More and more data fusion models contain state constraints with valuable information in the filtering process. In this study, an optimal filter of risk-sensitive with quasi-equality constraints is formulated using the reference probability method. Through recursion processes of probability density acquired from the probability measure change, the derived algorithm is optimal in the sense of the risk-sensitive parameter. The system and constraint models are consistent in statistics. Simulation results show that it is more robust and efficient than projection filters for the worst-case of noises and model uncertainty. More and more data fusion models contain state constraints with valuable information in the filtering process. In this study, an optimal filter of risk-sensitive with quasi-equality constraints is formulated using the reference probability method. Through recursion processes of probability density acquired from the probability measure change, the derived algorithm is optimal in the sense of the risk-sensitive parameter. The system and constraint models are consistent in statistics. Simulation results show that it is more robust and efficient than projection filters for the worst-case of noises and model uncertainty.