Efficient Uniform Quantization Likelihood Evaluation for Particle Filters in Embedded Implementations

In this paper, we propose a uniform quantization likelihood evaluation (UQLE) algorithm for particle filters (PFs). This algorithm simplifies the exact likelihood evaluation (ELE) algorithm, the most computationally demanding function in PFs, by using a uniform quantization scheme to generate approximated weights. Simulation results indicate that PFs using UQLE can achieve comparable or better accuracy than PFs using ELE. The software implementation of UQLE for the bearing-only tracking (BOT) model in fixed-point arithmetic with 32 quantized intervals achieves 39.5× average speedup over the software implementation of ELE. An Application-specific Instruction-set Processor instruction was designed to accelerate the UQLE algorithm in a hardware implementation. The custom instruction implementation of UQLE for the BOT model with 32 intervals achieves 23.0× average speedup over the software implementation on a general-purpose processor with 5 % additional gates.

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