Easy-hardware-implementation MMPF for maneuvering target tracking

摘要

In this paper, we present an easy-hardware-implementation multiple model particle filter (MMPF) for maneuvering target tracking. In this proposed filter, the sampling importance resampling (SIR) filter is extended to multiple models that consist of two models, namely a constant velocity (CV) model and a “current” statistical (CS) model, and the Independent Metropolis Hasting (IMH) sampler is utilized for the resampling step in each model. Compared with the standard MMPF, the proposed MMPF requires no knowledge of models and model transition probabilities for different maneuvering motions, and keeps a constant number of particles per model at all times. This allows a regular pipelined hardware structure and can be implemented in hardware easily. Furthermore, using the IMH sampler for the resampling step avoids the bottleneck introduced by the traditional systematic resampler and reduces the latency of the whole implementations. Simulation results indicate that the proposed filter shows approximately equal tracking performance with the standard MMPF.