A New Approach for Tracking Moving Objects in Underwater Environment

Obstacle avoidance and navigation is a demanding task for an autonomous underwater vehicle (AUV) due to the complex nature of the underwater environment. However, an automatic detection and tracking system is the primary element for an AUV or an aqueous surveillance network. Tracking underwater objects in an active context, represents an ongoing challenge in the field of signal processing. In order to detect the target's presence under water, the echoes reflected by the target are analysed by the receiver. Track accuracy is one of the paramount performance measures of a tracking system. Towards this, various methods such as Kalman filter (KF), extended Kalman filter (EKF) and least squares (LS) have been explored. However, all these methods have their own drawbacks. In this study, a new approach called modified gain EKF has been implemented on the simulated data for tracking of underwater moving object using bearing and elevation measurements. AUV fitted with a single sonar is used for validating the proposed bearing and elevation only tracking (BEOT) algorithm. The performance of the algorithm is evaluated in Monte Carlo simulations and results are presented in stipulated geometries.