Research On Particlew Filtering In Target Tracking

Highly non-linear and non-Gaussian estimation problems are ubiquitous in target tracking, and particle filter (PF) is an effective tool for such problems. In this dissertation, a key technique in PF and several tracking problems are studied in the framework of PF. The main contributions are as follows:In order to handle the impoverishment caused by resampling a non-Gaussian posterior distribution in PF, a random vector generation algorithm is proposed based on the idea of local approximation, in which new non-repeated random vectors are generated by random linear combination of given samples in each region, and the selections of key parameters are discussed. Since no prior information and assumptions about the underlying distribution are required, it is suitable for simulating pseudo-random vectors from a non-Gaussian distribution, and its validity is verified in the simulation.The performance and implementation of radar tracking by exploiting correlativity of radar sampling cells based on raw data are studied. Based on the correlativity model that is built according to the expansion of beam and signal in space and delay-Doppler domain, tracking performance is derived and analyzed, and the PF implementation is introduced. Furthermore, two amplitude-sampling-free implementations for the case of unknown amplitude are also proposed. The simulation results show that radar tracking by exploiting correlativity of sampling cells outperforms the traditional tracking based on abstracted target parameters, and the tracking scheme can be implemented by PF effectively.A multiple model PF with separated model variable is proposed in the research of maneuvering target track-before-detection (TBD). By separating the kinematic state and model variable, the model-conditioned target state and model probability are estimated independently, and thus the constraint between model probability and particle number of individual model is eliminated, which leads to the flexibility of efficiently assigning adequate particles for each model, and prevents degeneracy when model transforms. The simulation results show that the algorithm improves the performance of detection, model estimation, and tracking precision evidently when SNR is low. Multiple target tracking based on random set is studied and its PF implementation is improved:1) According to the analysis to the principle of target number estimation of probability hypothesis density (PHD) filter and related factors, an improved target number estimation and state estimation algorithm for particle PHD filter is proposed. In this algorithm, the space is first divided according to resolvability, and then the detection state is judged according to the local target number estimation, and further processing is taken based on the decision. The algorithm improves the performance of target number estimation and state estimation when detection probability is low.2) A generic of PHD filter with non-standard measurement model is proposed in the preliminary research on multitarget TBD, and the difficulties of this problem and possible solution are discussed.