NDLI: Adaptive particle filter based on Kullback–Leibler distance for underwater terrain aided navigation with multi-beam sonar

Content Provider	IET Digital Library
Author	Zhou, Tian Peng, Dongdong Xu, Chao Zhang, Wanyuan Shen, Jiajun
Abstract	Terrain aided navigation (TAN) is a promising approach to bound accumulated errors inherent to inertial navigation system by comparing terrain measurement with a reference map. Due to the non-linear nature of terrain, particle filters (PFs) are extensively studied for TAN because of its strong capability of dealing with non-linear problems. So far, most existing PFs for TAN manually select a fixed number of sampling particles during the entire filtering process. However, it can be highly inefficient, since the probability distribution of the state often varies drastically over time. To improve the efficiency, the Fox's adaptive PF based on Kullback–Leibler distance (KLD) is introduced for TAN, referred to as the normal KLD-PF here. In the normal KLD-PF, the number of sampling particles is adjusted online by KLD-sampling according to the size of state space. However, the normal KLD-PF has a fixed bin size, which easily causes the number of particles to surge at the early filtering stage. Thus, an improved KLD-PF with a variable bin size is proposed through limiting the total number of particles below certain value. Using a multi-beam sonar, simulation experiments with real underwater reference map demonstrate the efficiency of the proposed method.
Starting Page	433
Ending Page	441
Page Count	9
ISSN	17518784
Volume Number	12
e-ISSN	17518792
Issue Number	Issue 4, Apr (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/12/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2017.0239
Journal	IET Radar, Sonar & Navigation
Publisher Date	2018-01-15
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Filter Adaptive Particle Filter Autonomous Underwater Vehicle Digital Signal Processing Filtering Method in Signal Processing Filtering Process Fox Adaptive PF Inertial Navigation System KLD-sampling Kullback-Leibler Distance Marine System Control Mobile Robots Monte Carlo Method Multibeam Sonar Nonlinear Problem Normal KLD-PF Oceanographic And Hydrological Technique And Equipment Oceanographic Technique Particle Filter Real Underwater Reference Map Sampling Particle Signal Sampling Sonar And Acoustic Radar Sonar Signal Processing State Space Size Telerobotics Terrain Mapping Underwater Terrain Aided Navigation
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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