INS/Log Integrated Navigation System with Ocean Current Velocity Model Based on Multiple Model Adaptive Estimation

Xinle Zang, Yueyang Ben, and Qian Li

Abstract: The performance of a strapdown inertial navigation system aided by a relative speed log is usually affected by ocean current. To tackle this problem, a Multiple Model Adaptive Estimation algorithm to estimate the ocean current velocity is proposed in this paper. Owing to uncertainty of the ocean current velocity, the single model usually fails in representation of its velocity, so we proposes Multiple Model Adaptive Estimation for estimating ocean current velocity, where multiple Kalman filters run in parallel, and state estimation from each filter is combined by computing weight factors and summed with the weighted outputs. By this way we can get the precise ocean current velocity and overcome the effect caused by the velocity relative to water on the filtering performance. The simulation demonstrates that the proposed algorithm can accurately estimate and compensate the ocean current velocity compared with a common Kalman filter algorithm.
Published in: 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)
April 20 - 23, 2020
Hilton Portland Downtown
Portland, Oregon
Pages: 817 - 821
Cite this article: Zang, Xinle, Ben, Yueyang, Li, Qian, "INS/Log Integrated Navigation System with Ocean Current Velocity Model Based on Multiple Model Adaptive Estimation," 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, Oregon, April 2020, pp. 817-821.
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