Estimate-Merge-Technique-based algorithms to track an underwater moving target using towed array bearing-only measurements

• Published in: Sadhana (Bangalore) Vol. 42; no. 9; pp. 1617 - 1628
• Main Authors: Kumar, D V A N Ravi, Rao, S Koteswara, Raju, K Padma
• Format: Journal Article
• Language: English
• Published: New Delhi Springer India 01.09.2017; Springer Nature B.V
• Subjects: Bearing (direction); Computer simulation; Engineering; Estimates; Estimating techniques; Extended Kalman filter; Kalman filters; Monte Carlo simulation; Tracking; Underwater; towed array; particle filter; Extended Kalman filter; unscented Kalman filter; weighted least squares estimator
• ISSN: 0256-2499; 0973-7677
• DOI: 10.1007/s12046-017-0691-z

Bearing-only passive target tracking is a well-known underwater defence issue dealt in the recent past with the conventional nonlinear estimators like extended Kalman filter (EKF) and unscented Kalman filter (UKF). It is being treated now-a-days with the derivatives of EKF, UKF and a highly sophisticated particle filter (PF). In this paper, two novel methods based on the Estimate Merge Technique are proposed. The Estimate Merge Technique involves a process of getting a final estimate by the fusion of a posteriori estimates given by different nonlinear estimates, which are in turn driven by the towed array bearing-only measurements. The fusion of the estimates is done with the weighted least squares estimator (WLSE). The two novel methods, one named as Pre-Merge UKF and the other Post-Merge UKF, differ in the way the feedback to the individual UKFs is applied. These novel methods have an advantage of less root mean square estimation error in position and velocity compared with the EKF and UKF and at the same time require much lesser number of computations than that of the PF, showing that these filters can serve as an optimal estimator. A testimony of the afore-mentioned advantages of the proposed novel methods is shown by carrying out Monte Carlo simulation in MATLAB R2009a for a typical war time scenario.