CN115220002A - 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 fixed single-station multi-target data association tracking method, which is used for more accurately carrying out data association on observed 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 the correlation center value of each target object according to the target time period of the target time; determining a second actual observation value set of each target object from the first actual observation value set according to the association center value based on the joint maximum likelihood estimation model; and based on the single-target tracking model, determining the motion state information of each target object at the target moment according to the second actual observation value set, 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 a passive positioning system, due to the presence of environmental noise and random disturbance, multiple observations can be obtained by one detection, and of the observations, it is not known which are from tracked targets and which are false observations; when a plurality of target objects need to be observed, some observation values far away from the predicted value of the motion state of the target object are not determined to belong to which target object, so that the observation values need to be associated with the target objects, which is called data association.
A data association method includes the steps of predicting the motion state of a target object at the next moment according to an observation value at the previous moment, comparing the obtained predicted value with an actual value, calculating the data with small difference through maximum joint likelihood estimation, and determining the association 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 at this time, if the predicted value of the track measurement is correlated with the real measurement, a 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, and aims to improve the accuracy of data association in multi-target tracking.
In a first aspect, an embodiment of the present application provides a multi-target data association tracking method for 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 the correlation center value of each target object according to the target time period of the target time; determining a second set of actual observations of each of the target objects from the first set of actual observations based on a joint maximum likelihood estimation model according to the associated center value; 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 the track of each target object in the target time period.
In a second aspect, embodiments of the present application provide a passive positioning system, which includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for implementing connection communication between the processor and the memory, wherein the program, when executed by the processor, implements the steps of the foregoing method.
In a third aspect, embodiments of the present application provide a storage medium for a computer-readable storage, the storage medium storing one or more programs, which are executable by one or more processors to implement the steps of the foregoing method.
According to the joint maximum likelihood data association method and the related device based on the information entropy, the association center value in the target time period is determined according to the target time period in which at least two target objects are located at the target time, the motion state information of each target object at the target time is determined according to the second actual observation value set of each target object, and further tracking of each target object is performed, so that the second actual observation value set of each target object can be determined from the first actual observation value sets of at least two target objects by using different association center values according to different characteristics of the flight path in different stages in the flight path, and the data association can be performed on the observation values of the 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 needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic architecture diagram of a fixed single-station passive positioning multi-target tracking method according to an embodiment of the present disclosure;
FIG. 2 is a schematic flowchart illustrating a step of a multi-target data association tracking method for a fixed single station according to an embodiment of the present application;
fig. 3 is a schematic architecture diagram of a fixed single-station multi-target data association method according to an embodiment of the present application;
fig. 4 is a schematic flowchart illustrating another step of a multi-target data association tracking method for a fixed single station according to an embodiment of the present application;
fig. 5 is a schematic flowchart illustrating another step of a multi-target 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 the multi-target data association tracking method for a fixed single station according to the embodiment of the present application;
FIG. 7 is a flowchart illustrating another step of a multi-target 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-target data association tracking method for a fixed single station according to an embodiment of the present application;
fig. 9 is a real trajectory diagram in an application scenario of the multi-target data association tracking method for a fixed single station according to the embodiment of the present application;
FIG. 10 is a real and estimated trajectory diagram in an application scenario of a fixed single-station multi-target data association tracking method according to an embodiment of the present application;
fig. 11 is a root mean square error graph of one eight targets in an application scenario of the fixed single-station multi-target data association tracking method according to the embodiment of the present application;
fig. 12 is an eight-target average position error diagram in an application scenario of the multi-target data association tracking method for a fixed single station according to the 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 of 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, is acquired by using a certain method. And the data processing can further process the point track and the track of the target, predict the position of the target at a future moment and form a reliable target track, thereby realizing the real-time tracking of the target.
The radar data processing comprises several main links such as point track condensation, track initiation, target tracking, multi-target association and the like. Two basic problems studied by the method are the correlation problem of point trace to point trace and point trace to flight trace under different environments. The former relates to track initiation, emphasizes on control of relevant range of point track and selection of 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 target's track using the target's information provided by the radar and to give the target's position at the next time.
The radar data processing process mainly comprises data preprocessing, track starting, data association, tracking filtering, track extinction, quality evaluation and the like.
