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Particle filtering is a statistical signal processing methodology that has recently gained popularity in solving several problems in signal processing and communications. Particle filters (PFs) have been shown to outperform traditional filters in important practical scenarios. However their computational complexity and lack of dedicated hardware for real-time processing have adversely affected their use in real-time applications. In this paper, we present generic architectures for the implementation of the most commonly used PF, namely, the sampling importance resampling filter (SIRF). These provide a generic framework for the hardware realization of the SIRF applied to any model. The proposed architectures significantly reduce the memory requirement of the filter in hardware as compared to a straightforward implementation based on the traditional algorithm. We propose two architectures each based on a …