Random set theory-based multi-target tracking method

A maneuvering target tracking, multi-target technology, applied in the research field of multi-maneuvering target tracking technology under random set theory, can solve the problems of inconsistency, strong multi-target maneuverability, difficult to apply to complex scenes with high maneuverability, etc.

Active Publication Date: 2016-12-07
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

[0005] The purpose of the present invention is to aim at the defect of background technology, research and design a kind of multi-maneuvering target tracking method based on random set theory, realize the multi-maneuvering target tracking based on generalized label multi-target Bernoulli filter, solve existing generalized label multi-target The Bernoulli filter is difficult to apply to the problem of complex scenes with high mobility of the target
This method has the characteristics of strong robustness, wide adaptability, and high estimation accuracy. It can effectively solve the problem of strong and inconsistent multi-target maneuverability that often occurs in practical applications, and realizes maneuvering multi-target tracking in complex scenes. Estimated target motion model

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Embodiment Construction

[0058] The present invention mainly adopts the method of computer simulation to verify, and all steps and conclusions are verified correctly on MATLAB-R2010b. The specific implementation steps are as follows:

[0059] Step 1. Parametrically characterize the generalized labeled multi-objective Bernoulli distribution:

[0060]

[0061] Among them, π(X) represents the generalized label multi-objective Bernoulli posterior probability distribution, X represents the target state set, and Ξ is the discrete space; represents the set of target tracks, means all A collection of subsets, I is a collection of any number of targets; w (I,ξ) Represents the weight, non-negative and satisfies p (ξ) (·,l) is a probability density function that satisfies ∫p (ξ) (x,l)dx=1. Through this step, with the parameter w (I,ξ) and p (ξ) (·,l) fully characterizes the generalized labeled multi-objective Bernoulli distribution.

[0062] Step 2, multi-objective state space is carried out aug...

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Abstract

The invention discloses a random set theory-based multi-target tracking method. The method includes the following steps that: a multi-target state space is expanded, model dimension is increased based on original kinetic information, and therefore, characterization of target model information can be realized; a state transfer function and a likelihood function are expanded based on a jump Markov system, so that the state transfer function and the likelihood function can contain the model information; and the prediction and update process of an expanded multi-model generalized label multi-target Bernoulli filter is realized, and target states are extracted, and a target motion model is estimated. With the method adopted, problems in maneuvering multi-target tracking can be solved. The method has the advantages of high robustness, high adaptability and high estimation accuracy. With the method adopted, the problem of high multi-target maneuverability and inconsistency in practical application can be solved, and maneuvering multi-target tracking and target motion model estimation under complex scenes can be realized.

Description

technical field [0001] The invention belongs to the technical field of radar, and relates to the research on multi-maneuvering target tracking technology under random set theory. Background technique [0002] Multi-target tracking is one of the research hotspots in the field of radar. Its difficulties mainly focus on: 1) The measurement values ​​received by the radar are not all from the target, but also include clutter, false alarm, interference, etc.; 2) Due to the new Targets appear, old targets disappear, and the number of targets changes over time. [0003] In the past few decades, multi-target tracking mainly uses the traditional tracking method based on data association. The basic idea is to decompose the multi-target tracking problem into several sub-problems, and filter each single target. The measured values ​​are correctly correlated. However, in engineering applications, data association is not an easy task, and it is computationally intensive and error-prone. ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S13/66
CPCG01S13/66
Inventor 易伟姜萌陈方园谌振华王佰录李溯琪孔令讲
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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