implementing the steps of the aforementioned method.
In a third aspect, embodiments of the present application provide a storage medium for computer-readable storage, the storage medium storing one or more programs executable by one or more processors to implement the steps of the foregoing method.
According to the information entropy-based combined maximum likelihood data association method and the information entropy-based combined maximum likelihood data association device, the association center value of at least two target objects in the target time period is determined according to the target time period of the target objects, the motion state information of each target object in the target time is determined according to the second actual observation value set of each target object, and further each target object is tracked, so that the fact that the second actual observation value set of each target object is determined from the first actual observation value sets of the at least two target objects according to different characteristics of different stage tracks in different stages of tracks can be achieved, and data association can be carried out on observation values of multiple target objects more accurately.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a fixed single-station passive positioning multi-target tracking method according to an embodiment of the present application;
FIG. 2 is a flowchart illustrating a step of a multi-objective data association tracking method for a fixed single station according to an embodiment of the present application;
Fig. 3 is a schematic diagram of a multi-objective data association method for a fixed single station according to an embodiment of the present application;
FIG. 4 is a flowchart illustrating another step of a multi-objective data association tracking method for a fixed single station according to an embodiment of the present application;
FIG. 5 is a flowchart illustrating another step of a multi-objective data association tracking method for a fixed single station according to an embodiment of the present application;
FIG. 6 is a flowchart illustrating another step of a multi-objective data association tracking method for a fixed single station according to an embodiment of the present application;
FIG. 7 is a flowchart illustrating another step of a multi-objective data association tracking method for a fixed single station according to an embodiment of the present application;
FIG. 8 is a flowchart illustrating another step of a multi-objective data association tracking method for a fixed single station according to an embodiment of the present application;
fig. 9 is a real track diagram in an application scenario of a multi-target data association tracking method for a fixed single station according to an embodiment of the present application;
fig. 10 is a real and estimated trajectory diagram in an application scenario of a multi-target data association tracking method for a fixed single station according to an embodiment of the present application;
fig. 11 is a root mean square error diagram of eight targets in an application scenario of a multi-target data association tracking method for a fixed single station according to an embodiment of the present application;
fig. 12 is an average position error map of eight targets in an application scenario of a multi-target data association tracking method for a fixed single station according to an embodiment of the present application;
fig. 13 is a schematic structural diagram of a passive positioning system according to an embodiment of the present application.
Detailed Description
Radar data processing and radar signal processing are important components in modern radar systems. The signal processing is used for detecting the target, and various useful information of the target such as distance, speed, shape of the target and the like are acquired by a certain method. The data processing can further process the track points and the tracks of the targets, predict the positions of the targets at the future time, and form reliable target tracks, so that real-time tracking of the targets is realized.
The radar data processing comprises a plurality of main links such as point trace aggregation, track initiation, target tracking, multi-target association and the like. Two basic problems studied by the method are point trace-to-point trace and point trace-to-track association problems under different environments. The former relates to track initiation, and focuses on control of a track relevant range and selection of a relevant algorithm; the latter involves target tracking, focusing on the application of target motion models and filtering algorithms. The purpose of radar data processing is to estimate the track of the target and to give the position of the target at the next moment using the information of the target provided by the radar.
The radar data processing process mainly comprises data preprocessing, track initiation, data association, tracking filtering, track extinction, quality evaluation and the like.
The input data of radar data processing is also called observation, which is not data obtained by direct scanning of the radar, but data obtained by firstly processing radar signals and then obtaining the data by a data recorder. Typical observations include radar scan period, radar scan batch, number of targets scanned per batch, and specific information (radial distance, azimuth angle, pitch angle) for each target. In practical engineering, observations are generally contaminated with noise, these contamination coming mainly from the following aspects:
1) Random false alarms exist in the scanning process;
2) Clutter generated by false targets;
3) An interference target;
4) Baits, and the like.
Although the modern radar signal processing technology has been greatly developed, some interference is doped in the observation after signal processing, and the quantity of general observation data is large, so that the requirements on the storage and processing of subsequent computers are high. The data preprocessing is to screen one data before the observed data is subjected to other data processing processes such as starting, association and the like, reject the data which are not in the threshold, and only the data which pass through all the decision threshold are reserved.
The observation data preprocessing has the advantages that the scale of data in the subsequent data processing process is obviously reduced, the calculated amount is greatly reduced, the burden of a computer can be lightened to a certain extent, the data processing speed and the target tracking precision are improved, and meanwhile, the possibility of forming false tracks is reduced.
