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.