Underwater Terrain Matching Navigation Research Articles

Underwater terrain positioning method based on Markov random field for unmanned underwater vehicles

Underwater terrain-matching navigation technologies have become a popular topic for the high-precision positioning and navigation of autonomous underwater vehicles. This paper proposes an underwater terrain-matching positioning method based on a Markov random field model, which is based on real-time terrain data obtained using a multi-beam echo sounder. It focuses on the strong correlation between adjacent terrain data, which can improve terrain adaptability and matching accuracy. Playback simulation tests were conducted based on actual sea trial data, and the results showed that the proposed method has good positioning performance, which can correct the cumulative errors of inertial navigation systems. The results demonstrated the usability of the proposed method for positioning correction in underwater engineering applications.

Research on Performance of Terrain Matching Algorithm for Underwater Autonomous Vehicle Based on Particle Filter

AUV is a powerful tool for exploration and development of marine resources. Underwater terrain matching navigation is an emerging passive autonomous navigation method, which improves the long-term and high-precision navigation capability of AUV. The underwater terrain matching algorithm based on particle filter (PF) use a large number of particle sampling to approximate the system state, which can avoid the Taylor expansion truncation error caused by the terrain linearization method, and has good matching precision and terrain adaptability. However, due to the strong nonlinearity and the uneven distribution of the underwater terrain features, it is difficult to achieve the full-range terrain matching optimize with a single configuration parameter of PF underwater terrain matching algorithm. For this reason, this paper has comprehensively studied the PF terrain matching algorithm (referred to as PF matching algorithm). The principle, accuracy and implementation steps of PF algorithm are analyzed. With the UKF algorithm as a reference, the advantages and disadvantages of PF-based algorithm are studied through computer simulation of terrain matching navigation. The matching precision and terrain adaptability of PF-based algorithm has been tested. From the simulation results, it is concluded that the particle diversity decline is one of the main factors affecting the matching performance, which provides a basis for algorithm performance improvement and engineering adaptability improvement.

Underwater terrain matching navigation based on Gaussian process regression with a multi-beam Bathymetric sonar

Terrain matching navigation estimates the position of underwater vehicle by matching measured terrain against a prior map, which is attractive to fix the drift inherent to inertial navigation system. With a multi-beam bathymetric sonar, a local terrain is usually reconstructed by deterministic interpolation methods to match the prior map, such as linear and spline interpolation methods. However, these deterministic method change the statistical properties of the terrain, which reduce the positioning accuracy. To improve the positioning accuracy, a probabilistic interpolation method based on Gaussian process regression is proposed to reconstruct the terrain in this paper. Different from the deterministic interpolation, Gaussian process regression can not only maintain the statistical properties as far as possible but also give the uncertainty of a interpolated depth. The uncertainty can then be fused into the measurement error in the maximum likelihood estimation method to improve the positioning accuracy. Simulation experiments in a real underwater map demonstrate that the proposed method is feasible and more accurate than the traditional deterministic interpolation for underwater terrain matching navigation.

Geodesic-Based Method for Improving Matching Efficiency of Underwater Terrain Matching Navigation.

In this study, we improved the matching efficiency of underwater terrain matching navigation. Firstly, a new geodesic-based method was developed by combining the law of the shortest arc in spherical geometry with the theory of the attitude control in space and maritime environments. Secondly, along a design track, the geodesic-based method helped reduce the radius of the search matching area, and improved the matching efficiency. Finally, for parameter setting, the search matching time of underwater terrain matching navigation was reduced from 9.84 s to 1.29 s (about 7.6 times), with the matching accuracy being invariable using the new geodesic-based method.

Review of AUV Underwater Terrain Matching Navigation

Underwater terrain matching navigation technology is an important research area for the underwater navigation of Autonomous Underwater Vehicles (AUVs). Terrain matching navigation can realise long-term, subtle, all-weather, and high-precision underwater AUV navigation. In this paper, the research status of the application of AUV underwater terrain matching navigation is reviewed, the system composition, theory and terrain matching methods of underwater terrain matching navigation are summarised and the advantages of a multi-beam bathymetric system in underwater terrain matching navigation are discussed. The current research thoughts are summarised, the key issues are pointed out, and possible future development trends are discussed.

The Improved Kriging Interpolation Algorithm for Local Underwater Terrain Based on Fractal Compensation

The interpolation-reconstruction of local underwater terrain using the underwater digital terrain map (UDTM) is an important step for building an underwater terrain matching unit and directly affects the accuracy of underwater terrain matching navigation. The Kriging method is often used in terrain interpolation, but, with this method, the local terrain features are often lost. Therefore, the accuracy cannot meet the requirements of practical application. Analysis of the geographical features is performed on the basis of the randomness and self-similarity of underwater terrain. We extract the fractal features of local underwater terrain with the fractal Brownian motion model, compensating for the possible errors of the Kriging method with fractal theory. We then put forward an improved Kriging interpolation method based on this fractal compensation. Interpolation-reconstruction tests show that the method can simulate the real underwater terrain features well and that it has good usability.

Study on Underwater Terrain Matching Techniques Based on an Improved PMF Algorithm

Due to the existence of the accumulation errors in inertial navigation system(INS) and traditional point-mass filter (PMF) algorithm has problems of navigation precision and reliability when it is used for underwater terrain matching navigation, an improved PMF algorithm is proposed. Because the PMF algorithm is sensitive to initial errors and degeneration phenomenon usually appears with the increasing of matching number, the improved algorithm adopts cubic spline interpolation method to deal with the posterior probability density to prevent degeneration phenomenon. Then the TERCOM algorithm is introduced for initial alignment to reduce the influence of the initial errors. Finally the improved algorithm is simulated. The simulation results show the algorithm has a good robustness and accuracy.