Integrated Unscented Kalman filter for underwater passive target tracking with towed array measurements

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• 作者：D.V.A.N.Ravi Kumar^a ; ^{ravikumardwarapu@gvpcew.ac.in" class="auth_mail" title="E-mail the corresponding author} ; S.Koteswara Rao^b ; K.Padma Raju^c
• 关键词：Extended Kalman filter ; Unscented Kalman filter ; Towed array ; Least squares estimator ; Integrated Unscented Kalman filter
• 刊名：Optik - International Journal for Light and Electron Optics
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：127
• 期：5
• 页码：2840-2847
• 全文大小：531 K

文摘

Under water moving target is usually tracked using the Traditional non-linear estimators such as Extended Kalman filter (EKF) and Unscented Kalman filter (UKF) with the help of noisy measurements given by a SONAR operating in passive mode. Here in this paper an Integration Technique based approach which works on the principle “Collective Opinion is better than individual” is proposed to improve the performance of the existing algorithms. In this novel method multiple UKFs accept measurements from towed array and the estimates of these different UKFs are integrated using least squares estimator, and hence the algorithm is named as Integrated Unscented Kalman filter (IUKF). Monte Carlo simulation in MATLAB R2009a is carried out to compare the performance of the proposed IUKF with the existing traditional nonlinear estimators EKF and UKF for two different scenarios to show the superiority of the proposed method.