Underwater Bearings-Only Tracking using Particle Filter
Garapati Vaishnavi1, B. Keshav Damodhar2, S. Koteswara Rao3, Kausar Jahan4
1Garapati Vaishnavi, Department of ECE, Koneru Lakshmaiah Education Foundation, Vijayawada (A.P), India.
2B.Keshav Damodhar, Department of ECE, Koneru Lakshmaiah Education Foundation, Vijayawada (A.P), India.
3S.Koteswara Rao, Department of ECE, Koneru Lakshmaiah Education Foundation, Vijayawada (A.P), India.
4Kausar Jahan, Department of ECE, Koneru Lakshmaiah Education Foundation, Vijayawada (A.P), India.
Manuscript received on 07 March 2019 | Revised Manuscript received on 20 March 2019 | Manuscript published on 30 March 2019 | PP: 451-455 | Volume-8 Issue-5, March 2019 | Retrieval Number: E3068038519/19©BEIESP
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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: Underwater target tracking is a pivotal area in the present scenario. In this paper, passive target tracking is accomplished. By using bearings-only measurements, the parameters like range, course and speed of the target with respect to observer are calculated which helps in determining the target motion. This is called Target Motion Analysis (TMA). As bearings-only tracking is non-linear in nature, traditional Kalman filter which is linear filter, cannot be used. So, Particle filter (PF) which is non-linear filter is preferred. Since particles/samples are used, particle degeneracy or sample impoverishment may occur. To avoid sample impoverishment, resampling of the particles is done after every iteration. So, stratified resampling which can give greater precision is used to reduce the sample impoverishment problem. For improved performance of the filter, PF is combined with Modified Gain Extended Kalman filter (MGEKF). The algorithm is simulated using different scenarios in MATLAB to evaluate its sensitivity. Estimating the performance of the algorithm depending on their convergence time is carried out.
Keyword: Modified Gain Extended Kalman Particle Filter (MGEKPF), Particle Filter (PF), Stratified Resampling, Sample Impoverishment, Target Tracking.
Scope of the Article: Microwave Filter