access icon free Robust particle filter based on Huber function for underwater terrain-aided navigation

© The Institution of Engineering and Technology
Received 22/03/2019, Accepted 21/06/2019, Revised 30/05/2019, Published 24/06/2019

Terrain-aided navigation (TAN) is a promising technique to determine the location of underwater vehicle by matching terrain measurement against a known map. The particle filter (PF) is a natural choice for TAN because of its ability to handle non-linear, multimodal problems. However, the terrain measurements are vulnerable to outliers, which will cause the PF to degrade or even diverge. Modification of the Gaussian likelihood function by using robust cost functions is a way to reduce the effect of outliers on an estimate. The authors propose to use the Huber function to modify the measurement model used to set importance weights in a PF. They verify their method in simulations of multi-beam sonar in a real underwater digital map. The results demonstrate that the proposed method is more robust to outliers than the standard PF (SPF).

Inspec keywords: oceanographic techniques; path planning; particle filtering (numerical methods); geophysical signal processing; radar signal processing; Gaussian processes; terrain mapping; sonar; autonomous underwater vehicles

Other keywords: multibeam sonar; robust particle filter; Huber function; measurement model; robust cost functions; underwater vehicle; Gaussian likelihood function; terrain measurement; TAN; underwater terrain-aided navigation; underwater digital map; multimodal problems

Subjects: Signal processing and detection; Geophysics computing; Filtering methods in signal processing; Monte Carlo methods; Probability theory, stochastic processes, and statistics; Oceanographic and hydrological techniques and equipment; Data and information; acquisition, processing, storage and dissemination in geophysics; Mobile robots; Geography and cartography computing; Sonar and acoustic radar; Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research; Telerobotics; Monte Carlo methods

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