The input data processed by the radar data is also called observation, the observation is not data obtained by direct scanning of the radar, but the data scanned by the radar is firstly processed by a radar signal and then is obtained 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, pitch) for each target. In practical engineering, the observation is generally contaminated with noise, mainly from several aspects:
1) Random false alarms exist during the scanning process;
2) Clutter generated by false targets;
3) An interfering target;
4) Baits, and the like.
Although modern radar signal processing technology is greatly developed, interference still exists in observation after signal processing, and generally, the observation data quantity is large, and the requirement on the aspect of subsequent computer storage and processing is high. The data preprocessing is to screen data before other data processing processes such as starting and correlation are carried out on the observed data, remove the data which are not within the threshold, and reserve the data which pass through all the decision thresholds.
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 reduced to a certain degree, the data processing speed and the target tracking precision are improved, and 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 processes of track initiation, data association and the like. A wave gate is a region that is generally divided into an initial wave gate and a correlation wave gate.
The initial gate is typically used in the initial phase of the track and is an area centered at any point that defines a spatial range over which an observation of the target may occur. Since the target is far away at the beginning of the track, 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 target, and defines a range in which an observed value of the tracked target may appear. The shape and size of the correlation waveform are determined on the one hand by making the true observation in the correlation wave gate have a high probability, and on the other hand, by not allowing too many independent observation points in the correlation wave gate. The size of the relevant gates should generally be matched to the type of target, e.g. a fixed target gate generally depends only on the accuracy of the observation, a straight target gate depends on the accuracy of the observation and the prediction filter, and a maneuvering target gate takes into account acceleration factors, etc. The commonly used related gates include rectangular gates, circular gates, elliptical gates, and sector gates in a polar coordinate system.
Track initiation refers to the first point in establishing the target's track, i.e., the process from the target falling within the radar detection range to the track establishment for that target. The track starting process is an important link in the radar data processing process. In other words, "good start is half of success", and conversely, if the track start is unsuccessful, the track is not established smoothly, and it is very likely that a reliable track cannot be established, so that the target cannot be tracked correctly.
The track starting process is one of important links in the radar data processing process, one of the track starting tasks is to quickly establish a track for a target entering a power area of a radar, and the other task is to avoid establishing a false track by false points as far as possible. However, in order to avoid the establishment of the false track, the start of the false track needs to wait for a long time, and a certain contradiction exists between the two tasks, namely the contradiction between the speed and the quality, so that an optimal compromise needs to be found between the two tasks. The track initiation algorithm is many, and the common algorithms include an intuitive method, a logic method, a modified 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. However, the environment is not ideal in a practical system. Due to the existence of factors such as observation noise and the like, phenomena such as false alarm and the like can occur, and in addition, random interference existing in an observed area can cause clutter to occur in the area where the target can appear. In summary, multiple observations may result from one detection, and of these observations, it is not known which are from the tracked target and which are spurious observations. This factor determines that the data correlation process is an important part of the radar data processing system.
When only one target exists in the radar scanning area and interference does not exist, only one point trace exists in a related wave gate of the target, and the problem of data association does not exist at the moment. However, when multiple targets are present 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 at this time, a data association problem is involved. The data association is to judge the relationship between the radar observation data at a certain moment and the observation data at other moments or the existing tracks, thereby realizing the process of pairing the trace points and the tracks.
Generally, data association can be classified into the following cases according to the difference of the inter-associated objects:
1) Starting a flight path: interconnecting the trace points with the trace points;
2) And (3) track updating: the interconnection of the trace points and the track (track prediction points), which can also be called track maintenance;
3) And (3) track fusion: and interconnecting the flight path and the flight path.
The data association method is various and can be roughly divided into two types, one type is a Bayesian data association algorithm, and the other type is a maximum likelihood data association algorithm. The Bayesian algorithm mainly comprises a nearest field algorithm, a probability data association algorithm and the like, and is based on a Bayesian criterion. The maximum likelihood algorithm mainly comprises an integral fork method, a combined maximum likelihood algorithm and the like, and the maximum likelihood algorithm is based on the likelihood ratio of an observation sequence.
