Nonlinear estimation using risk sensitive formulation of cubature quadrature Kalman filter

This paper proposes a novel method to minimize the risk sensitive cost function based on cubature quadrature algorithm. The proposed filter is named as risk sensitive cubature quadrature Kalman filter (RSCQKF). The theory and formulation of the RSCQKF have been presented in this paper. The performance of proposed risk sensitive filter is compared with its risk neutral counterpart for a ballistic target tracking problem. The simulation results show that for wrongly modeled process noise parameters, the RSCQKF outperforms the cubature quadrature Kalman filter (CQKF).