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Multi-target data association method and device and computer readable storage medium

A data association and multi-target technology, which is applied in the field of devices and computer-readable storage media, and multi-target data association methods, can solve the problems of low correlation accuracy of target data association algorithms, achieve multi-target tracking performance, and improve accuracy Effect

Pending Publication Date: 2021-10-22
SHENZHEN UNIV
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Problems solved by technology

[0004] The main purpose of the embodiments of the present invention is to provide a multi-object data association method, device and computer-readable storage medium, which can at least solve the problem of low association accuracy of the object data association algorithm provided in the related art

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  • Multi-target data association method and device and computer readable storage medium
  • Multi-target data association method and device and computer readable storage medium
  • Multi-target data association method and device and computer readable storage medium

Examples

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no. 1 example

[0051] In order to solve the problem of low association accuracy of the target data association algorithm provided in the related art, this embodiment proposes a multi-object data association method, such as figure 1 Shown is a schematic flow chart of the multi-object data association method provided in this embodiment. The multi-object data association method proposed in this embodiment includes the following steps:

[0052] Step 101 , calculating the characteristics of each observation in the observation set to obtain an observation feature set, and performing intuitionistic fuzzification on the preset target trajectory feature set and the observation feature set to obtain a training set and a test set.

[0053] Specifically, in the data association algorithm based on the multi-objective T-S intuitionistic fuzzy model of this embodiment, for the T-S intuitionistic fuzzy model, the input of the model includes: the observation set O={o 1 ,o 2 ,o 3 ,...,o n}, [t-n,t-1] time ...

no. 2 example

[0162] In order to verify the effectiveness of the proposed algorithm, this embodiment conducts a simulation experiment on radar target tracking in a complex environment. At the same time, it is compared with the standard JPDAF algorithm, Fitzgerald-JPDAF algorithm and the representative MaxEntropy-JPDAF algorithm. The performance indicators for comparison are mainly tracking error, simulation time and tracking stability.

[0163] The experimental object of the simulation in this embodiment is two small-angle intersecting targets. Among them, the initial position coordinate of target 1 track is x 1 = 1km, y 1 =5.3km; the coordinates of the initial position of the target 2 trajectory is x 1 = 1km, y 1 = 2.3km. Both targets move in a straight line at a constant speed, the speed of target 1 in the y direction is -0.1km / s; the speed of target 2 in the y direction is 0.15km / s; the speeds of target 1 and target 2 in the x direction are both 0.3km / s.

[0164] The simulation ti...

no. 3 example

[0176] In order to solve the problem of low association accuracy of the target data association algorithm provided in the related art, this embodiment shows a multi-object data association device. For details, please refer to Figure 4 , the multi-object data association device of this embodiment includes:

[0177] The fuzzy module 401 is used to calculate the characteristics of each observation in the observation set to obtain an observation feature set, and perform intuitionistic fuzzification on the preset target trajectory feature set and the observation feature set to obtain a training set and a test set;

[0178] The identification module 402 is used to identify the antecedent parameters of the training set and identify the subsequent parameters of the training set; wherein, the antecedent parameters include: degree of membership, degree of non-membership, and intuition index;

[0179] The update module 403 is used to update the multi-objective T-S intuitionistic fuzzy m...

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Abstract

The invention discloses a multi-target data association method and device and a computer readable storage medium, and the method comprises the steps: calculating the features of all observations in an observation set to obtain an observation feature set, and carrying out the intuition fuzzification of a preset target track feature set and the observation feature set, and obtaining a training set and a test set; carrying out preceding component parameter and following component parameter identification on the training set; updating the multi-target T-S intuitionistic fuzzy model by adopting the front part parameters and the back part parameters obtained by identification; inputting the test set into the trained multi-target T-S intuitionistic fuzzy model to obtain a target association matrix; and performing multi-target data association based on the target association matrix. Through the implementation of the method, the intuitionistic fuzzy set is introduced to enrich the feature information of the trajectory and the observation point, so that the feature of each sample has three measurement indexes of membership, non-membership and intuitionistic index, the accuracy of multi-target data association is effectively improved, and the multi-target tracking performance in a dense clutter environment is ensured.

Description

technical field [0001] The present invention relates to the technical field of target detection, in particular to a multi-target data association method, device and computer-readable storage medium. Background technique [0002] With the development of radar signal processing technology, the correlation between track and point track data has become the core of radar tracking system. Especially in some complex scenes, such as multiple targets, strong interference, dense clutter and cross tracks, etc., it will bring great difficulties to target classification and association. Therefore, the primary and secondary processing of radar signals is particularly important. The primary function of radar signal processing is to extract useful information in a complex environment. A common practice is to set a certain threshold at the center of the prediction point and filter out clutter beyond the threshold to achieve the purpose of improving the signal-to-noise ratio. ; The secondar...

Claims

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Application Information

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IPC IPC(8): G01S13/66G06N7/02
CPCG01S13/66G06N7/023
Inventor 李良群黄帅
Owner SHENZHEN UNIV
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