Belief Condensation Filter for Navigation in Harsh Environments

Traditional techniques for navigation such as the Kalman filter cannot capture the nonlinear and non-Gaussian models appearing in wireless localization systems deployed in harsh environments. Nonparametric filters as particle filters can cope with such models at the expense of a computational complexity beyond the reach of low-cost navigation devices. In this paper, we establish a general framework for parametric filters based on belief condensation (BC), which can express highly nonlinear and non-Gaussian system and measurement models. Our methodology exploits the specific structure of the problem and decomposes it in such a way that the linear and Gaussian part can be solved efficiently. The set of parameters for the posterior distribution is updated by an optimization process, referred to as BC. The simulation results show that the performance of the proposed parametric filter is close to that of the particle filter, but with a much lower complexity.