The embodiment of the application provides a fixed single-station multi-target data association tracking method and a related device, which can determine a second actual observation value set of each target object from first actual observation value sets of at least two target objects by using different association center values at different stages of a flight path according to different characteristics of the flight path at different stages, thereby more accurately performing data association on the observation values of the multi-target objects.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort belong to the protection scope of the present application.
The flowcharts shown in the figures are illustrative only and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
It is to be understood that the terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification of the present application 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 this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments and features of the embodiments described below can be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a fixed single-station passive positioning multi-target tracking method according to an embodiment of the present disclosure.
The passive positioning system acquires a set of actual observation values of a plurality of target objects at different moments, wherein the actual observation values may include azimuth angle information, azimuth angle change rate information, doppler frequency change rate information and the like. Each actual observation value in the actual observation value sets of the multiple target objects does not necessarily have a one-to-one correspondence with the target object, and due to the existence of random disturbance and environmental noise, even if the correspondence between the actual observation value at the initial time and each target object is determined, the correspondence between the actual observation value at the subsequent time and each target object cannot be determined.
Through data association, the actual observed values of multiple targets can be associated with each target object, and the actual observed value corresponding to each target object at the target moment is determined.
When the data association is completed, after the actual observed value of each target object at the target moment is determined, the multi-target tracking problem is converted into a single-target tracking problem, maneuvering judgment needs to be carried out on each target object, and after tracking gate rules and filtering and prediction are utilized, the track start and end of each target object are determined.
After the track start and the track end of each target object are determined, the plurality of target objects can be comprehensively analyzed, and finally the state of a system formed by the plurality of target objects is determined.
Based on the architectural 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 combined with maximum likelihood data association.
Referring to fig. 2, a schematic flowchart of a step of a method for tracking multiple target data associated with a fixed single station according to an embodiment of the present application is provided.
201. A first set of actual observations of at least two target objects at a target time is obtained.
An observation station in a passive positioning system observes a motion system containing at least two target objects, and a first actual observation set of the motion system containing at least two target objects at a target time is obtained by analyzing electromagnetic wave signals which are generated by the motion system containing at least two target objects and received by the observation station, wherein the first actual observation set represents an actual observation set corresponding to the motion system containing at least two target objects at the target time, and a corresponding relation between each target object in the motion system and each actual observation value in the first actual observation set is not accurately established.
202. And determining the association center of each target object according to the target time period of the target time.
The positioning and tracking of the motion system including at least two target objects are realized by associating the actual observation value in the first actual observation value set representing the actual observation value of the motion system as a whole with each target object, that is, determining the actual observation value of each target object. The association center is a basis for establishing association between each actual observation value in the first actual observation value set and each target object. The association center is not fixed, but the selection of the association center of each target object is dynamically adjusted according to the target time period in which the target object is located when the target object moves at the target time.
203. Based on the joint maximum likelihood estimation model, a second set of actual observations for each target object is determined from the first set of actual observations from the associated center values.
After determining an association center value of a target time according to a target time period of the target time of a motion system containing at least two target objects, based on a joint maximum likelihood estimation model, taking the association center value as a basis for establishing association between each actual observation value in a first actual observation value set and each target object, and determining a second actual observation value set of each target object from the first actual observation value set, wherein each actual observation value in the second actual observation value set has a corresponding relation with each target object.
204. And based on the single-target tracking model, determining the motion state information of each target object at the target moment according to the second actual observation value set, wherein the motion state information is used for determining the track of each target object in the target time period.
After the incidence relation between each actual observation value in the first actual observation 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 containing at least two target objects is converted into the determination of the motion state of each target object in the motion system. Based on a single-target tracking model, 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, and determining the track of each target object in the target time period according to the motion state information of each target object at multiple moments, thereby realizing the positioning and tracking of each target object.
Based on the fixed single-station multi-target data association tracking method shown in fig. 2, there are various ways to determine the association center of each target object according to the target time period at which the target time is located in step 202, and it is possible to improve accuracy by determining different association centers for data association in different stages according to the motion characteristics and observation value characteristics of a motion system including at least two target objects in different stages, and it is described below that different association centers are selected in different stages.
Referring to fig. 3, fig. 3 is a schematic diagram illustrating a multi-target data association method for a fixed single station.
