Architectures for particle filtering

There are many applications in which particle filters outperform traditional signal processing algorithms. Some of these applications include tracking, joint detection and estimation in wireless communication, and computer vision. However, particle filters are not used in practice for these applications mainly because they cannot satisfy real-time requirements. This chapter discusses several important issues in designing an efficient resampling architecture for high throughput parallel particle filtering. The resampling algorithm is developed in order to compensate for possible error caused by finite precision quantization in the resampling step. Communication between the processing elements after resampling is identified as an implementation bottleneck, and therefore, concurrent buffering is incorporated in order to speed up communication of particles among processing elements. The mechanism utilizes a particle-tagging scheme during quantization to compensate possible loss of replicated particles due to the finite precision effect. Particle tagging divides replicated particles into two groups for systematic redistribution of particles to eliminate particle localization in parallel processing. The mechanism utilizes an efficient interconnect topology for guaranteeing complete redistribution of particles even in case of potentialweight unbalance among processing elements. The architecture supports high throughput and ensures that the overall parallel particle filtering execution time scales with the number of processing elements employed.