Extended Kalman Filter for Bearings-Only Tracking
Kausar Jahan1, S. Koteswara Rao2
1Kausar Jahan, Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation (Deemed to be University), Vaddeswaram, Guntur, Andhra Pradesh, India.
2S. Koteswara Rao, Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation (Deemed to be University), Vaddeswaram, Guntur, Andhra Pradesh, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 637-640 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8088088619/2019©BEIESP | DOI: 10.35940/ijeat.F8088.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Target tracking using bearings-only measurements in passive mode operation of sonar is a crucial issue of underwater tracking. Target motion in underwater scenario is analyzed using bearings-only measurements and calculating parameters like range, course and speed of the target. This is called Target Motion Analysis (TMA). TMA process is highly non-linear as the measurements chosen are nonlinearly related to the selected target state vector and the traditional, optimal linear Kalman filter will not be appropriate to use. It is presumed that the target is moving in straight line path with constant velocity, so Extended Kalman Filter (EKF) is proposed in this paper. The algorithm is simulated for several scenarios using MATLAB. Monte-Carlo runs are performed to evaluate the capability of the algorithm.
Keywords: Extended Kalman Filter, Statistical signal processing, Target motion analysis, Target tracking.