Research On TBD Technology Of Multi-Radar Multi-Target Particle Filter Based On Weight Selection

Early warning systems mostly use radar networking systems to jointly detect and track multiple targets.When one or several radars have poor detection results,direct fusion may reduce the overall detection effect,leading to the loss of targets or the discovery of a large number of false targets.Far worse than the detection effect of a single radar.Particle filter-based tracking before detection algorithm(PF-TBD)is a commonly used target detection and tracking algorithm.When using PF-TBD for multi-radar and multi-target joint detection and tracking,it can effectively use the measurement information of multiple radars.Improve the detection effect of the target,and improve the false alarm and missed detection of the target.In PF-TBD,the calculation of particle weights is based on radar measurement information on targets.Particle weights can reflect the quality of radar measurement information on targets to a certain extent.Therefore,the selection and use of radar measurement and The fusion process can be transformed into a computational fusion process of particle weights.Based on the above analysis,this paper starts from the research on the calculation process and method of particle weight in PF-TBD.Based on the two-layer particle filter structure,this paper proposes a variety of particle weight calculation methods to realize the selection and utilization of multiple radar measurements.And through the fusion of particle weights to achieve the fusion of multi-radar measurement information,so effectively use multiple radar measurements to improve the correct probability of target detection,improve target false alarm and missed detection.The main research work of this paper is as follows:1.The basic principle and system model of particle filter track before detect algorithm are introduced,and the implementation process of multi-radar single-target PF-TBD algorithm is expounded.2.A multi-radar multi-target particle filter tracking algorithm before detection(PWSPF-TBD)based on particle weight selection is proposed to solve the radar selection problem in the multi-radar multi-target particle filter algorithm and improve the target false alarm situation.This algorithm proposes a new method for calculatingparticle weights.First,the target weight sequence corresponding to each radar measurement is calculated,and the radar joint detection performance parameters are obtained.Then,for each particle,the particle particles corresponding to each radar measurement are calculated.Weights,and adjust particle sub-weights based on radar joint detection performance,reduce the sub-weights corresponding to radars with poor detection capabilities,thereby reducing their impact on the fused particle weights,and finally reduce the particle sub-weights Fusion is performed to obtain particle weights,so that multiple radar measurements are fused.The simulation results show that compared with the traditional multi-radar multi-target pre-detection tracking algorithm,the algorithm reduces the number of false targets,reduces the effect of the less effective radar,and realizes the selective use of multiple radars.This algorithm can also be applied to the multi-radar single-target particle filter pre-detection tracking algorithm.3.A multi-radar multi-target particle filter tracking algorithm before detection(RWSPF-TBD)based on radar weight selection is proposed to solve the problem of missing target detection in multi-radar multi-target particle filter algorithm under low signal-to-noise ratio environment.In the particle weight calculation process,for each radar,the algorithm calculates the particle sub-weights and corresponding weight correction coefficients of all particles obtained by the radar,and then uses the weight correction coefficients to modify the particle sub-weights.Expand the difference between the weights of particles near the target's grid and particles far from the target's grid,and increase the probability that the particles near the target will be resampled.In addition,the algorithm uses a particle swarm resampling method for target tracking particle swarm resampling to improve the diversity of particle swarm distribution.Simulation results show that compared with the PWSPF-TBD algorithm,the algorithm improves the target miss detection situation and achieves the effective use of different radar measurement information in a low signal-to-noise ratio environment.This algorithm can also be applied to the single radar multi-target particle filter tracking algorithm before detection.4.A hybrid multi-radar multi-target particle filter tracking algorithm before detection(HMPF-TBD)is proposed,which combines the characteristics of PWSPF-TBD and RWSPF-TBD algorithms to improve the multi-radar multi-target particle filter algorithm in low signal-to-noise ratio Target false alarms and missingtargets in.This algorithm organically fuses the two algorithm structures.When calculating particle weights,the particle weight selection method is first used to adjust multiple particle sub-weights for each particle,and then the radar weight selection method is used for each For each radar,the sub-weights of all particles corresponding to each radar are modified to organically fuse the two algorithms.Simulation results show that compared with the PWSPF-TBD algorithm and the RWSPF-TBD algorithm,the hybrid algorithm combines the advantages of the two algorithms,and at a low signal-to-noise ratio,the detection system improves the detection of weak targets at multiple distances Correct rate.