Study on Particle Filter Based Terrain Aided Navigation under the Ultra-low Resolution Map of the Arctic

Liu, Yanji; Lu, Run; Chen, Mengwei

doi:10.23919/ccc50068.2020.9188384

Cited by 3 publications

(1 citation statement)

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“…In this paper, the sequential importance sampling and resampling (SISR) algorithm is selected as the filter of the TAN system to estimate the position of AUV. 1, it is a 3-DOF kinematic coordinate system of AUV in the horizontal plane [43]. [v and w] are the velocity and angular velocity of AUV relative to the body coordinate, [x, y, and θ] is the pose of AUV relative to the global coordinate, and |v/w| is the radius of rotation of the AUV around its turning center.…”

Section: Pf Position Estimation Systemmentioning

confidence: 99%

Underwater Terrain-Aided Navigation Relocation Method in the Arctic

Liu

Che

2020

Mathematical Problems in Engineering

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To solve the localization failure problem of terrain-aided navigation (TAN) system of the autonomous underwater vehicle (AUV) caused by large area of underwater flat terrain in the Arctic, a navigation system with relocation part is constructed to enhance the robustness of localization. The system uses particle filter to estimate the AUV’s position and reduce the nonlinear noise disturbance, and the prior motion information is added to avoid the mismatching caused by the similar altitude of low-resolution map. Based on the estimate data and the measured altitude data, the normalized innovation square (NIS) is used to evaluate the differentiation of terrain sequence, and the differentiation is used as a judgment of whether the AUV is in the switch location. A simulation experiment is carried out on the 500 m resolution underwater map of the Arctic. The results show that adding the prior motion information can restrain the divergence of the estimator; NIS can accurately reflect the sharp change of terrain sequence. After the relocation process, the AUV can still maintain the positioning accuracy within 2 km after running 50 km in the area including flat and rough terrain. This research solves the problem of localization errors in the Arctic flat terrain in the system level and provides a solution for the application of underwater navigation in the Arctic.

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Section: Pf Position Estimation Systemmentioning

confidence: 99%