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Multi-target tracking method for passive sensor based on particle filtering

A passive sensor and multi-target tracking technology, applied in the field of target tracking and systems, can solve the problems of limited number of samples, tracking divergence, loss of sample diversity, etc.

Inactive Publication Date: 2011-02-16
XIDIAN UNIV
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Problems solved by technology

However, in practical applications, considering the comprehensive requirements of tracking accuracy and real-time performance, the number of samples is usually limited, and the phenomenon of sample depletion will occur in the process of sampling and resampling, which makes the samples lose diversity and the state estimation is unstable. lead to tracking divergence

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  • Multi-target tracking method for passive sensor based on particle filtering
  • Multi-target tracking method for passive sensor based on particle filtering
  • Multi-target tracking method for passive sensor based on particle filtering

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Embodiment Construction

[0040] 1. Introduction to basic theory

[0041] 1. System equations

[0042] In the Cartesian coordinate system, the system state takes the position and velocity in the x and y directions, and the following nonlinear dynamic system model can be established:

[0043] x t + 1 i = Fx t i + Gv t i - - - 1 )

[0044] y t = h ( x t i ) + e t - - - 2 )

[0045] Among them, i=1, L, c represents the sequence number of the target, c represents the total target number, respectivel...

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Abstract

The invention discloses a multi-target tracking method for a passive sensor based on particle filtering, which belongs to the technical field of guidance and mainly solves the problems of easy divergent tracking and inaccurate target state estimation in the traditional multi-target tracking method. The method optimizes distribution of multi-target samples through particle swarm optimization and sample mixing sampling algorithms and tracks the multi-target combined with a joint probability data association algorithm. The method comprises the following steps of: firstly, optimizing the distribution of multi-target joint samples by utilizing the particle swarm optimization algorithm so that the multi-target joint samples are gathered in a high likelihood region with a bigger probability of occurrence of a real target; secondly, calculating an associated probability between the targets and observation and the posterior probability distribution of the targets by utilizing the samples; and finally, decomposing a joint sample weight into the corresponding target sample in a likelihood way according to each target sample in the re-sampling process, and independently re-sampling each target according to the decomposed weight, and further optimizing the distribution of the target sample so as to improve the precision of target tracking.

Description

technical field [0001] The invention belongs to the technical field of guidance and relates to target tracking. Specifically, it is a passive sensor multi-target tracking method based on particle swarm optimization and sequential Monte Carlo, which can be used in infrared guidance and other systems. Background technique [0002] In multi-target tracking, due to the influence of target miss detection and clutter, there is uncertainty in the correlation between the measurement obtained by the sensor and the target, and the angle information measured under passive conditions is a nonlinear function of the target state, so it is necessary to Accurately estimating the target state to realize target tracking requires solving two problems of measurement and target data association and nonlinear filtering. [0003] Traditional multi-target tracking methods include the nearest neighbor method NN, the joint probability data association JPDA, and the multi-hypothesis tracking MHT algo...

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Application Information

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IPC IPC(8): G01C21/20
Inventor 姬红兵蔡绍晓张俊根
Owner XIDIAN UNIV
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