复杂环境下探测器高精度自主导航算法研究

为提高探测器在复杂深空环境中自主导航精度，提出基于二阶中心差分法和PCA模型改进的粒子滤波算法。首先通过二阶中心差分滤波方法获取最优的重要性密度函数，采用对称比例的采样算法从中进行样本点采样；然后通过引入主成分分析法对采集的样本集进行预处理，引入比例缩放因子，对粒子集合进行粒子重采样。通过仿真实验，此方法能够在很大程度上改进粒子滤波算法中出现的粒子退化的问题，使跟踪系统平均误差达到0.429，提高算法的跟踪精度和稳定性，实现探测器复杂环境下高精度自主导航。

Research on high precision autonomous navigation algorithm of detector in complex environment

In order to improve the autonomous navigation accuracy of the detector in complex deep space environment, an improved particle filter algorithm based on second-order central difference method and PCA model is proposed. Firstly, the optimal importance density function is obtained by the second-order central difference filtering method, and the sample points are sampled by the symmetrical proportional sampling algorithm; Then, the principal component analysis method is introduced to preprocess the collected sample set, and the scaling factor is introduced to resample the particle set. Through simulation experiments, this method can greatly improve the particle degradation problem in the particle filter algorithm, make the average error of the tracking system reach 0.429, improve the tracking accuracy and stability of the algorithm, and realize high-precision autonomous navigation in the complex environment of the detector.