The data association is the core of a multi-target tracking scheme and can convert a multi-target tracking problem into single-target tracking, and the data association in the multi-target tracking comprises three steps of association preprocessing, association center selection, and information entropy uncertainty calculation and association.
Firstly, preprocessing measurement information, arranging a tracking wave gate, enabling all observations received by an observation station to pass through the wave gate, filtering out the observations with overlarge errors by using an associated wave gate, and taking the observations passing through the associated wave gate as candidate observations.
And then selecting a correlation center, wherein the initial positioning error of the fixed single station is overlarge, so that the observed gray predicted value is used as the correlation center at the initial stage of the track, and the observed predicted value is used as the correlation center after the target is stably tracked.
After the association centers are determined, the observation and association centers of all targets are matched by an association method based on uncertainty of information entropy to construct association pairs, each association pair represents that each target has an observation and a pair thereof, and therefore the multi-target tracking problem is converted into the 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 initial positioning, maneuvering judgment and filtering of the target.
At the initial time of the flight path, 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 a starting point, and the tracking of each target flight path at the later time is estimated by utilizing observation information and a filtering algorithm.
And before the target state is updated by using the observation and filtering algorithm, a target maneuvering judgment is made. When the maneuvering target is detected, the covariance is adaptively adjusted and then filtered, so that the maneuvering target is tracked.
And finally, performing state updating estimation on the target in the associated pair by using a filtering algorithm and the observation of the associated pair.
With reference to the embodiments shown in fig. 2 and fig. 3, different association centers are used at different stages, and the track starting stage in the embodiment shown in fig. 3 is described based on the embodiment shown in fig. 2.
Referring to fig. 4, fig. 4 is a flowchart illustrating another step of the multi-target data association tracking method for a fixed single station at the track start stage, wherein step 202 in fig. 4 includes step 2021 and step 2022.
2021. And when the target time period indicates that at least two target objects are in the track starting stage, determining a gray prediction value of each target object at the target moment based on the gray prediction model.
When the target time periods of the current target time of at least two target objects represent the track starting stage, the previous n times of measurement which are successfully correlated are used as prediction preparation data, and then a grey prediction model is used for obtaining a group of prediction data. The grey prediction model is a mathematical model that can be built using only a small amount of incomplete information and then used to predict the predicted value for the next step. Currently, there are many typical gray prediction methods, such as regression analysis.
Firstly, accumulating the data of previous time to form a new sequence, and setting the original sequence as
z (0) ＝(z (0) (0),z (0) (1),…z (0) (k)) (1)
The first accumulation yields the representation as:
GM (1.1), one of the grey prediction systems, is a first order differential equation model of the form:
the prediction formula is as follows:
defined by the derivative:
when Δ t takes 1, so:
the solving formula for obtaining the predicted value by arranging the formula (4) and the formula (6) is as follows:
the corresponding predicted value at the moment of obtaining k +1 is:
2022. and determining the grey predicted value as the associated central value.
And after a grey prediction value is obtained based on the grey prediction model, the grey prediction value is used as a correlation center value of the target object at the target moment in the track starting stage.
Based on the embodiment shown in fig. 4, the actual observed value corresponding to each object is determined according to the associated central value, and specifically, the relevant gates are used for screening from the first actual observed value set.
Referring to fig. 5, fig. 5 is a schematic flow chart illustrating another step of the multi-target data association tracking method for a 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 a single target should pass through a relevant wave gate, the grey predicted value of each target object at the target time is taken as the wave gate center of each target object, and the relevant wave gate of each target object at the target time can be determined after the wave gate shape is determined.
It should be noted that the selection of the wave gate shape may be various shapes, such as an elliptic wave gate or a square wave gate, and in this embodiment, the fixed single-station multi-target tracking scheme selects an elliptic wave gate as the correlation wave gate.
2032. A set of candidate observations for each target object is determined from the first set of actual observations according to the correlation gates.
The correlation wave gate can limit the number of observations participating in correlation discrimination and filter out the observations with excessive errors. There is a correlation gate for each target object, and a candidate observation set for each target object can be filtered from the first actual observation set according to the correlation gate for each target object.
