Improving Matching Accuracy of Underwater Gravity Matching Navigation Based on Iterative Optimal Annulus Point Method with a Novel Grid Topology

Abstract

In this study, we improve the matching accuracy of underwater gravity matching navigation. Firstly, the Iterative Optimal Annulus Point (IOAP) method with a novel grid topology is proposed for breaking through the inherent grid structure limit of the canonical gravity matching algorithm and enhancing its underwater gravity matching accuracy. The theory of IOAP is as follows: (1) small-annulus matching and positioning mechanism on the tracking starting point is developed by employing the starting point and drift error of the INS (Inertial Navigation System), the fixed rotation angle, etc. The optimal matching location of the starting point is obtained by matching and comparing the matched points in this small-annulus grid, which contributes to heightening the initial-position error insensitivity of the algorithms. (2) Variable-angle three-layer annulus matching and positioning mechanisms on the tracking ending point were constructed by using the optimal matching location of the starting point and combining the tracking direction-and-distance information of the INS and the cumulative drift error, etc. It is used to generate the annulus matching points with the ring-type grid topology. (3) The optimal matching position of the ending point in this annulus is obtained by iteratively calculating the evaluation index value of the matching points and following the evaluation index optimal rule. Secondly, we comprehensively consider the main performance evaluation indexes of the underwater gravity matching algorithms, such as the statistical indicators of the matching accuracy, the average matching time and the matching success rate, and take them as a basis of the pros and cons of the matching analysis. Furthermore, under conditions that include different scale searching regions or different reference-angle ring radii, the statistical results verify that the IOAP had a different matching ability and better robustness. Finally, several trajectories with the starting points from different areas and the ending points in different gravity ranges are tested and compared to carry out the numerical simulations. These results indicate that the IOAP has many advantages, such as a high matching accuracy and strong positioning applicability in different gravity regions. Compared with the TERCOM (terrain contour matching algorithm), its average matching accuracy was the highest, increased by 40.39%.

Improving Matching Efficiency and Out-of-domain Reliability of Underwater Gravity Matching Navigation Based on a Novel Soft-margin Local Semicircular-domain Re-searching Model

“…Based on the previous research [1] on positioning statistical results of TERCOM, we find that, according to the matched success rate difference between the tracking real position and these pre-matched points, the surrounding area of TERCOM's square domain can be divided into 3 types: as the in-domain high-probability matched Region F, the in-domain small-probability matched Region G and the out-of-domain small-probability matched Region H, as shown in Figure 1.…”

“…Example data were downloaded from the San Diego website (http://topex.ucsd.edu/, 10 June 2020) from the university of California, where the resolution of gravity data is 1 × 1 . Moreover, the satellite remote sensing graph from the South China Sea for study is plotted in Figure 4a, which is directly taken from the GEBCO website (https://download.gebco.net/, 21 January 2020), and the longitude-latitude scope of the selected studied region is (113 [1]. Meanwhile, we employed a bilinear interpolation method [26] to convert the gravity benchmark data into the gravity data with the 100 m × 100 m grid resolution, as shown in Figure 4b.…”

“…In the simulation sample block, the resolution of the gravity grid after interpolation is 100 m × 100 m, and other simulated parameters [1] are set as listed in Table 1. Moreover, the positioning accuracy in this paper refers to the Euclidean distance from the matching position to the actual position of underwater vehicles.…”

“…The INS (Inertial Navigation System) is the most common real-time, autonomous, all-weather navigation technology for underwater vehicles, and it has better high-accuracy positioning characteristics that operate within a very short time [1,2]. However, the intrinsic errors of inertial navigation components (i.e., gyroscopes and accelerometers) and multiple integrals of positioning solutions lead to the accumulation and divergence of INS errors over time [3], making it difficult to achieve the high-accuracy positioning objective of underwater vehicles for a long time.…”