Target Tracking Before Detection Method Based on Gaussian Particle Potential Probability Assumption Density Filtering

Abstract

The invention discloses a method for tracking before target detection based on Gaussian particle potential probability hypothesis density filtering. The main ideas are as follows: determine that there are Nk targets at k time, record the motion state of the p-th target at k time as The likelihood function corresponding to the motion state xk of Nk targets contained in the radar observation area under the condition of the motion state of a target; calculate the state mean value estimation of Nk targets and the covariance estimation of Nk targets respectively at k time; Sequentially calculate the probability that the number of targets at time k is Nk and the estimated value of the number of targets Nk contained in the radar observation area under the Cartesian coordinate system. Add 1 to k, so as to obtain the targets contained in the radar observation area under the Cartesian coordinate system at time 1 From the estimated value of the number N1 to the estimated value of the target number ND contained in the radar observation area under the Cartesian coordinate system at time D.

Description

technical field [0001] The invention belongs to the technical field of radar target tracking, in particular to a target tracking method before detection based on Gaussian particle potential probability hypothesis density filtering, that is, a target tracking method before detection based on Gaussian particle potential probability hypothesis density (cardinalized probability hypothesis density, CPHD) filtering , which is suitable for weak and small target tracking in the case of low signal-to-noise ratio or large amount of data processing. Background technique [0002] Due to the urgent needs of radar anti-stealth and long-range early warning and other fields, weak target detection has gradually become a current research hotspot; the current detection of weak targets mainly uses Track Before Detection (TBD) technology, which does not set a threshold for each scan. Energy accumulation is performed on multi-frame scanning data to detect and track weak targets; the implementatio...

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Problems solved by technology

The above two algorithms are implemented by Monte Carlo, and the particle support set is large in a complex environment, resulting in high complexity

Jayesh H.K, Petar M.D. proposed Gaussian particle filter (Gaussian particle filter, GPF) in Gaussian particle filtering [J].IEEE Trans.on Aerospace and Electronic Systems, 2001:429-432 published by Jayesh H.K, Petar M.D. The posterior distribution of the unknown state variable Approximate to a Gaussian function, iteratively store the mean and covariance of the target state, which can significantly reduce the complexity of the operation, but the improvement in the estimation of the target number is limited

[0005] At present, the problems and challenges faced in the detection and tracking of an unknown number of weak targets are as follows: on the one hand, the target state needs to be obtained by computationally complex clustering methods, and the tracking accuracy decreases when the signal is weak; on the other hand, due to the particle filter There are problems such as particle degradation and sampling exhaustion. Although the particle support set can be expanded to solve it, it is at the cost of sacrificing time, which leads to a large limitation in practical applications, and the estimation accuracy of the target number is not ideal under low signal-to-noise ratio.

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