PARRALELIZATION OF NON-LINEAR & NON-GAUSSIAN BAYESIAN STATE ESTIMATORS (PARTICLE FILTERS)

Particle filter has been proven to be a very effective method for identifying targets in non-linear and non-Gaussian environment. However, particle filter is computationally intensive and may not achieve the real time requirements. So, it's desirable to implement it on parallel platforms by exploiting parallel and pipelining architecture to achieve its real time requirements. In this work, an efficient implementation of particle filter in both FPGA and CPU is proposed. Particle filter has also been implemented using MATLAB Parallel Computing Toolbox (PCT). Experimental results show that FPGA and GPU architectures can significantly outperform an equivalent sequential implementation. The results also show that FPGA implementation provides better performance than the GPU implementation. The achieved execution time on dual core and quad core Dell PC using PCT were higher than FPGAs and GPUs as was expected.