2033. And determining a second actual observed value set according to the candidate observed value set and the gray predicted value based on the joint maximum likelihood estimation model.
And after the candidate observation value set of each target object is determined, determining a second actual observation value set of each target object according to the candidate observation value set and the gray predicted value of each target object based on the joint maximum likelihood estimation model.
In connection with the embodiment shown in fig. 5, the determination of the second set of actual observations of each target object from the set of candidate observations of each target object in the embodiment of the present application is 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.
Setting a matrix formed by m targets and n measurement indexes of the system as follows:
in the formula，λ ij Represents the jth measurement indicator for the jth target.
And (3) making a difference between the measurement value and the measurement predicted value in the matrix A, then taking an absolute value, and recording the absolute value as:
in the formula,. DELTA. ij The difference between the jth measurement indicator representing the target i and its corresponding predicted value, i.e.
20332. And obtaining the expansion uncertainty of each candidate observation value by taking the difference value as input based on the information entropy calculation model.
Entropy is a measure of the degree of disorder of a system, assuming that the system contains a number of measurement indices j (j =1,2, \8230;, n), with a proportion p of the individual measurement index of each target i (i =1,2, \8230;, m) ij Then, the entropy of the measurement indicator is defined as:
when in use
When the measured values are the same, the maximum entropy can be expressed as:
H＝log 2 m j max (13)
therefore, if the entropy of information of a measurement index is smaller, the amount of information provided by the measurement index is larger.
The ratio of the jth measurement index of target i is:
the extended uncertainty of the l-th measurement index is defined as:
if s is larger, the effect of the measurement index in the target tracking process is larger.
20333. Based on the joint maximum likelihood estimation model, a second set of actual observations is determined from the extended uncertainty of each valid observation.
Assume that state estimates for n targets are obtained at time k-1
k is at time m k And (3) when each measurement passes the correlation threshold, recording the set of all confirmed measurements at the k-recording time as:
the event that the jth measurement in the set of k-time confirmed measurements belongs to the target i is recorded as
Dividing each of the plurality of wave gate internal measurements into a plurality of feasible divisions
Some division thereof. The measurements within the feasible partition r should satisfy the following requirements:
and is
For each feasible partition r, an event can be defined
θ (r) = { division of the measurement set r is true } (20)
The set of all feasible partitions can be defined as:
Γ＝{r} (21)
the uncertainty of the information entropy is used as a certain measurement to divide the measurement distance between the first index and the first index of the measurement prediction value.
The result of the uncertainty of the information entropy appears in a plurality of numbers, and the modulus of the result represents the measurement distance between the first index of a certain measurement division and the index corresponding to the measurement predicted value, namely
The improved information entropy uncertainty better reflects the difference between the measurement division certain index and the measurement prediction. The smaller the uncertainty is, the closer the first index of the measurement division is to the corresponding index of the measurement prediction value. Therefore, the uncertainty of the information entropy of all the indexes of a certain measurement division is combined, and the minimum uncertainty value of the combination of the information entropy is the maximum possible measurement division, namely
Based on the embodiments shown in fig. 2 to fig. 6, the candidate observation value set of each target object is determined from the first actual observation value set according to the relevant gates, and the candidate observation value set can also be screened from the first actual observation value set in a manner of universally adding the gate constraint.
Referring to fig. 7, step 2032 in fig. 2 and 4 to 6 includes step 20321 to step 20323.
20321. And determining a set of measurement index prediction values of each target object at the target moment.
And determining a measurement index prediction value set of each target object at the target moment, wherein the measurement index prediction value set comprises at least one of an azimuth angle prediction value set, a Doppler frequency prediction value set, an azimuth angle change rate prediction value set and a Doppler frequency change rate prediction value set.
20322. And determining the corresponding wave gate constraint of the relevant wave gate according to the at least two measurement index prediction value sets and the measurement index actual value set of each target object at the previous moment of the target moment.
At least two measurement indicator prediction value sets are determined, and an azimuth prediction value set and a Doppler frequency prediction value set are selected in the embodiment.
On the basis of taking the elliptic wave gate as a related wave gate, the wave gate constraint of two measurement indexes of azimuth angle and Doppler frequency is increased.