The wave gate is an important concept in the data processing process, and the concept is used in the track initiation process, the data association process and the like. The wave gate is a region, and is generally divided into an initial wave gate and an associated wave gate.
The initial gate is typically used in the initial stage of the track, and is a region centered at any point that defines a spatial range in which the target's observations may occur. Because the track is initially a relatively long distance from the target, the initial gate typically establishes a large gate for better target capture.
The correlation gate is a spatial region centered on the predicted value of the tracked object, and defines the range in which the observed value of the tracked object may appear. The shape and size of the relevant waveforms are determined on the one hand so that there is a high probability of a real observation falling into the gate, and on the other hand so that too many irrelevant observation tracks are not allowed in the relevant gate. The size of the associated gate should generally match the type of target, e.g. the gate of a fixed target generally depends only on the accuracy of the observation, the gate of a linear target depends on the accuracy of the observation and the prediction filter, and the gate of a maneuvering target also takes into account acceleration factors, etc. The related wave gates which are commonly used are rectangular wave gates, annular wave gates, elliptic wave gates, sector wave gates under a polar coordinate system and the like.
Track initiation refers to the process of establishing a first point of track for a target, i.e., track establishment from a target falling within radar detection range to the target. The track initiation process is an important link in the radar data processing process. Colloquially speaking, "the good beginning is half of success", on the contrary, if the track is not successfully started, the track is not successfully established, and the reliable track is not established, so that the accurate tracking of the target cannot be realized.
The track initiation process is one of important links in the radar data processing process, one of the tasks of the track initiation is to quickly establish a track for a target entering a radar power zone, and the other task is to avoid false points as far as possible from establishing false tracks. However, in order to avoid the establishment of false tracks, the establishment needs to be initiated for a long time, and a certain contradiction exists between the two tasks, namely the contradiction of speed and quality, so that an optimal compromise needs to be found between the two tasks. The track initiation algorithm is a plurality of, and more commonly used methods include an intuitive method, a logic method, a corrected logic method and the like.
In an ideal object motion model, the observation environment is always considered "clean", only one observation is detected at a time, and this observation is from the object being tracked. But in practical systems the environment is not ideal. Due to the existence of factors such as observation noise, false alarms and the like can occur, and in addition, clutter can occur in the area where the target possibly appears due to random interference existing in the observed area. In summary, one detection may result in multiple observations, and of these observations, it is not known which are from tracked objects and which are false observations. This factor determines that the data correlation process is an important element in radar data processing systems.
When only one target exists in the radar scanning area and no interference exists, only one point trace exists in the relevant wave gate of the target, and at the moment, the problem of data association does not exist. However, when multiple targets appear in the radar scanning area or clutter exists, the same point trace may fall into multiple wave gates or multiple point traces may appear in the same wave gate, and this involves a problem of data association. The data association is to judge the relation between the radar observation data at a certain moment and the observation data at other moments or the existing tracks, so that the track point and track pairing process is realized.
In general, data association can be divided into the following cases, depending on the objects associated with each other:
1) Track initiation: the trace points are interconnected with the trace points;
2) Track updating: the interconnection of the points and the track (track prediction points) can also be called track maintenance;
3) Track fusion: the track is interconnected with the track.
There are many data association methods, and they can be roughly classified into two types, one is a bayesian type data association algorithm, and the other is a maximum likelihood type data association algorithm. The Bayesian algorithm mainly comprises a nearest domain algorithm, a probability data association algorithm and the like, and is based on Bayesian criteria. The maximum likelihood algorithm mainly comprises an aviation integral fork method, a combined maximum likelihood algorithm and the like, and the maximum likelihood algorithm is based on the likelihood ratio of the observation sequence.
The embodiment of the application provides a multi-target data association tracking method and a related device for a fixed single station, which can determine a second actual observed value set of each target object from first actual observed value sets of at least two target objects by using different association center values according to different characteristics of tracks at different stages of tracks, so that data association is more accurately carried out on observed values of the multi-target objects.
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The flow diagrams depicted in the figures are merely illustrative and not necessarily all of the elements and operations/steps are included or performed in the order described. For example, some operations/steps may be further divided, combined, or partially combined, so that the order of actual execution may be changed according to actual situations.
