An application of Sequential Monte Carlo samplers: An alternative to particle filters for non-linear non-Gaussian sequential inference with zero process noise
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Particle filters are not applicable in sequential parameter estimation scenarios, ie scenarios involving zero process noise. Sequential Monte Carlo (SMC) samplers provide an alternative sequential Monte-carlo approximation to particle filters that can address this issue. This paper aims to provide a description of SMC samplers that is accessible to an engineering audience and illustrate the utility of SMC samplers through their application to a specific problem. The problem involves processing a stream of bearings-only measurements to perform localisation of a stationary tar get. The SMC sampler solution is shown to outperform an Extended and Unscented Kalman filter in nonlinear scenarios (as defined by a novel metric for nonlinearity that this paper describes). The SMC sampler offers a computational cost that is near-constant over time on average. Future work aims to investigate the utility of Approximate Bayesian Computation and apply the technique within a Simultaneous Localisation and Mapping context. (8 pages)