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Comparative Analysis of ML-PSO DOA Estimation with Conventional Techniques in Varied Multipath Channel Environment

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Abstract

In the field of array signal processing, direction of arrival (DOA) estimation is a prime area of research. DOA estimation and adaptive beamforming (ABF) are the main issues in smart antennas, which dynamically find the direction of desired and interfering users and finds the optimum weights for beamforming. There are numerous spectral and eigen structure algorithms for estimating the direction of narrow band sources. However, in a complex multipath channel environment, received signals from different directions are fully or partially correlated, which prevents the applications of high resolution techniques to estimate the direction of incoming signals. Maximum likelihood (ML) is an efficient technique for DOA estimation in a low signal to noise ratio (SNR) and coherent channel environment. In this paper, we use particle swarm optimization (PSO) for estimating ML solution by optimizing complex non linear multimodal function over a high dimensional space in linear arrays. PSO-ML estimator has been compared with conventional DOA estimation techniques in uncorrelated, partially correlated and coherent channel environment. The performance of PSO-ML estimator and conventional algorithms are analyzed in varying partially correlated channel environment. The simulation results demonstrate that PSO based estimator gives superior statistical performance.

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Authors and Affiliations

  1. Department of Electronics Instrumentation & Control Engineering, University of Petroleum & Energy Studies, Dehradun, 248007, India

    Abhinav Sharma

  2. Department of Electronics & Communication Engineering, College of Technology, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, 263145, India

    Sanjay Mathur

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  1. Abhinav Sharma
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Correspondence to Abhinav Sharma.

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Sharma, A., Mathur, S. Comparative Analysis of ML-PSO DOA Estimation with Conventional Techniques in Varied Multipath Channel Environment. Wireless Pers Commun 100, 803–817 (2018). https://doi.org/10.1007/s11277-018-5350-0

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