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  


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


  1. [1] M. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, (2002) “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking" IEEE Transactions on Signal Processing 50(2): 174–188. DOI: 10.1109/78.978374.
  2. [2] D. Simon. Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches. Wiley-Interscience, USA, 2006.
  3. [3] F. Dong, L. Xu, and X. Li, (2020) “Particle Filter Algorithm for DOA Tracking Using Co-Prime Array" IEEE Communications Letters 24(11): 2493–2497. DOI: 10.1109/LCOMM.2019.2953466.
  4. [4] R. Hostettler, F. Tronarp, Á. F. García-Fernández, and S. Särkkä, (2020) “Importance Densities for Particle Filtering Using Iterated Conditional Expectations" IEEE Signal Processing Letters 27: 211–215. DOI: 10.1109/LSP.2020.2964531.
  5. [5] T. Li, S. Sun, T. P. Sattar, and J. M. Corchado, (2014) “Fight sample degeneracy and impoverishment in particle filters: A review of intelligent approaches" Expert Systems with Applications 41(8): 3944–3954. DOI:10.1016/j.eswa.2013.12.031.
  6. [6] L. Chen, J. Chen, H. Wang, Y. Wang, J. An, R. Yang, and H. Pan, (2020) “Remaining Useful Life Prediction of Battery Using a Novel Indicator and Framework With Fractional Grey Model and Unscented Particle Filter" IEEE Transactions on Power Electronics 35(6): 5850–5859. DOI: 10.1109/TPEL.2019.2952620.
  7. [7] S. S. Yu, J. Guo, T. K. Chau, T. Fernando, H. H.-C. Iu, and H. Trinh, (2020) “An Unscented Particle Filtering Approach to Decentralized Dynamic State Estimation for DFIG Wind Turbines in Multi-Area Power Systems" IEEE Transactions on Power Systems 35(4): 2670–2682. DOI: 10.1109/TPWRS.2020.2966443.
  8. [8] N. Ashok Kumar and G. Sasibhushana Rao, (2019) “Unscented Kalman Filter for GPS Based Positioning and Tracking Services" International Journal of Innovative Technology and Exploring Engineering 8(7S2):645–650.
  9. [9] M. K. Pitt and N. Shephard, (1999) “Filtering via Simulation:
    Auxiliary Particle Filters" Journal of the American Statistical Association 94(446): 590–599. DOI: 10.1080/01621459.1999.10474153.
  10. [10] W. Song, Z. Wang, J. Wang, F. E. Alsaadi, and J. Shan, (2021) “Distributed Auxiliary Particle Filtering With Diffusion Strategy for Target Tracking: A Dynamic Event-Triggered Approach" IEEE Transactions on Signal Processing 69: 328–340. DOI: 10.1109/TSP.2020.3042947.
  11. [11] Y.Wu, J.Wang, and P.-C. Zhang, (2014) “Least-squares particle filter" Electronics Letters 50: 1881–1882. DOI: 10.1049/el.2014.2980.
  12. [12] N. Ashok Kumar, C. Suresh, and G. Sasibhushana Rao, (2018) “Extended Kalman Filter for GPS Receiver Position Estimation" Advances in Intelligent Systems and Computing 695: 481–488. DOI: 10.1007/978-981-10-7566-7_47.
  13. [13] P. Sirish kumar, V. B. S. Srilatha Indira Dutt, and N. Ashok kumar, (2020) “A sensitivity analysis of extended kalman filter for gps position estimation with and without clock offset" Materials Today: Proceedings 33: 3626–3629. DOI: 10.1016/j.matpr.2020.05.667.
  14. [14] P. Galkowski and M. Islam, (1991) “An alternative derivation of the modified gain function of Song and Speyer" IEEE Transactions on Automatic Control 36(11): 1323–1326. DOI: 10.1109/9.100947.
  15. [15] T. Song and J. Speyer, (1985) “A stochastic analysis of a modified gain extended Kalman filter with applications to estimation with bearings only measurements" IEEE Transactions on Automatic Control 30(10): 940–949. DOI: 10.1109/TAC.1985.1103821.
  16. [16] C. Pengpeng, M. Honglu, G. Shouwan, and Y. Huang, (2015) “Modified Extended Kalman Filtering for Tracking with Insufficient and Intermittent Observations" Mathematical Problems in Engineering 2015: 1–9.
  17. [17] G. S. Rao. Global Navigation Satellite Systems - with essentials of satellite communication. MC GRAW HILL, India, 2010.
  18. [18] T. Peter J G and M. Oliver. Springer Handbook of Global Navigation Satellite Systems. Springer International Publishing, Switzerland, 2017.


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