Unknown clutter passive cooperative positioning method based on sparsity order optimization

A technology of co-location and sparsity, applied in positioning, radio wave measurement systems, measurement devices, etc., can solve the problems of uneven distribution of clutter and affect the performance of target tracking, so as to solve the problem of multi-target tracking and improve multi-target tracking. Effects of tracking performance

Active Publication Date: 2019-06-21
HANGZHOU DIANZI UNIV
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Problems solved by technology

The environment of the PCL system is complex and changeable, especially near the coastline. In the complex environment, the distribution of clutter will no lo...

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  • Unknown clutter passive cooperative positioning method based on sparsity order optimization
  • Unknown clutter passive cooperative positioning method based on sparsity order optimization
  • Unknown clutter passive cooperative positioning method based on sparsity order optimization

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Embodiment Construction

[0017] The present invention is further analyzed below in conjunction with specific embodiment.

[0018] N k unknown and N k The target ≥ 0 appears within the detection range of the sensor, and the position of the receiving station is marked as X Rr =[x Rr ,y Rr ], considering that the external radiation source is an airborne external radiation source, the position of the transmitting station at the kth moment is denoted as X k,T =[x k,Ts ,y k,Ts ]. Consider a single target t(0≤t≤N k ) state X at time k k,t for in is the position of the target t in the Cartesian coordinate system at time k, is the instantaneous velocity of the target t at time k, and the state transition model is as follows:

[0019] x k+1.t =F·X k,t +v k (1)

[0020]

[0021] Where F is the state transition matrix, I 2 represents the second-order identity matrix, is the Kronecker product, the process noise v k ~N(0,Q), σ v is the standard deviation of the process noise, and τ is...

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Abstract

The invention provides an unknown clutter passive cooperative positioning method based on sparsity order optimization. According to the method, a clutter density estimator with optimized sparsity order is embedded into a Gaussian mixture probability hypothesis density filter to estimate the multi-target state and number. Firstly, target measurement is removed through a threshold technology and target state feedback, so that clutter measurement is obtained, secondly, a sample is selected from the clutter measurement, and GA-SVR is used for fitting a sample, an extreme point of a fitting curve is obtained through a gradient method, and the x-coordinate of the extreme point is downwards rounded to obtain the optimal sparsity order n. According to the method, the passive cooperative positioning multi-target tracking performance under unknown clutters can be effectively improved, and the problem of multi-target tracking 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 sparsity order optimization. Background technique [0002] The passive co-location (Passive Coherent Location, PCL) system is a system composed of a receiving station and an external radiation source signal. Usually, a TV or broadcast signal is used as the external radiation source signal, and the direct wave of the external radiation source is received after being reflected by the target. The target can be located after coherent processing of the reflected wave. Because the system itself does not emit electromagnetic energy, it has better concealment, and at the same time, PCL has the advantages of effectively dealing with stealth targets and low cost. Applying the traditional data association filter to the PCL system has the characteristics of high complexity. The probability hypothesis density filter...

Claims

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Application Information

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IPC IPC(8): G01S5/02
Inventor 郭云飞钱恒泽陈志坤薛梦凡
Owner HANGZHOU DIANZI UNIV
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