It is to be understood that the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should also be understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
Some embodiments of the present application are described in detail below with reference to the accompanying drawings. The following embodiments and features of the embodiments may be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a schematic diagram of a passive positioning multi-target tracking method for a fixed single station according to an embodiment of the application.
The passive positioning system acquires actual observation value sets of a plurality of target objects at different moments, wherein the actual observation values can comprise azimuth angle information, azimuth angle change rate information, doppler frequency change rate information and the like. Each actual observed value in the actual observed value sets of the plurality of target objects does not necessarily have a one-to-one correspondence with the target object, and even if the correspondence between the actual observed value at the initial time and each target object is determined due to the random disturbance and the existence of the environmental noise, the correspondence between the actual observed value at the subsequent time and each target object cannot be determined.
Through data association, the actual observation values of multiple targets can be associated with each target object, and the actual observation value corresponding to each target object at the target moment is determined.
After the data association is completed and the actual observation value of each target object corresponding to the target moment is determined, the multi-target tracking problem is converted into a single-target tracking problem, maneuvering judgment is needed to be carried out on each target object, and track initiation and track termination of each target object are determined after tracking gate rules and filtering and prediction are utilized.
After the track start and end of each target object are determined, the multiple target objects can be comprehensively analyzed, and finally the state of a system formed by the multiple target objects is determined.
Based on the architecture schematic diagram of the fixed single-station passive positioning multi-target tracking method shown in fig. 1, the embodiment of the application provides a multi-target tracking method associated with maximum likelihood data.
Referring to fig. 2, a flowchart of a step of a multi-objective data association tracking method for a fixed single station is provided in an embodiment of the present application.
201. And acquiring a first actual observation value set of at least two target objects at the target moment.
An observation station in the passive positioning system observes a moving system containing at least two target objects, and analyzes electromagnetic wave signals generated by the moving system containing at least two target objects and received by the observation station to obtain a first actual observation value set of the moving system containing at least two target objects at a target moment, wherein the first actual observation value set represents an actual observation value set corresponding to the moving system containing at least two target objects at the target moment, and the corresponding relation between each target object in the moving system and each actual observation value in the first actual observation value set is not accurately established.
202. And determining the association center of each target object according to the target time period of the target moment.
The implementation of the positioning tracking of a moving system comprising at least two target objects requires that the actual observations in a first set of actual observations representing the actual observations of the moving system as a whole are associated with each target object, i.e. that the actual observations of each target object are determined. The association center is a basis in the process of establishing association between each actual observed value in the first actual observed value set and each target object. The association center is not fixed, but rather the selection of the association center for each target object is dynamically adjusted according to the target time period in which the target object is moving at the target time.
203. A second set of actual observations for each target object is determined from the first set of actual observations based on the associated central values based on the joint maximum likelihood estimation model.
After determining a correlation center value of a target moment according to a target time period of a motion system comprising at least two target objects, taking the correlation center value as a basis for establishing correlation between each actual observed value in a first actual observed value set and each target object based on a joint maximum likelihood estimation model, determining a second actual observed value set of each target object from the first actual observed value set, wherein each actual observed value in the second actual observed value set has a corresponding relation with each target object.
204. And determining the motion state information of each target object at the target moment according to the second actual observation value set based on the single-target tracking model, wherein the motion state information is used for determining the track of each target object in the target time period.
After the association relation between each actual observed value in the first actual observed value set and each target object is established, the multi-target tracking problem is converted into a single-target tracking problem, namely, the determination of the motion state of a motion system comprising at least two target objects is converted into the determination of the motion state of each target object in the motion system. And determining the motion state information of each target object at the target moment according to the second actual observation value set corresponding to each target object based on the single-target tracking model, and determining the track of each target object in the target time period according to the motion state information of each target object at a plurality of moments so as to realize positioning and tracking of each target object.
Based on the multi-target data association tracking method of the fixed single station shown in fig. 2, in step 202, the association center of each target object may be determined according to the target time period where the target time is located in multiple manners, and according to the motion characteristics and the observation characteristics of the motion system including at least two target objects in different stages, the data association performed by determining different association centers in different stages may improve accuracy, and the selection of different association centers in different stages is described below.
Referring to fig. 3, fig. 3 is a schematic diagram of a multi-objective data association method for a fixed order station.
The data association is the core of a multi-target tracking scheme, can convert a multi-target tracking problem into single-target tracking, and comprises three steps of association preprocessing, selection of an association center, calculation of information entropy uncertainty and association.
