基于 Fisher 信息的传感器航迹自适应滤波算法

To address the problem of incomplete observability of the bearing-only tracking system with single sensor，an optimal motion trajectory of the sensor is presented under the condition of maximizing variational evidence lower bound to accumulate more information from measurement. Firstly，assuming that sensor’s speed is a constant，a joint optimization framework with the super-parameters of sensor’s heading，state estimation and its estimation error covariance is established by using variational Bayesian inference on this basis，the Fisher information matrix with respect to sensor’s heading is derived by maximizing the evidence lower bound，so that the optimal heading can be computed at the point of Fisher information accumulation maximization. Meanwhile，considering the robustness of estimated system，a weighted Kullback-Leibler divergence between variational distribution and the posteriori is taken as a regularization to constructed an optimization function，in which state estimation and the associated error covariance are set as variational super-parameters. Furthermore，the partial derivatives of optimization function with respect to state estimation and the associated error covariance are presented to compute estimation update. Simulations of bearing-only tracking system are presented showing that the proposed algorithm can accumulate information from measurement effectively and achieve a higher estimation performance of accuracy by changing sensor’s heading.