Application of PFMGBEKF for Bearings-only Tracking
L. Sandeep1, S.Koteswara Rao2, Kausar Jahan3
1L.Sandeep, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Guntur (A.P), India.
2S.Koteswara Rao, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Guntur (A.P), India.
3Kausar Jahan, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Guntur (A.P), India.
Manuscript received on 07 March 2019 | Revised Manuscript received on 20 March 2019 | Manuscript published on 30 March 2019 | PP: 196-200 | Volume-8 Issue-5, March 2019 | Retrieval Number: E2954038519/19©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: Detection and estimation of object in motion is crucial issue in tracking. In underwater object tracking, object parameters like course, range and speed of the object are estimated using passive mode operation of the sonar. In this paper particle filter combined with modified gain bearings-only extended Kalman filter (PFMGBEKF) and residual sampling are used. One of the main assumptions is that the object is moving with constant velocity. Bearing measurements are nonlinearly related to the state of the object and sub-optimal filter for a nonlinear approach is unscented Kalman Filter (UKF). But UKF is unreliable under non-Gaussian noise environment. Particle filter is an advanced filter that processes nonlinear data in non-Gaussian noise environment but hassample degeneracy problem. So, PFMGBEKF is applied and the operation is analysed based on the solution convergence time. Simulation of algorithms on numerous scenarios which are close to reality is done using MATLAB.
Keyword: Bearings-Only Tracking, Modified Gain Bearings-Only Extended Kalman Filter, Particle Filter, Residual Sampling, Statistical Signal Processing.
Scope of the Article: Application of WSN in IoT