N. Ashok Kumar This email address is being protected from spambots. You need JavaScript enabled to view it.1,2, G. Sasibhushana Rao2, and S. Sudha Rani3

1Department of Electronics and Communication Engineering, Raghu Engineering College(A), Visakhapatnam, Pin: 531162, India
2Department of Electronics and Communication Engineering, AUCE (A), Andhra University, Visakhapatnam, Pin: 530003, India
3Defence Electronics Research Laboratory (DLRL) - DRDO, Hyderabad, Pin: 500005, India


 

Received: August 27, 2021
Accepted: November 6, 2021
Publication Date: December 6, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202208_25(4).0016  


ABSTRACT


Signal processing in complex environments is a challenging issue in many applications. Recent studies shows that variants of particle filter achieve significant performance in positioning and tracking applications under complex environments. In this article, a covariance-tuned EKF resampling based particle filter (CTEKF-PF) is proposed. In CTEKF-PF, a double-resampling of prior particles is introduced during the resampling stage, in which latest observed measurements are effectively integrated into each already resampled particle, rather than into the sampled particle, using a covariance-tuned Extended Kalman Filter (EKF). Thereby facilitating motion of resampled particles towards the high likelihood regions. Experimental results from the GPS receiver position estimation application show improved estimation accuracy, reduced computational load, and reduced computation time for the proposed CTEKF-PF compared to Least-squares particle filter (LSPF) and standard particle filter.


Keywords: EKF; particle filter; resampling; signal processing


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