Applications of a nonlinear optimization solver and two-stage comprehensive Denoising techniques for optimum underwater wideband sonar echolocation system

Chien Hsun Tseng

doi:10.3934/jimo.2013.9.205

Article Contents

2013, Volume 9, Issue 1: 205-225. Doi: 10.3934/jimo.2013.9.205

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Applications of a nonlinear optimization solver and two-stage comprehensive Denoising techniques for optimum underwater wideband sonar echolocation system

Chien Hsun Tseng^1,

1.
Department of Information Engineering, Kun Shan University

Received: September 2011

Revised: June 2012

Published: January 2013

Abstract / Introduction Related Papers Cited by

Abstract

This paper focuses on empirical design and performs real data test of a novel algorithm that contributes to the purpose of solving a specific SIP problem arising from a classical wideband active sonar echo location system in noisy environment. The algorithm is achieved by firstly isolating potential contact signals of interest embedded in the scattered returns through the first-stage denoising using an adaptive noise canceling (ANC) neuro-fuzzy scheme. The ANC output is then feed into an iterative target motion analysis (TMA) scheme composed of the second-stage denoising and optimal motion estimation. In the first-stage denoising, the adaptive neuro-fuzzy inference system (ANFIS) is the core processor of ANC for tracking both the linear and nonlinear relations among complex contact signals. The second-stage denoising is appealed for further noise compression and is accomplished via trimmed-mean (TM) levelization and discrete wavelet denoising (WDeN). The two-stage comprehensive denoising techniques yield fine tuned signals for the system deconvolution based on solving a semi-infinite programming (SIP) problem. These two schemes form an ANC-TMA(CWT) algorithm for rapid processing of target echoes and provide a higher degree of signal detection capability with an increased robustness against false signal detections. Advantages and simulation results are discussed in terms of detection performance and computational time consumption.

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Mathematics Subject Classification: 90C34.

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