A Parallel Systematic Resampling Algorithm for High-Speed Particle Filters in Embedded Systems

In this paper, we propose a parallel systematic resampling (PSR) algorithm for particle filters, which is a new form of systematic resampling (SR). The PSR algorithm makes iterations independent, thus allowing the resampling algorithm to perform loop iterations in parallel. A fixed-point version of the PSR algorithm is also proposed, with a modification to ensure that a correct number of particles is generated. Experiments show that the fixed-point implementation of the PSR algorithm can use as few as 22 bits for representing the weights, when processing 512 particles, while achieving results equivalent to a floating-point SR implementation. Four customized instructions were designed to accelerate the proposed PSR algorithm in Application-Specific Instruction-set Processors. These four custom instructions, when configured to support four weight inputs in parallel, lead to a 73.7 × speedup over a floating-point SR implementation on a general-purpose processor at a cost of 47.3 K additional gates.