Navigability Analysis of Local Gravity Map With Projection Pursuit-Based Selection Method by Using Gravitation Field Algorithm

The positioning accuracy of the gravity-aided navigation is closely related to the selection of local gravity maps. In this paper, the local gravity maps are converted into 8-bit gray images. The navigability features of the local gravity maps are extracted by using the image texture feature analysis methods, such as the gray histogram complexity, the Sobel operator, and the gray level co-occurrence matrix, and the navigability comprehensive evaluation of each local gravity map is obtained by using the projection pursuit model. According to the nebula model, a gravitation field algorithm is proposed. The gravitation field algorithm, genetic algorithm, and firefly algorithm are used to obtain the optimal projection direction of the projection pursuit model, respectively. We compared the optimizing results and gave the navigability evaluation of local gravity maps that provide the basis for the selection of local gravity map. The comparison results show that the gravitation field algorithm has the best performance in obtaining the optimal projection direction, and the contributions of the navigability features in the navigability evaluation are most equal. Under the same experimental conditions, the local gravity map selected by the method proposed in this paper has the highest positioning accuracy and the best matching track.