volume | PIER Journals

Abstract

This paper proposes an underdetermined direction of arrival (DOA) estimation for multiple input and multiple output (MIMO) sparse additive white Gaussian noise (AWGN) channels. Accurate DOA estimation helps in better signal analysis and noise cancellation in the channel. A novel multiplicative multi-kernel basis vector-based non-negative sparse Bayesian learning (NNSBL) algorithm is implemented over a predefined grid. Simultaneously stochastic cuckoo search algorithm (CSA) is exploited virtually to improve the DOA approximation for a non-uniform linear array (NULA) geometry by an optimized antenna reconfiguration model. The simulated and experimental results show that the proposed algorithm yields an optimized root mean square error (RMSE) for different optimized wavelengths of the randomly generated signals. The RMSE convergence graphs demonstrate the effectiveness of the new method for different signal-to-noise (SNR) values.

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Dr. Narayanaswamy Anughna

Dr. Muniyappa Ramesha