Optimisation of particle filters using simultaneous perturbation stochastic approximation
The paper addresses the optimisation of particle filtering methods aka sequential Monte Carlo (SMC) methods using stochastic approximation. First, the SMC algorithm is parameterised smoothly by a parameter. Second, optimisation of an average cost function is performed using simultaneous perturbation stochastic approximation (SPSA). Simulations demonstrate the efficiency of our algorithm.