Fuzzy track association method based on accumulated historical similarity

Abstract

The invention provides a fuzzy track association method based on accumulated historical similarity, which comprises the following steps: acquiring state estimation of two radars on a target track, and constructing a fuzzy factor set of position estimation, speed estimation and acceleration estimation in x, y and z directions and a weight set of fuzzy factors; obtaining the similarity of the two tracks according to the membership degree of the fuzzy factors, and constructing a similarity matrix; obtaining the historical cumulative similarity of the moment k, and constructing a historical cumulative matrix based on the historical cumulative similarity; and mapping the historical accumulation matrix to the incidence matrix, constructing an optimal two-dimensional distribution model, carrying out optimal distribution calculation on the optimal two-dimensional distribution model through a Hungary algorithm, and outputting an associated track pair.

Description

Technical Field

[0001] The invention relates to a radar target tracking technology, in particular to a fuzzy track association method based on accumulated historical similarity. Background Art

[0002] In multi-radar systems, a distributed radar data fusion architecture is often used. Each radar node processes the measurement data detected by it locally, generates local tracks, and reports these track information to the fusion center. The core task of track association is to determine whether multiple tracks from different nodes originate from the same target. When the position differences between the tracks received by the fusion center are significant and there are no complex interactions such as bifurcation, overlap, splitting or maneuvering, the association problem is relatively simple at this time, and the traditional association algorithm can provide correct association results. However, when faced with complex dynamic scenes, traditional methods often find it difficult to maintain their effectiveness and are prone to problems of mis-association or missed association. The existing track association algorithms mainly include track association algorithms based on statistical mathematics and track association algorithms based on fuzzy mathematics. Traditional track association methods based on statistical mathematics often classify noise, uncertainty and complex dynamic scenes as noise when dealing with them. However, in practical applications, due to radar errors, environmental interference and the unpredictability of target behavior, the observation data often have significant uncertainty and fuzziness, so it is difficult to obtain suitable prior parameters to establish a noise model. The traditional fuzzy track association algorithm only considers the similarity at the current moment, which makes it difficult to distinguish when the targets overlap or intersect. Summary of the invention

[0003] The object of the present invention is to provide a fuzzy track association method based on cumulative historical similarity, comprising:

[0004] Step S100, obtaining the state estimation of the target track by two radars and , construct the fuzzy factor set for position estimation, velocity estimation and acceleration estimation in the x, y, z directions and the weight set of fuzzy factors, where A and B are the indexes of radars, i and j are the target tracks corresponding to radars A and B, respectively. are the fuzzy factors for position estimation, velocity estimation, and acceleration estimation in the x, y, and z directions, respectively, and k represents time;

[0005] Step S200, obtaining the similarity s of the two tracks according to the membership degree of the fuzzy factor ij (k), construct the similarity matrix S(k);

[0006] Step S300, obtaining the historical cumulative similarity at time k , based on historical cumulative similarity Constructing the historical accumulation matrix ;

[0007] Step S400: The historical accumulation matrix Mapping to the incidence matrix , construct the optimal two-dimensional allocation model, perform the optimal allocation calculation on the optimal two-dimensional allocation model through the Hungarian algorithm, and output the associated track pairs.

[0008] Furthermore, the fuzzy factor set in step S100 is the Euclidean distance of the spatial position, velocity and azimuth heading angle of tracks i and j.

[0009] (1)

[0010] in, and They represent the spatial positions of the targets of track i and j at the kth moment, and represents the speed of the target on track i, j at the kth moment.

[0011] Furthermore, the membership function in step S200 is

[0012] (2)

[0013] Among them, l=(1,2,...,n), n is the number of fuzzy factors, the membership function is the normal membership function, u is the expectation, is the variance.

[0014] Furthermore, in step S200, each fuzzy factor is introduced into the membership function to obtain the similarity s between track i and track j at time k. ij (k)

[0015] (3)

[0016] in, represents the lth fuzzy factor.

[0017] Further, in step S200, the n A Tracks and radar B B The similarity between the two tracks is judged, and n A ×n B Similarities are constructed into a similarity matrix S(k), which is expressed as

[0018] (4).

[0019] Furthermore, in step S300, the historical cumulative similarity at time k for

[0020] (5).

[0021] Furthermore, define the historical accumulation matrix

[0022]

[0023] Right now

[0024] (6).

