Particle Filter Centralized Tracking Method for Multistatic Radar Out-of-Sequence Measurement Fusion

Abstract

The invention discloses a particle filter centralized tracking method for multi-base radar out-of-sequence measurement fusion, and belongs to the technical field of multi-base radar system data fusion. The invention sequentially uses the out-of-sequence measurement to update all the states between the time when the out-of-sequence measurement is generated and the latest sequential filtering time, and uses particle filtering to realize target tracking, which overcomes the problems caused by different data processing times of radar sites and communication link delays. The radar measurements are out of order, which leads to the deterioration of target fusion tracking performance. The present invention judges whether the current measurement is a sequential measurement, if it is a sequential measurement update, otherwise it performs out-of-sequence measurement fusion, which effectively solves the reasons of different data preprocessing times of each radar site and communication link delay in a multi-base radar system. For the problem of multiple out-of-sequence measurements of arbitrary timing, a universal solution is proposed for the problem of out-of-sequence measurement of multiple arbitrary time sequences, which has higher fusion accuracy than directly ignoring out-of-sequence measurements.

Description

technical field [0001] The invention belongs to the technical field of multistatic radar system data fusion, in particular to a particle filter centralized tracking method for multistatic radar out-of-sequence measurement fusion. Background technique [0002] As the modern environment becomes more and more complex, the available resources such as structural space and power of monostatic radar are limited, and the detection capability is greatly limited. Multistatic radar has the functions of anti-stealth, anti-radiation missile, anti-jamming and anti-low altitude penetration Features, can be well adapted to the modern battlefield environment. In recent years, multistatic radar systems have been widely used in many fields such as sea, land, and air, and have received great attention in many aspects such as detection, identification, tracking, and imaging. In particular, the data fusion of radar receivers at different locations can effectively improve the performance of targe...

AI Technical Summary

Problems solved by technology

[0003] However, in the multistatic radar system, the radar sites are far apart and work independently. In the centralized fusion processing, due to the different data preprocessing time of each receiver and the delay of the communication link, etc., when the measurements of the radar sites reach the fusion center The out-of-sequence phenomenon is serious, and these out-of-sequence measurements frequently arrive at the fusion center in arbitrary timing, that is, multiple out-of-sequence measurements or consecutive arrivals, or multiple out-of-sequence measurements cross-arrival, or out-of-sequence measurements and sequential measurements cross-arrival

Since there are often a large number of out-of-sequence measurements in multistatic radar systems, if these out-of-order measurements are directly ignored, the information loss will be serious, resulting in a serious deterioration of the target fusion tracking performance, so it is necessary to find a specific method to deal with multistatic radar systems The out-of-order measurement problem in

An ideal approach would be to reorder and refilter all measurements whenever an out-of-sequence measurement arrives at the fusion center. However, this approach requires caching of historical measurements, which is difficult in a multistatic radar system with limited memory capacity. of

In the prior art, an optimal update method is proposed based on the linear minimum mean square error estimation method, which effectively solves the fusion problem of multiple out-of-sequence measurement updates at any time sequence, but this method is based on the Kalman filter algorithm and is only applicable to For linear Gaussian systems, the tracking performance of this method is severely degraded in multistatic radar systems; in addition, a storage-effective Gaussian approximation fusion method is proposed for multiple out-of-order measurements, which effectively solves the problem of multiple out-of-order measurements in nonlinear systems. However, this method only uses a single mean and covariance matrix to approximate the probability density function of the target state. When the target state of the multistatic radar system is multi-mode distribution, the tracking accuracy of this method is poor.

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