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International Journal of Mathematical, Engineering and Management Sciences

ISSN: 2455-7749


Maximum Likelihood Direction of Arrival Estimation using Chicken Swarm Optimization Algorithm

Maximum Likelihood Direction of Arrival Estimation using Chicken Swarm Optimization Algorithm

Abhinav Sharma
Department of Electrical and Electronics Engineering, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India.

R. Gowri
Department of Electrical and Electronics Engineering, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India.

Vinay Chowdary
Department of Electrical and Electronics Engineering, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India.

Abhishek Sharma
Department of Research & Development, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India.

Vibhu Jately
MCAST Energy Research Group, Institute of Engineering and Transport, MCAST, Paola, Malta.

DOI https://doi.org/10.33889/IJMEMS.2021.6.2.038

Received on September 15, 2020
  ;
Accepted on December 22, 2020

Abstract

Aspects towards the area of array signal processing are majorly confined to two techniques, Direction of arrival (DOA) estimation and adaptive beamforming (ABF). There exist different traditional techniques for estimating the direction of incoming signals such as spectral and Eigen structure-based methods that find the direction of incoming signals. The major drawback of these techniques are that they fail to find the direction of the incoming signal in environments of low signal to noise (SNR). The maximum likelihood (ML) method has an upper hand in terms of statistical performance as compared to conventional methods and finds the direction of signal in low SNR conditions. In this article, the chicken swarm optimization (CSO) algorithm is explored for the optimization of ML function to find the direction of signals in uniform linear arrays (ULA). The algorithm is inspected with respect to the root mean square error (RMSE) and the probability of resolution (PR). Simulation results of the proposed technique prove that the ML-CSO algorithm outperforms other heuristic approaches such as the flower pollination algorithm (FPA) and other conventional techniques such as Capon, multiple signal classification (MUSIC), estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm in lower SNR environment.

Keywords- DOA, CSO, FPA, ML, RMSE.

Citation

Sharma, A., Gowri, R., Chowdary, V., Sharma, A., & Jately, V. (2021). Maximum Likelihood Direction of Arrival Estimation using Chicken Swarm Optimization Algorithm. International Journal of Mathematical, Engineering and Management Sciences, 6(2), 621-635. https://doi.org/10.33889/IJMEMS.2021.6.2.038.