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The Applied Research of Particle-filtering in the Multibeam Bathymetric Bottom Detection
Author: LiuXiangMing
Tutor: LiHaiSen
School: Harbin Engineering University
Course: Underwater Acoustics
Keywords: multibeam bathymetric bottom detection state-space particle-filtering Kalman filtering
CLC: P714
Type: Master's thesis
Year: 2012
Downloads: 20
Quote: 0
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Abstract
Multibeam sounding has been widely applied to the sea developing and scienceresearch, so more accurate and more reliable multibeam echosounders are needed.Bottom detection technique is an essential element for Multibeam sounding, improvingbottom detection is able to heighten the accurate of multibeam echosounders.Bottom detection technique of multibeam sounding includes beam forming technique,signal detecting algorithm and filtering algorithm.Firstly the paper discusses the beam forming technique and signal detecting algorithmabout bottom detection technique; briefly summarizes the typical multibeam bathymetricsystem; explains the theory of the basic beam forming technique and the focusing beamforming technique which is usually used in multibeam sounding; illuminates the energycenter algorithm and WMT algorithm about signal detecting algorithm.The paper mostly researches the filtering algorithm, for the first time makes use ofparticle-filtering (PF) algorithm in multibeam bathymetric bottom detection and study thebasic theory of particle-filtering algorithm. In the article Kalman filtering (KF) algorithmand median filtering algorithm are discussed; a state-space framework about multibeambathymetric bottom detection is proposed, the framework is solved, emulated andanalysed to validate its feasibility by Kalman filtering algorithm and particle-filteringalgorithm, and then a lots of experiment data are processed by Kalman filtering algorithm,median filtering algorithm and particle filtering algorithm. By comparing and analyzingthose processing results, it indicates that the state-space framework for multibeambathymetric bottom detection is absolutely right and feasible, particle filtering algorithmutterly can be applied in multibeam bathymetric bottom detection and for someexperiment data which include complicated noise (e.g. no gauss noise), the filtering resultby PF is better than KF and median filtering. So applying PF in multibeam sounding canadvance veracity and farther improve multibeam bathymetric precision.
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CLC: > Astronomy,Earth Sciences > Oceanography > Marine survey and observation > Investigation and observation methods
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