Firstly, measurement information preprocessing is carried out, a tracking wave gate is arranged, all observations received by an observation station pass through the wave gate, the correlation wave gate filters out observations with excessive errors, and the observations passing through the correlation wave gate are candidate observations.
Then, the correlation center is selected, and the initial positioning error of the fixed single station is overlarge, so that the observed grey predicted value is used as the correlation center in the initial stage of the track, and the observed predicted value is used as the correlation center after the target tracking is stable.
After the association center is determined, the association method based on uncertainty of information entropy is used for matching the observation of each target with the association center to construct association pairs, each association pair represents that each target has one observation and one pairing, and the multi-target tracking problem is converted into a single-target tracking problem.
After the data association is established, the tracking of each target is a single target tracking problem, and the tracking of the single target comprises the initial positioning, maneuvering judgment and filtering of the target.
At the track starting moment, the initial state of the target can be directly calculated by utilizing the relation between the observation of each target and the target state, the initial state of each target is taken as the starting point, and the tracking of each target track at the later moment is estimated by utilizing the observation information and a filtering algorithm.
And making a judgment of target maneuver before updating the target state by using an observation and filtering algorithm. When the maneuvering target is detected, the covariance is adaptively adjusted and then filtered, so that tracking of the maneuvering target is completed.
And finally, carrying out state update estimation on the targets in the associated pair by using a filtering algorithm and observation in the associated pair.
The initial stage of the track in the embodiment of fig. 3 will be described below based on the embodiment of fig. 2, with the use of different association centers at different stages in the embodiments of fig. 2 and 3.
Referring to fig. 4, fig. 4 is a flowchart illustrating another step of the multi-objective data association tracking method of the fixed single station in the track initiation stage, and step 202 in fig. 4 includes step 2021 and step 2022.
2021. When the target time period indicates that at least two target objects are in the track initial stage, a gray prediction value of each target object at the target moment is determined based on the gray prediction model.
When the target time period of the current target time of at least two target objects represents the track initial stage, the last n times of measurement which are successfully associated are used as prediction preparation data, and then a gray prediction model is used for obtaining a group of prediction data. The gray predictive model is a mathematical model that can be built with little, incomplete information and then used to predict the next predicted value. Currently, typical gray prediction methods are numerous, such as regression analysis.
Firstly, accumulating, namely accumulating the data at the previous moment in sequence to form a new number sequence, and setting the original number sequence as
z(0)＝(z(0)(0),z(0)(1),…z(0)(k)) (1)
The one-time accumulation generation is expressed as:
GM (1.1) is one of the gray prediction systems, a first order differential equation model, which is in the form of:
the prediction formula is as follows:
Defined by the derivative:
when Δt takes 1, so:
The solving formulas for obtaining the predicted value by the arrangement formula (4) and the formula (6) are as follows:
the corresponding predicted value of k+1 time is:
2022. And determining the grey predicted value as the association center value.
And after the grey predicted value is obtained based on the grey predicted model, taking the grey predicted value as an associated central value of the target object at the target moment in the track initial stage.
Based on the embodiment shown in fig. 4, the actual observed value corresponding to each object is determined according to the association center value, and specifically, the correlation wave gate is used for screening from the first actual observed value set.
Referring to fig. 5, fig. 5 is a flowchart illustrating another step of the multi-target data association tracking method of the fixed single station, and step 203 in fig. 5 includes steps 2031 to 2033.
2031. And determining the relevant wave gate of each target object according to the grey predicted value.
The observation of the single target should be carried out through an associated wave gate, the gray predicted value of each target object at the target moment is taken as the wave gate center of each target object, and the associated wave gate of each target object at the target moment can be determined after the wave gate shape is determined.
It should be noted that the shape of the wave gate may be selected from a plurality of shapes, such as an elliptic wave gate or a square wave gate, and the fixed single-station multi-target tracking scheme in this embodiment selects the elliptic wave gate as the relevant wave gate.
2032. And determining a candidate observation value set of each target object from the first actual observation value set according to the correlation wave gate.
The correlation gate can limit the number of observations participating in correlation discrimination and filter out observations with excessive errors. Each target object has a correlation gate, and the candidate observation value set of each target object can be screened from the first actual observation value set according to the correlation gate of each target object.
2033. And determining a second actual observation set according to the candidate observation set and the gray predicted value based on the combined maximum likelihood estimation model.
