针对单传感纯角度被动跟踪系统的不完全可观问题，结合变分置信下界最大化提出一种基于Fisher信息累积的传感器航迹自适应估计算法。首先，建立以传感器航向角、目标状态估计及其估计误差协方差为参数的变分贝叶斯迭代联合优化框架；在此基础上，推导给出变分置信下界关于航向角的Fisher信息矩阵，并将其最大化以获得传感器航向角的最优解，从而实现传感器平台轨迹自适应优化；同时，考虑纯角度跟踪的鲁棒性，结合正则化方法以相邻迭代变分分布之间的加权Kullback-Leibler散度为惩罚项，构建以状态估计及其估计误差协方差为超参数的优化函数；继而，通过线性化方法给出优化函数的二阶近似，并计算其关于超参数的偏导数，获得状态估计及其估计误差协方差的迭代更新解析表达式；最后，通过被动传感器纯角度跟踪系统仿真实验，验证所提算法能够通过改变传感器航向角有效提高信息的累积量，实现较高精度的目标跟踪。

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.