Risk-sensitive formulation of unscented kalman filter

A novel method for non-linear risk-sensitive estimation based on the unscented transform has been developed. The proposed filter, referred to as risk-sensitive unscented Kalman filter (RSUKF), and would be able to overcome inherent disadvantages associated with the earlier reported extended risk-sensitive filter (ERSF). The theory and formulation of RSUKF has been presented and possible variants thereof indicated. Using two well-known non-linear examples, the superiority of RSUKF performance has been demonstrated. As the RSUKF has similar computational efficiency and better robustness as compared with the ERSF, the former may be more suitable for onboard implementation, quick exploration of risk-sensitive filtering for nonlinear problems and also for generating proposal densities for more computation intensive risk-sensitive particle filters.