Recording the correlated measured azimuth angle of each target at the k-1 moment
Doppler frequency of
Measured azimuth prediction at time k is
Measure Doppler frequency estimate as
The constrained range of azimuth angles is then:
wherein beta is limit Is the azimuthal constraint magnitude.
The constrained range of doppler frequencies is:
wherein f is limit The magnitude is constrained for the doppler frequency.
20323. A set of candidate observations is determined from the first set of actual observations according to a gate constraint.
Observations that fall within the elliptic wave gate and within the range of the azimuthal constraint and the doppler frequency constraint are valid measurements for the target. If no observation falls into the wave gate, the state of the point is updated by the predicted value of the observation, and if no observation falls into the flight path for a plurality of times, the flight path disappearance is recorded. A set of candidate observations for each target object is determined from the first set of actual observations according to a gate constraint.
With reference to the embodiments shown in fig. 2 and 3, different association centers are used at different stages, and the track stabilization stage in the embodiment shown in fig. 3 is described based on the embodiment shown in fig. 2.
Referring to fig. 8, fig. 8 is a flowchart illustrating another step of the multi-target data association tracking method for a fixed single station in the track stabilization phase, wherein 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 when the target time of at least two target objects is in a target time period representing a track stabilization stage, determining the observation predicted value of each target object at the target time based on the track prediction model. The observation predicted value is obtained by taking the observation actual value as input and based on a flight path prediction model.
2024. And determining the observation predicted value as the correlation center value.
And after the observation predicted value is determined, when the target time period is in the aircraft stabilization stage, adopting the observation predicted value as an association center.
It should be noted that the technical solutions of the embodiments shown in fig. 5 to fig. 7 can be applied to the technical solution of the aircraft stabilization phase shown in the embodiment shown in fig. 8, that is, the technical solutions shown in fig. 8 and fig. 5, fig. 6, and fig. 7 can be combined to form a technical solution similar to the embodiment shown in fig. 5 and fig. 6, and details are not 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 trajectory diagram in an application scenario of the fixed single-station multi-target data association tracking method according to the embodiment of the present application.
Under a two-dimensional scene, the observation station is positioned at the origin of coordinates, the target is 200-300km away from the observation station, and the target radiation source does uniform motion with acceleration disturbance. The total time length of the target movement is 300s. The target radiation source frequency is 10GHz, and the target state is as follows:
the observed quantity comprises an azimuth angle alpha and an azimuth angle change rate
Doppler frequency f, doppler frequency rate of change
The standard deviation of the observed noise is respectively: sigma α ＝2°、
σ f ＝1Hz、
And (4) simulating various motion scenes with an observation period T =1s, and recording the influence of the various scenes on the performance index. Simulation scene: 8 targets carry out uniform-speed movement with turning maneuver, and the 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 a two-dimensional plane, the target motion interval is in a first quadrant and a second quadrant, targets 1 to 5 do uniform linear motion, and target 6 does uniform linear motion with direction adjustment, and maneuvering occurs when the uniform linear motion is 100s and 200s respectively; 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 shows the actual track of each target, and fig. 10 shows the track of each target. It can be seen from fig. 9 and 10 that the algorithm can effectively track both the uniform linear motion target, the uniform turning maneuvering target and the maneuvering target with changed course angle. Fig. 11 is a root mean square error map of the position 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 significantly higher than that of the uniform linear motion target, because the maneuvering target is detected during maneuvering detection, the covariance matrix is adaptively adjusted, the fuzzy gaussian particle filter tracks the target again in a short time, and then the tracking error gradually decreases. Fig. 12 can reflect the performance of the whole multi-target tracking system, and clearly shows the effectiveness of the data association algorithm proposed herein.
Referring to fig. 13, an embodiment of the present application provides a passive positioning system, which includes a processor, a memory and a network interface connected by a system bus, where the memory may include a nonvolatile storage medium and an internal memory.
The non-volatile storage medium may store an operating system and a computer program. The computer program includes program instructions that, when executed, cause a processor to perform any one of the methods of processing video data.
The processor is used for providing calculation and control capability and supporting the operation of the whole computer equipment.
The internal memory provides an environment for running a computer program in the nonvolatile storage medium, and the computer program can enable the processor to execute any one of the fixed single-station multi-target data association tracking methods when being executed by the processor.
