A resampling method for parallel particle filter architectures

Particle filters are able to represent multi-modal beliefs but require a large number of particles in order to do so. The particle filter consists of three sequential steps: the sampling, the importance factor, and the resampling step. Each step processes every particle in oder to acquire the final state estimation. A high number of particles leads to a high processing time, thus reducing the particle filters usefulness for real-time embedded systems. Through parallelization, the processing time can be significantly reduced. However, the resampling step is not easily parallelizable since it requires the importance factor of each particle. In this work, a resampling scheme is proposed which uses virtual particles to solve the parallelization problem of the resampling component. Besides evaluating its performance against the multinomial resampling scheme, it is also implemented on a Xilinx Zynq-7000 FPGA.