After the candidate observation value set of each target object is determined, a second actual observation value set of each target object is determined according to the candidate observation value set and the gray predicted value of each target object based on the combined maximum likelihood estimation model.
In connection with the embodiment shown in fig. 5, the determination of the second actual observation set for each target object from the candidate observation set for each target object in the embodiment of the present application will be described below.
Referring to fig. 6, step 2033 in fig. 6 includes steps 20331 to 20333.
20331. And calculating the difference value between the gray predicted value and each candidate observation value in the candidate observation value set.
The matrix consisting of m targets and n measurement indexes of the system is set as follows:
where lambda ij represents the jth measurement index of the jth target.
Taking the difference between the measured value and the measured predicted value in the matrix A, and then taking the absolute value, and marking as:
Wherein Delta ij represents the difference between the jth measurement index of the target i and its corresponding predicted value, i.e
20332. And based on the information entropy calculation model, taking the difference value as input to obtain the expansion uncertainty of each candidate observation value.
The information entropy is a measure of the disorder degree of the system, and assuming that the system comprises a plurality of measurement indexes j (j=1, 2, …, n), and the specific gravity occupied by a single measurement index of each target i (i=1, 2, …, m) is p ij, the entropy of the measurement index is defined as follows:
When (when) The maximum entropy can be expressed as:
H＝log2mj max (13)
therefore, if the information entropy of the measurement index is smaller, the amount of information provided by the measurement index is larger.
The j-th measurement index of the target i has the following specific weight:
The extended uncertainty of the first measurement index is defined as:
if s is larger, the measurement index has a larger effect in the process of target tracking.
20333. Based on the joint maximum likelihood estimation model, a second set of actual observations is determined from the extended uncertainty of each of the effective observations.
Assume that state estimation of n targets is obtained at time k-1M k measurements at time k pass through the associated threshold, and the set of all confirmed measurements at time k is recorded as:
Determining the event of the j-th measurement in the measurement set belonging to the target i at the moment k as Dividing the measurement in each wave gate into a plurality of possible divisions
For some division thereof. The measurements within the feasible division r should meet the following requirements:
And is also provided with
For empty sets, the above equation indicates that a measurement can only belong to one track.
For each possible partition r, an event may be defined
Θ (r) = { dividing measurement combination r is true } (20)
The set of all possible partitions can be defined as:
Γ＝{r} (21)
And using the uncertainty of the information entropy as the measurement distance between the first index of a certain measurement division and the first index of the measurement predicted value.
The result of uncertainty of information entropy is complex, and the modulo result represents the measurement distance of the first index of a measurement division and the index corresponding to the measurement predicted value, namely
The improved uncertainty of the information entropy better reflects the difference between a certain index of measurement division and measurement prediction. The smaller the uncertainty, the closer the first index of the measurement division is to the index corresponding to the measurement predicted value. Therefore, the uncertainty of the information entropy of all indexes of a certain measurement division is combined, and the smallest value of the combined uncertainty of the information entropy is the maximum possible measurement division, namely
Based on the embodiments shown in fig. 2 to 6, the candidate observation set of each target object is determined from the first actual observation set according to the correlation gate, and may be screened from the first actual observation set in a manner of adding a gate constraint in a general manner.
Referring to fig. 7, step 2032 in fig. 2, 4 to 6 includes steps 20321 to 20323.
20321. And determining a measurement index predicted value set of each target object at the target moment.
And determining a measurement index predicted value set of each target object at the target moment, wherein the measurement index predicted value set comprises at least one of an azimuth angle predicted value set, a Doppler frequency predicted value set, an azimuth angle change rate predicted value set and a Doppler frequency change rate predicted value set.
20322. And determining the gate constraint corresponding to the relevant gate according to at least two measurement index predicted value sets and the measurement index actual value set of each target object at the moment before the target moment.
At least two sets of measurement index predictors are determined, in this embodiment a set of azimuth predictors and a set of doppler frequency predictors are selected.
On the basis of taking an elliptic wave gate as a related wave gate, the wave gate constraint of two measurement indexes of azimuth angle and Doppler frequency is increased.
Bearing angles measured by each target in association at moment k-1 are recordedDoppler frequency is/>The measured azimuth angle at time k is predicted as/>Measurement Doppler frequency prediction is/>The constraint range of azimuth angle is:
where β limit is the azimuthal constraint size.
The constraint range of the Doppler frequency is:
Where f limit is the doppler frequency constraint size.
