Research On Track-Before-Detect Algorithm For Multiple Amplitude Fluctuation Targets In The Random Finite Set Framework

The detection and tracking of weak targets is a hot research topic in target tracking.Traditional radar target tracking uses a high detection threshold to maintain the track.Due to the relative motion between the radar and the target,the radar cross section(RCS)of the target will fluctuate with the change of view angle at a certain time,and the target’s echo amplitude may not exceed the radar detection threshold,which will lead to the loss of target information.In order to solve the above problems,one of the methods is to use the Track-Before-Detect(TBD)algorithm without threshold processing,to jointly process multi-frame data,and achieve target detection and tracking through multi-frame energy accumulation.It is worth noting that most target tracking under radar systems usually only consider the amplitude information of the target and discard the phase information when processing radar measurement data.Loss of phase information results in a decrease in filter sensitivity.Based on the Random Finite Set(RFS)theory,this paper studies the detection and tracking of weak targets with multiple fluctuations on the basis of considering the phase information.The main structural arrangements are as follows:1.The multi-target tracking problem under the random finite set framework is introduced,several types of random finite set are briefly summarized,and two classes of the likelihood ratio functions under the three types of amplitude fluctuation Swerling 0,1,3 models are introduced.2.Aiming at the detection and tracking of weak radar targets,the Swerling 0,1,3 three types of fluctuating target models are studied,and TBD algorithm of weak radar targets with amplitude fluctuations based on probability hypothesis density(PHD)filter is proposed.We establish two tracking models of complex likelihood ratio(CLR)and amplitude likelihood ratio(ALR)under the PHD-TBD algorithm.As results of making better use of the original target information,the CLR approach makes up for the shortcomings of the ALR.In order to solve the problem of newborn targets with unknown prior distribution information,we propose the adaptive target birth algorithm based on measurement likelihood ratio.The simulation results show that,in the case of target amplitude fluctuation,the CLR method is superior to the ALR in the estimation performance of target position and number,and the calculation efficiency is higher.At low signal-to-noise ratio(SNR),complex likelihood can still detect and track unknown number of weak targets effectively.3.This chapter addresses the detection and tracking of multiple fluctuating targets for TBD algorithm based on Multi-Bernoulli(MB-TBD)filter in surveillance radar systems.MB-TBD usually considers target amplitude information and ignores the fact that radar measurements are complex valued.We first propose to utilize phase information to improve the discrimination of targets from noise.More precisely,CLR is used instead of ALR for fluctuations of types Swerling 0,1,3.Secondly,the traditional MB-TBD filter cannot solve the problem of coexistence between targets with stronger amplitude and weaker amplitude when multiple fluctuating targets are moving.To address this limitation,an adaptive birth distribution based on joint successive target cancellation and measurement likelihood ratio driven is proposed.Moreover,in order to reduce computational complexity,the Bernoulli components of the same targets are merged after the MB-TBD updating.Finally,the proposed algorithm is implemented using Sequential Monte Carlo(SMC)technology.The simulation results show that in challenging scenarios,the performance of the improved algorithm is better than the traditional algorithm,and it has a good application prospect.4.For the problem of the detection and tracking about multiple maneuvering fluctuating weak targets in radar systems,a track-before-detect algorithm based on interactive multi-model(IMM)MB filtering(IMM-MB-TBD)is proposed.Considering the complexity and the tracking performance,the IMM method is the most effective among the known multi-model methods,and has now become the mainstream algorithm for solving maneuvering target tracking.at the same time,in a TBD algorithm,the ALR is usually utilized to match the possible trajectories of targets.In this method,however,only the amplitude information is considered and the phase information is ignored,which leads to a decrease in filter performance.We use the CLR instead of the ALR in the IMM-MB-TBD and preserve the spatial coherence of the phase to realize the detection and tracking about multiple amplitude fluctuation targets with the Swerling 0,1,and 3 fluctuations.Furthermore,in order to accommodate to the situation where the prior information of target births is unknown and overcome the difficulty of detecting a weaker target and a stronger target at the same time when the targets fluctuate,an adaptive birth algorithm based on measurement likelihood ratio is proposed.Simulation results show that at low SNR,the proposed IMM-MB-TBD algorithm provides better performance in estimating of the state and number of targets.5.The detection and tracking of two types of multi-fluctuation targets in linear phased array antenna active mono-pulse radar systems are studied,and a new TBD algorithm based on tag multi-Bernoulli filtering is proposed.Firstly,in order to improve the ability to distinguish between target and noise,a CLR function based on phase information is introduced into the tracking-before-detection algorithm of label multi-Bernoulli filtering.Secondly,a new adaptive new label dynamic grouping method is proposed to solve the problem of strong the problem of coexistence of weak-amplitude targets and the unknown prior information of targets.Finally,a method of estimating the likelihood function is deduced to solve the problem that the likelihood ratio function fails when the SNR is unknown.The simulation experiment verifies that the proposed method can well solve the detection and tracking problem of unknown time-varying multi-fluctuation targets,and for the scene with unknown SNR,the detection and tracking performance of the proposed method is comparable to that of the known SNR.