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.
Highlights
Active research scope in the area of wireless communication is Direction of arrival (DOA) estimation (Godara, 1997; Stoica and Nehorai, 1990)
In high SNR conditions above 15 dB multiple signal classification (MUSIC), ESPRIT, and chicken swarm optimization (CSO) algorithm performs. This proves that conventional techniques MUSIC, ESPRIT algorithm can be utilized for DOA estimation for deterministic signals. (b) Probability of Resolution (PR) This is the algorithm's ability to resolve signals that are closely spaced
In this article, CSO is explored for finding the direction of closely spaced signals which are directed towards the linear array
Summary
Active research scope in the area of wireless communication is DOA estimation (Godara, 1997; Stoica and Nehorai, 1990). Metaheuristic (high-level search) approaches have been considered for optimizing the likelihood function for accurate estimation of the direction of signals in a low SNR environment. Sharma and Mathur (2018) explored PSO to estimate the incoming signal direction in the presence of partially correlated, uncorrelated, and coherent channels for linear arrays and compared the results with conventional techniques. Gravitational search algorithm (GSA) is one of the physics-based algorithms and has been used to optimize the ML function to find the direction of uncorrelated signals in linear array (Sharma and Mathur, 2016). The signal vector is deterministic with unknown sequences In this deterministic model, the estimate of the incoming signal’s angle, θ, by ML, is acquired by the process of optimization of the non-linear multimodal function, given by: fDML = tr[(IM − A(AHA)−1AH)R]. With this set of array weights, the pseudospectrum is given by: PC(θ)
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