[0025] Furthermore, the correlation matrix in step S400 As shown in formula (7)

[0026] (7)

[0027] Will Mapping to the incidence matrix The mapping criterion is

[0028]

[0029] in is the similarity threshold.

[0030] Furthermore, the optimal two-dimensional allocation model in step S400 is

[0031] .

[0032] On the basis of the fuzzy track association algorithm, the present invention proposes a historical accumulation matrix, accumulates the similarity information of all past moments, thereby forming a comprehensive evaluation mechanism in the dimension of time series, sets a similarity threshold of the historical accumulation matrix, maps the matrix to the association matrix through the threshold, and performs ambiguity judgment on the association matrix. When an ambiguous judgment occurs, the Hungarian algorithm is used to perform ambiguity processing in combination with the historical accumulation matrix, and finally outputs the judgment track association pair. When the targets overlap or intersect, even if the data at individual moments cannot provide enough distinguishing information, the historical accumulation similarity matrix can still provide a reliable judgment basis for the association decision. This method can improve the correct association rate and stability.

[0033] The present invention will be further described below in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of the process of the present invention.

[0035] Figure 2 This is a schematic diagram of the radar detection track intersection scenario.

[0036] Figure 3 Schematic diagram of the correct association rate of each algorithm in the cross-scenario.

[0037] Figure 4 Schematic diagram of the error association rate of each algorithm in the cross-scenario.

[0038] Figure 5 Schematic diagram of the missed association rate of each algorithm in the cross-scenario.

[0039] Figure 6 This is a schematic diagram of the radar detection formation target maneuvering scenario.

[0040] Figure 7 Schematic diagram of the correct association rate of each algorithm in the formation maneuver scenario.

[0041] Figure 8 Schematic diagram of the error association rate of each algorithm in the formation maneuver scenario.

[0042] Fig. 9 Schematic diagram of the missed association rate of each algorithm in the formation maneuver scenario.

[0043] Fig.10 Schematic diagram of radar detection of multiple targets.

[0044] Fig.11 Schematic diagram of the correct association rate of each algorithm in a multi-target scenario.

[0045] Fig.12 Schematic diagram of the error association rate of each algorithm in a multi-target scenario.

[0046] Fig.13 Schematic diagram of the error association rate of each algorithm in a multi-target scenario. DETAILED DESCRIPTION

[0047] Step S100, obtaining the state estimation of the target track by two radars and , construct the fuzzy factor set for position estimation, velocity estimation and acceleration estimation in the x, y, z directions and the weight set of fuzzy factors, where A and B are the indexes of radars, i and j are the target tracks corresponding to radars A and B, respectively. are the fuzzy factors for position estimation, velocity estimation, and acceleration estimation in the x, y, and z directions, respectively, and k represents time;

[0048] Step S200, obtaining the similarity s of the two tracks according to the membership degree of the fuzzy factor ij (k), construct the similarity matrix S(k);

[0049] Step S300, obtaining the historical cumulative similarity at time k , based on historical cumulative similarity Constructing the historical accumulation matrix ;

[0050] Step S400: The historical accumulation matrix Mapping to the incidence matrix , construct the optimal two-dimensional allocation model, perform the optimal allocation calculation on the optimal two-dimensional allocation model through the Hungarian algorithm, and output the associated track pairs.

[0051] In step S100, the fuzzy factor set is the Euclidean distance between the spatial position, velocity and azimuth heading angle of tracks i and j.

[0052] (1)

[0053] in, and They represent the spatial positions of the targets of track i and j at the kth moment, and represents the speed of the target of track i, j at the kth moment. In this embodiment, the weight set of the fuzzy factors is set to {0.55, 0.35, 0.1}.

[0054] The membership function in step S200 is

[0055] (2)

[0056] Among them, l=(1,2,...,n), n is the number of fuzzy factors, the membership function is the normal membership function, u is the expectation, By bringing each fuzzy factor into the membership function, we can get the similarity s between track i and track j at time k: ij (k)

[0057] (3)

[0058] in, represents the lth fuzzy factor.

[0059] In step S200, the n A Tracks and radar B B The similarity between the two tracks is judged, and n A ×n B Similarities are constructed into a similarity matrix S(k), which is expressed as

[0060] (4)

[0061] In step S300, the historical cumulative similarity at time k for

[0062] (5)

[0063] Defining the historical accumulation matrix

[0064]

[0065] Right now

[0066] (6)

[0067] The correlation matrix in step S400 As shown in formula (7)

[0068] (7)

[0069] Will Mapping to the incidence matrix The mapping criterion is

[0070]

[0071] in is the similarity threshold, which is a value that changes over time. It is usually a recursive formula and can be expressed as .

