Effect of Resampling on the Performance and Execution Speed of Adaptive Marginalized Particle Filter
Sanil Jayamohan1, Santhi N2, Jinju Joy3, Ramar K4
1Sanil Jayamohan*, Electronics and Communication, Noorul Islam Centre for Higher Education, Kumaracoil, Kanyakumari District, Thuckalay, Tamil Nadu, India.
2Santhi N, Electronics and Communication, Noorul Islam Centre for Higher Education, Kumaracoil, Kanyakumari District, Thuckalay, Tamil Nadu, India.
3Jinju Joy, Electronics and Communication, Lourdes Matha College of Science and Technology, Kuttichal, Trivandrum, India.
4Ramar K, Computer Science, Muthayammal Engineering College (Autonomous), Rasipuram, Namakkal District, Tamil Nadu, India.
Manuscript received on 08 August 2019. | Revised Manuscript received on 16 August 2019. | Manuscript published on 30 September 2019. | PP: 4005-4012 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5359098319/2019©BEIESP | DOI: 10.35940/ijrte.C5359.098319
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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: One of the major factors that affects the performance of adaptive filters like Particle Filter (PF), Marginalized Particle Filter (MPF) and Adaptive Marginalized Particle Filter (AMPF) is sample degeneracy. Sample degeneracy occurs when the weights associated with particles converges to zero making them useless in state estimation. Resampling is the most common method used to avoid sample degeneracy problem, in which a new set of particles are generated and weights are assigned. Performance and execution time of these filter depends a lot on what type of resampling technique is employed. AMPF is the modified version of MPF which is typically faster than PF and MPF. The main aim of this paper is to find the effect of different types of resampling on the performance and execution time of AMPF. For this, a typical target tracking problem is simulated using MATLAB. AMPF with different types of resampling techniques is used for state estimation for the above-mentioned problem and the best in terms of performance and execution speed will be found out. From the simulation, it will be clear that AMPF with systematic resampling is found to be best in terms of execution speed and performance i.e. minimum Root Mean Square Error.
Keywords: Adaptive Marginalized Particle Filter, Resampling, State Estimation, Target Tracking.
Scope of the Article: High Performance Computing