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. INDEX TERMS Navigability analysis, gravitation field algorithm, projecting pursuit-based selection method, gravity navigability features.

The existing real-time iterative closest contour point (ICCP) algorithm uses fixed matching sequence length, which is set according to human experience and cannot obtain the best positioning accuracy on all tracks. This paper proposes a real-time ICCP algorithm with the optimized matching sequence length (OMSL-ICCP) based on the analysis of the shortcomings of the existing real-time ICCP. The optimal matching sequence length of OMSL-ICCP under the current measured gravity anomaly sequence is obtained by the golden section search. The Hausdorff distance is utilized to obtain the search range of the closest contour point, which can effectively shrink the search range of the closest contour point. And the gravitation field algorithm is applied to further improve the positioning accuracy. In simulation tests with different gravity sensor measurement noises, different INS positioning errors and different gravity map resolutions, the difference of positioning performance between the existing real-time ICCP and the OMSL-ICCP is compared. Under the same test conditions, the simulation results show that the positioning accuracy of OMSL-ICCP is higher than the positioning accuracy obtained by the existing real-time ICCP algorithm with the optimal sequence length.INDEX TERMS Gravity aided positioning, real-time ICCP, golden section search, gravitation field algorithm, Hausdorff distance.

Swarm intelligence method is an effective way to improve the particle degradation and sample depletion of the traditional particle filter. This paper proposes a particle filer based on the gravitation field algorithm (GF-PF), and the gravitation field algorithm is introduced into the resampling process to improve particle degradation and sample depletion. The gravitation field algorithm simulates the solar nebular disk model, and introduces the virtual central attractive force and virtual rotation repulsion force between particles. The particles are moves rapidly to the high-likelihood region under action of the virtual central attractive force. The virtual rotation repulsion force makes the particles keep a certain distance from each other. These operations improve estimation performance, avoid overlapping of particles and maintain the diversity of particles. The proposed method is applied into INS/gravity gradient aided navigation, by combining the sea experimental data of an inertial navigation system. Compared with the particle swarm optimization particle filter(PSO-PF) and artificial physics optimized particle filter (APO-PF), the GF-PF has higher position estimate accuracy and faster convergence speed with the same experimental conditions.

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