20323. A set of candidate observations is determined from the first set of actual observations in accordance with a wave gate constraint.
Observations fall within the elliptic wave gate and are effectively measured for the target within the azimuth constraint and doppler frequency constraint. If no observation falls into the wave gate, the point uses the observed predicted value to update the state, and if no observation falls into the track for a plurality of times, the track is recorded to disappear. A set of candidate observations for each target object is determined from the first set of actual observations in accordance with a wave gate constraint.
The track stabilization phase of the embodiment of fig. 3 will be described below based on the embodiment of fig. 2, with the use of different association centers at different phases in the embodiments of fig. 2 and 3.
Referring to fig. 8, fig. 8 is a flowchart illustrating another step of the multi-objective data association tracking method of the fixed single station in the track stabilization phase, and step 202 in fig. 8 includes step 2023 and step 2024.
2023. When the target time period indicates that at least two target objects are in a track stabilization stage, determining an observation predicted value of each target object at a target moment based on a track prediction model;
And determining an observation predicted value of each target object at the target moment based on the track prediction model when the target moments of at least two target objects are in a target time period representing a track stabilization stage. The observed predicted value is obtained based on the track prediction model by taking the observed actual value as an input.
2024. And determining the observed predicted value as the association center value.
After the observation predicted value is determined, the observation predicted value is used as a correlation center when the target time period is in the stable stage of the aeroplane.
It should be noted that the technical solutions of the embodiments shown in fig. 5 to fig. 7 may be applied to the technical solutions of the aeroplane stabilization stage shown in the embodiment shown in fig. 8, that is, fig. 8 and fig. 5, fig. 6 and fig. 7 may be combined to form the technical solutions similar to the embodiments shown in fig. 5 and fig. 6 and fig. 7, and details thereof will not be repeated here.
An application scenario of the technical solution formed by combining the embodiments shown in fig. 2 to 8 is shown in fig. 9.
Referring to fig. 9, fig. 9 is a real track diagram in an application scenario of a multi-target data association tracking method for a fixed single station according to an embodiment of the present application.
In a two-dimensional scenario, the observation station is located at the origin of coordinates, the target is 200-300km away from the observation station, and the target radiation source makes uniform motion with acceleration disturbance. The total duration of the target movement is 300s. The frequency of the target radiation source is 10GHz, and the target state is as follows: The observed quantity comprises azimuth angle alpha, azimuth angle change rate/> Doppler frequency f, doppler frequency rate of change/>The standard deviation of the observed noise is respectively as follows: σ α =2°,/>σf＝1Hz、/>The observation period T=1s simulates various motion scenes, and the influence of various scenes on the performance index is recorded. Simulation scene: 8 targets move at uniform speed with turning maneuver, and initial states of the targets are respectively as follows:
(-20km,0.3km/s,285km,0.1km/s)、(100km,-0.3km/s,285km,0.1km/s)、
(-10km,0.3km/s,310km,0km/s)、(-10km,0.3km/s,290km,0km/s)、
(60km,-0.3km/s,285km,0.1km/s)、(-45km,0.3km/s,270km,0.3km/s)、
(90km,0.3km/s,305km,-0.2km/s)、(-50km,-0.3km/s,260km,0.2km/s)；
In the two-dimensional plane, the target movement interval is in the first quadrant and the second quadrant, the targets 1-5 do uniform linear movement, the targets 6 do uniform linear movement with direction adjusted, and the movement is respectively performed at 100s and 200 s; the target 7 and the target 8 firstly do a section of uniform linear motion, then do uniform turning motion, and finally do a section of uniform linear motion, the turning rate is 2.9 degrees/s, and the turning radius is 10km.
Fig. 9 is a real track of each target, and fig. 10 is a tracking track of each target. It can be seen from fig. 9 and 10 that the algorithm can effectively track a uniform linear motion target, a uniform turning maneuver target and a heading angle-changing maneuver target. Fig. 11 is a position root mean square error map of each target, and fig. 12 is an average position error map of each target. Fig. 11 clearly reflects the tracking performance of each target, the tracking effect of the uniform linear motion target is ideal, and the tracking error of the maneuvering target is obviously higher than that of the uniform linear motion target, because the covariance matrix is adaptively adjusted when maneuvering detection is carried out on the target, the target is tracked again in a short time by fuzzy gaussian particle filtering, and the tracking error is then reduced slowly. Fig. 12 can reflect the performance of the entire multi-target tracking system, clearly showing the effectiveness of the data correlation algorithm presented herein.
