Terrain aided navigation for underwater vehicles using maximum likelihood method

Peng, Dongdong; Zhou, Tian; Li, Haisen; Zhang, Wanyuan

doi:10.1109/coa.2016.7535750

Cited by 9 publications

(8 citation statements)

References 9 publications

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“…Given the initial probability density of the state, the posterior probability density at each time k can be computed recursively by equations (6) and (7). In the case where the state space model and the measurement model are linear with the Gaussian noise, the analytic solution to the equations can be obtained by the Kalman filter.…”

Section: Recursive Bayesian Estimation Methodsmentioning

confidence: 99%

“…If a square grid with the same resolution Δ in both directions is considered, the state prediction equation (6) and the measurement update equation 7are performed in a direct way, as follows:…”

Section: Two-dimensional Point Massmentioning

confidence: 99%

“…So far, existing TAN methods may be grouped into the batch correlation method like TERCOM [4], ICCP [5], and maximum likelihood estimation [6,7] and recursive Bayesian method like SITAN, point mass filter [8,9], and particle filter [10]. Compared with the batch correlation method, the recursive Bayesian method is more sophisticated since it incorporates motion uncertainty between adjacent measurements which is not accounted for in the batch correlation method [11].…”

Section: Introductionmentioning

confidence: 99%

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Marginalized Point Mass Filter with Estimating Tidal Depth Bias for Underwater Terrain-Aided Navigation

Peng

Zhou

et al. 2019

Journal of Sensors

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Terrain-aided navigation is a promising approach to submerged position updates for autonomous underwater vehicles by matching terrain measurements against an underwater reference map. With an accurate prediction of tidal depth bias, a two-dimensional point mass filter, only estimating the horizontal position, has been proven to be effective for terrain-aided navigation. However, the tidal depth bias is unpredictable or predicts in many cases, which will result in the rapid performance degradation if a two-dimensional point mass filter is still used. To address this, a marginalized point mass filter in three dimensions is presented to concurrently estimate and compensate the tidal depth bias in this paper. In the method, the tidal depth bias is extended as a state variable and estimated using the Kalman filter, whereas the horizontal position state is still estimated by the original two-dimensional point mass filter. With the multibeam sonar, simulation experiments in a real underwater digital map demonstrate that the proposed method is able to accurately estimate the tidal depth bias and to obtain the robust navigation solution in suitable terrain.

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Section: Recursive Bayesian Estimation Methodsmentioning

confidence: 99%

Section: Two-dimensional Point Massmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Marginalized Point Mass Filter with Estimating Tidal Depth Bias for Underwater Terrain-Aided Navigation

Peng

Zhou

et al. 2019

Journal of Sensors

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“…In recent years, TAN's far-reaching application potential in marine fields has aroused widespread concern of many scholars. Algorithms for underwater topographic matching positioning and navigation have been proposed one after another, such as iterated closest contour point-(ICCP-) based [13], cross-correlation-based [14], and maximum likelihood estimation-based [15] terrain navigation. The ICCP-based terrain navigation algorithm works by single-beam sounding sonar, whereas the terrain navigation method which is based on correlation and maximum likelihood estimation is applicable to not only single-beam sonar but also multibeam sonar.…”

Section: Introductionmentioning

confidence: 99%

“…Under this criterion, in both X and Y directions, 31.74% of the points with larger gradient values can be screened out as matching points through decision threshold σ. Finally, the set of matching points is determined in (15).…”

mentioning

confidence: 99%

Terrain Matching Localization for Underwater Vehicle Based on Gradient Fitting

et al. 2018

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Terrain matching positioning is a promising method to overcome the problem that the inertial navigation error of the underwater vehicle accumulates over time. In the conventional terrain matching method, all measurement points are commonly used for matching and positioning. However, this method fails to be taken into a balanced consideration on both the computation complexity and the positioning accuracy. To reduce the computation and ensure the accuracy at the same time, an improved terrain matching method based on the gradient fitting is proposed in this paper. In the method, the gradient distributions of multiple terrain regions are firstly analyzed. Then, normal distribution is used to fit them, and according to the distribution, points with larger gradient values are selected as matching points. Finally, minimum absolute difference matching is chosen to match for positioning. Simulation results using multibeam sonar show that the improved terrain matching localization method not only reduces the computational complexity but also improves the accuracy of positioning.

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Fast Extraction of Local Underwater Terrain Features for Underwater Terrain-Aided Navigation

Chen

Zhang

Chang

et al. 2019

J. Marine. Sci. Appl.

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Terrain aided navigation for underwater vehicles using maximum likelihood method

Cited by 9 publications

References 9 publications

Marginalized Point Mass Filter with Estimating Tidal Depth Bias for Underwater Terrain-Aided Navigation

Marginalized Point Mass Filter with Estimating Tidal Depth Bias for Underwater Terrain-Aided Navigation

Terrain Matching Localization for Underwater Vehicle Based on Gradient Fitting

Fast Extraction of Local Underwater Terrain Features for Underwater Terrain-Aided Navigation

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