Single Observer Passive Localization Algorithm Based on Iterated Measurement Updating Filter

Single observer passive localization has strong concealment performance, and can avoid the problem of data fusion and synchronization among multiple observing platforms. To address the performance degeneration of single observer passive localization affected by the factors such as measurement error consistency and initial state error, a single observer passive localization algorithm based on iterated measurement updating filter(IMUF) is proposed in this paper. Firstly, based on the theory of linear estimation, the classical one-step discrete linear estimator update is rewritten as the step-by-step updating process in continuous time. Secondly, the evolution equations of continuous stepwise state and its error matrix are deduced and the iterated measurement updating equation is obtained by discretization. And then, the Sigma point method are used to approximate calculate the Gaussian matrix included in updating equations, and the IMUF is obtained, which has the Kalman filter-like computation form and is suitable for single observer passive localization. Finally, compared with the classical method, the experimental results show that the IMUF algorithm can effectively deal with the performance degradation problem under non-uniform measurement error and large initial state error, with better filtering convergence and estimation accuracy.