Referring to fig. 13, an embodiment of the present application provides a passive positioning system including a processor, a memory, and a network interface connected by a system bus, wherein the memory may include a non-volatile storage medium and an internal memory.
The non-volatile storage medium may store an operating system and a computer program. The computer program comprises program instructions that, when executed, cause a processor to perform any of a number of video data processing methods.
The processor is used to provide computing and control capabilities to support the operation of the entire computer device.
The internal memory provides an environment for the execution of a computer program in a non-volatile storage medium that, when executed by a processor, causes the processor to perform any one of a number of fixed single-station multi-target data association tracking methods.
The network interface is used for network communication such as transmitting assigned tasks and the like. It will be appreciated by those skilled in the art that the structure shown in FIG. 13 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
It should be appreciated that the Processor may be a central processing unit (Central Processing Unit, CPU), it may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), field-Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, the computer program comprises program instructions, and the processor executes the program instructions to realize the multi-target data association tracking method of any fixed single station provided by the embodiment of the application.
The computer readable storage medium may be an internal storage unit of the computer device according to the foregoing embodiment, for example, a hard disk or a memory of the computer device. The computer readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD), etc. that are provided on the computer device.
While the application has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the application. Therefore, the protection scope of the application is subject to the protection scope of the claims.
Claims (3)
1. A method for multi-target data association tracking for a fixed single station, comprising:
Acquiring a first actual observation value set of at least two target objects at a target moment;
determining the association center value of each target object according to the target time period of the target moment, wherein the association center value comprises the following steps:
When the target time period indicates that at least two target objects are in a track initial stage, determining a gray prediction value of each target object at the target moment based on a gray prediction model; determining the grey predicted value as the association center value;
when the target time period indicates that at least two target objects are in a track stabilization stage, determining an observation predicted value of each target object at the target moment based on a track prediction model; determining the observed predicted value as the association center value;
Determining a second set of actual observations of each of the target objects from the first set of actual observations according to the correlation center values based on a joint maximum likelihood estimation model, comprising:
Determining a relevant wave gate of each target object according to the grey predicted value; determining a candidate observation value set of each target object from the first actual observation value set according to the correlation wave gate; determining the second set of actual observations from the set of candidate observations and the gray predictions based on the joint maximum likelihood estimation model, comprising: calculating the difference value between the gray predicted value and each candidate observation value in the candidate observation value set; based on an information entropy calculation model, taking the difference value as input to obtain the expansion uncertainty of each candidate observation value; determining the second set of actual observations from the extended uncertainty of each effective observation based on the joint maximum likelihood estimation model;
determining a relevant wave gate of each target object according to the observation predicted value; determining a candidate observation value set of each target object from the first actual observation value set according to the correlation wave gate; determining the second set of actual observations from the set of candidate observations and the observation predictions based on the joint maximum likelihood estimation model, comprising: calculating a difference between the observed predicted value and each candidate observed value in the candidate observed value set; based on an information entropy calculation model, taking the difference value as input to obtain the expansion uncertainty of each candidate observation value; determining the second set of actual observations from the extended uncertainty of each effective observation based on the joint maximum likelihood estimation model;
Said determining a set of candidate observations for each of said target objects from said first set of actual observations according to said correlation gate comprises:
Determining a measurement index predicted value set of each target object at the target moment, wherein the measurement index predicted value set comprises at least one of an azimuth angle predicted value set, a Doppler frequency predicted value set, an azimuth angle change rate predicted value set and a Doppler frequency change rate predicted value set; determining a gate constraint corresponding to the relevant gate according to the measurement index predicted value set and the measurement index actual value set of each target object at the moment before the target moment; determining the candidate observation set from the first actual observation set according to the wave gate constraint;
And determining motion state information of each target object at the target moment according to the second actual observation value set based on a single-target tracking model, wherein the motion state information is used for determining a track of each target object in the target time period.
2. A passive positioning system comprising a memory, a processor, a program stored on the memory and executable on the processor, and a data bus for enabling a connection communication between the processor and the memory, the program when executed by the processor implementing the steps of the fixed single-station multi-target data association tracking method of claim 1.
3. A storage medium for computer readable storage, wherein the storage medium stores one or more programs executable by one or more processors to implement the steps of the fixed single-station multi-target data association tracking method of claim 1.
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