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Unknown clutter passive cooperation positioning method based on sliding window accumulation density estimation

A cumulative density and collaborative positioning technology, applied in positioning, radio wave measurement systems, and measurement devices, can solve problems such as uneven spatial distribution of clutter, uneven clutter density, high false track rate, etc. Target tracking performance, solving multiple target tracking challenges, and improving robustness

Active Publication Date: 2017-08-29
河北凯通信息技术服务有限公司
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AI Technical Summary

Problems solved by technology

However, multi-target tracking usually assumes that the spatial distribution of the clutter background is uniform and known a priori
In the PCL system for ground or sea target tracking applications, due to the complex background environment, the clutter density is usually uneven and unknown
On the other hand, due to the variability and unpredictability of the waves emitted by the external radiation source, the PCL system will generate a small range of strong direct wave interference within the radar receiving range, thus making the spatial distribution of clutter more uneven
When the preset clutter distribution model in the filter is quite different from the actual situation, it will lead to a high false track rate or a long track initial time

Method used

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  • Unknown clutter passive cooperation positioning method based on sliding window accumulation density estimation
  • Unknown clutter passive cooperation positioning method based on sliding window accumulation density estimation
  • Unknown clutter passive cooperation positioning method based on sliding window accumulation density estimation

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Embodiment Construction

[0020] The present invention is further analyzed below in conjunction with specific examples.

[0021] Considering that at time k there are N k A flying target in the air moves in a straight line with approximately uniform speed in the detection area of ​​the bi-base station radar, where N k is a non-negative unknown variable. The state of the t-th target is denoted as [x k,t ,y k,t ]and represent the position and velocity of the target in the x and y directions respectively, and record the state set of all targets as The target state transition model is:

[0022]

[0023] Where F is the state transition matrix, T is the sampling interval, I 2 is the second-order identity matrix, Represents the Kronecker product. v k-1 ~N(0,Q) is the process noise, N(m,P) represents the Gaussian function with mean m and covariance P. is the process noise covariance, where σ v is the standard deviation of the process noise.

[0024] Assuming that the measurement of the bi-b...

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Abstract

The invention provides an unknown clutter passive cooperation positioning method based on sliding window accumulation density estimation. In the method, a Gaussian mixing probability hypothesis filter is used to estimate a multi-target number and states. The method is characterized by firstly, introducing a clutter space sparsity estimator based on linear measurement into a passive cooperation positioning system under non-linear measurement; and secondly, during a clutter density estimation process, introducing multi-frame measurement, and using feedback of a threshold technology and posterior strength Gaussian mixture to reject potential target measurement in measurement data so as to reduce an influence of real target measurement on clutter density estimation. In the invention, multi-target tracking performance of an unknown clutter passive cooperation positioning system can be effectively increased and a multi-target tracking problem under an unknown clutter is solved.

Description

technical field [0001] The invention belongs to the field of target detection and tracking, and relates to an unknown clutter passive cooperative positioning method based on sliding window cumulative density estimation. Background technique [0002] Passive Coherent Location (PCL) systems use external radiation source signals (such as TV or radio signals) as transmitting signals for target tracking. Its low cost, high concealment and other advantages make the system a research hotspot in the field of early warning and detection in recent years. Many effective multi-target tracking methods can be applied to multi-target PCL systems, such as particle filter and maximum likelihood. The probability hypothesis density filter based on random finite set realizes the simultaneous estimation of target number and state without complex data association, and has gained wide attention. However, multi-target tracking usually assumes that the clutter background is spatially uniform and k...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S5/00
CPCG01S5/0009
Inventor 郭云飞潘金星李勇薛梦凡陈志坤
Owner 河北凯通信息技术服务有限公司
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