Risk sensitive unscented particle filter for bearing and frequency tracking

Abstract

Robust filter based on risk sensitive estimator is derived to estimate the state of the uncertain models, while the estimation error involves two terms, the first term coincides with the minimum value of the risk sensitive cost function, the second one is the distance between the true and design probability models. The proposed algorithm, which introduces risk sensitive estimator into the unscented particle filter, could automatically change the state noise covariance according to the magnitude of the risk function. As a result, sample impoverishment could be mitigated. In the simulation of submarine bearing and frequency tracking, the performance of the new algorithm is compared with the unscented kalman filter and the unscented particle filter. Simulation results show that the new algorithm performs better than the two others. © 2010 Springer-Verlag Berlin Heidelberg.