[0072] In this embodiment, the use of the historical accumulation matrix can effectively solve the ambiguity problem. When ambiguity occurs, it can be converted into an optimal two-dimensional allocation problem for processing. The mathematical model is as follows:

[0073]

[0074] The Hungarian algorithm is used to perform optimal allocation calculations to solve the ambiguity problem and finally output the associated track pairs.

[0075] Comparative Example

[0076] Compared with the traditional weighted track association algorithm and sequential track association algorithm, this patent has the highest correct association rate and the best stability in complex scenarios such as target intersection, density, and maneuvering formation flight.

[0077] Since the actual association probability cannot be obtained, the frequency is used to approximate the probability, and E c is the correct association rate, E e is the false association rate, E s is the leakage association rate, N l is the total number of associations, N c is the number of correct associations, N e is the number of incorrect associations, N s is the number of missed associations, and Nl =N c +N e +N s So there is

[0078]

[0079] E c +E e +E s =1. In order to measure the correlation performance of each step, the instantaneous correlation rate at time k is

[0080]

[0081] The average correlation rate is

[0082]

[0083] Use the above mentioned indicators to measure the quality of the algorithm.

[0084] Table 1 Average association rate of 100 Monte Carlo experiments in target crossover scenario

[0085]

[0086] Table 2 Average correlation rate of 100 Monte Carlo tests in formation maneuvering scenario

[0087]

[0088] Table 3 Average association rate of 100 Monte Carlo experiments in multi-target scenarios

[0089]

[0090] pass Figure 2-Figure 13 From the simulation comparison results shown, it can be found that the algorithm proposed in this patent is suitable for complex scenarios such as target intersection, maneuver formation, and multiple targets, and its correct association rate and stability are better than the comparison algorithm.

Claims

1. A fuzzy track association method based on cumulative historical similarity, characterized in that: include: Step S100, obtaining the state estimation of the target track by two radars and , construct the fuzzy factor set for position estimation, velocity estimation and acceleration estimation in the x, y, z directions and the weight set of fuzzy factors, where A and B are the indexes of radars, i and j are the target tracks corresponding to radars A and B, respectively. are the fuzzy factors for position estimation, velocity estimation, and acceleration estimation in the x, y, and z directions, respectively, and k represents time; Step S200, obtaining the similarity s of the two tracks according to the membership degree of the fuzzy factor ij (k), construct the similarity matrix S(k); Step S300, obtaining the historical cumulative similarity at time k , based on historical cumulative similarity Constructing the historical accumulation matrix ; Step S400: The historical accumulation matrix Mapping to the incidence matrix , construct the optimal two-dimensional allocation model, perform the optimal allocation calculation on the optimal two-dimensional allocation model through the Hungarian algorithm, and output the associated track pairs.

2. The method according to claim 1, characterized in that In step S100, the fuzzy factor set is the spatial position, velocity and Euclidean distance of the azimuth heading angle of tracks i and j. （1） in, and They represent the spatial positions of the targets of track i and j at the kth moment, and represents the speed of the target on track i, j at the kth moment.

3. The method according to claim 2, characterized in that The membership function in step S200 is （2） Among them, l=(1,2,...,n), n is the number of fuzzy factors, the membership function is the normal membership function, u is the expectation, is the variance.

4. The method according to claim 3, characterized in that In step S200, each fuzzy factor is introduced into the membership function to obtain the similarity s between track i and track j at time k. ij (k) （3） in, represents the lth fuzzy factor.

5. The method according to claim 4, characterized in that In step S200, the n A Tracks and radar B B The similarity between two tracks is judged, and n A ×n B Similarities are constructed into a similarity matrix S(k), which is expressed as （4）。 6. The method according to claim 5, characterized in that In step S300, the historical cumulative similarity at time k for （5）。 7. The method according to claim 6, characterized in that Defining the historical accumulation matrix , , Right now （6）。 8. The method according to claim 7, characterized in that The correlation matrix in step S400 As shown in formula (7) （7） Will Mapping to the incidence matrix The mapping criterion is , in is the similarity threshold.

9. The method according to claim 8, characterized in that The optimal two-dimensional allocation model in step S400 is 。

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