The network interface is used for network communication, such as sending assigned tasks and the like. Those skilled in the art will appreciate that the architecture shown in fig. 13 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
It should be understood that the Processor may be a Central Processing Unit (CPU), and the Processor may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The embodiment of the application further provides a computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, the computer program comprises program instructions, and the processor executes the program instructions to realize the multi-target data association tracking method for any one 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 described in 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 Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the computer device.
While the invention has been described with reference to specific embodiments, the scope of the invention is not limited thereto, and those skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the invention. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A multi-target data association tracking method for a fixed single station is characterized by comprising the following steps:
acquiring a first actual observation value set of at least two target objects at a target moment;
determining the correlation center value of each target object according to the target time period of the target moment;
determining a second set of actual observed values for each of the target objects from the first set of actual observed values according to the associated 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 the track of each target object in the target time period.
2. The fixed single-station multi-target data association tracking method according to claim 1, wherein the determining of the association center value of each target object according to the target time period of the target time comprises:
when the target time period represents that at least two target objects are in a track starting stage, determining a grey prediction value of each target object at the target moment based on a grey prediction model;
and determining the grey predicted value as the associated central value.
3. The fixed single-station multi-target data association tracking method according to claim 2, wherein determining a second set of actual observed values for each of the target objects from the first set of actual observed values according to the association center value comprises:
determining a relevant wave gate of each target object according to the grey predicted value;
determining a set of candidate observations for each said target object from said first set of actual observations according to said associated gate;
determining the second set of actual observations from the set of candidate observations and the gray predictors based on the joint maximum likelihood estimation model.
4. The fixed single-station multi-target data association tracking method according to claim 3, wherein the determining the second actual observation set according to the candidate observation set and the gray prediction value based on the joint maximum likelihood estimation model comprises:
calculating a 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, obtaining the expansion uncertainty of each candidate observation value by taking the difference value as input;
determining the second set of actual observations from the extended uncertainty of each valid observation based on the joint maximum likelihood estimation model.
5. The fixed single-station multi-target data association tracking method according to claim 1, wherein the determining of the association center value of each target object according to the target time period at which the target time is located comprises:
when the target time period represents 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;
and determining the observation predicted value as the correlation center value.
6. The fixed single-station multi-target data association tracking method according to claim 5, wherein determining a second set of actual observed values for each of the target objects from the first set of actual observed values according to the association center value comprises:
determining a relevant wave gate of each target object according to the observation predicted value;
determining a set of candidate observations for each of the target objects from the first set of actual observations according to the associated gate;
determining the second set of actual observations from the set of candidate observations and the observation predictors based on the joint maximum likelihood estimation model.
7. The fixed single-station multi-target data association tracking method according to claim 6, wherein the determining the second actual observation set according to the candidate observation set and the observation predicted value based on the joint maximum likelihood estimation model comprises:
calculating a difference value between the observation 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 valid observation based on the joint maximum likelihood estimation model.
8. The fixed single-station multi-target data association tracking method according to claim 3, 4, 6 or 7, wherein the determining the set of candidate observation values of each target object from the first set of actual observation values according to the relevant gates comprises:
determining a set of measurement indicator predictors for each of the target objects at the target time, the set of measurement indicator predictors including at least one of a set of azimuth predictor values, a set of doppler frequency predictor values, a set of azimuth change rate predictor values, and a set of doppler frequency change rate predictor values;
determining the corresponding gate constraint of the relevant gate according to the measurement index prediction value set and the measurement index actual value set of each target object at the previous moment of the target moment;
determining the set of candidate observations from the first set of actual observations according to the gate constraint.
9. A passive positioning system, characterized in that it comprises a memory, a processor, a program stored on said memory and executable on said processor, and a data bus for implementing a connection communication between said processor and said memory, said program, when executed by said processor, implementing the steps of the method for multi-target data association tracking of a fixed single station according to any one of claims 1 to 8.
10. A storage medium for computer readable storage, wherein the storage medium stores one or more programs which are executable by one or more processors to implement the steps of the method for multi-target data association tracking of a fixed single station according to any one of claims 1 to 8.
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