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Particle filtering is a recursive Bayesian estimation technique that hasgained popularity recently for tracking and localization applications. It usesMonte Carlo simulation and has proven to be a very reliable technique to modelnon-Gaussian and non-linear elements of physical systems. Particle filtersoutperform various other traditional filters like Kalman filters innon-Gaussian and non-linear settings due to their non-analytical andnon-parametric nature. However, a significant drawback of particle filters istheir computational complexity, which inhibits their use in real-timeapplications with conventional CPU or DSP based implementation schemes. Thispaper proposes a modification to the existing particle filter algorithm andpresents a highspeed and dedicated hardware architecture. The architectureincorporates pipelining and parallelization in the design to reduce executiontime considerably. The design is validated for a source localization problemwherein we estimate the position of a source in real-time using the particlefilter algorithm implemented on hardware. The validation setup relies on anUnmanned Ground Vehicle (UGV) with a photodiode housing on top to sense andlocalize a light source. We have prototyped the design using Artix-7field-programmable gate array (FPGA), and resource utilization for the proposedsystem is presented. Further, we show the execution time and estimationaccuracy of the high-speed architecture and observe a significant reduction incomputational time. Our implementation of particle filters on FPGA is scalableand modular, with a low execution time of about 5.62 us for processing 1024particles and can be deployed for real-time applications.

Source localization using particle filtering on FPGA for robotic navigation with imprecise binary measurement