Abstract
This paper proposes a new low complexity angle of arrival (AOA) method for signal direction estimation in multi-element smart wireless communication systems. The new method estimates the AOAs of the received signals directly from the received signals with significantly reduced complexity since it does not need to construct the correlation matrix, invert the matrix or apply eigen-decomposition, which are computationally expensive. A mathematical model of the proposed method is illustrated and then verified using extensive computer simulations. Both linear and circular sensors arrays are studied using various numerical examples. The method is systematically compared with other common and recently introduced AOA methods over a wide range of scenarios. The simulated results show that the new method has several advantages in terms of reduced complexity and improved accuracy under the assumptions of correlated signals and limited numbers of snapshots.
Highlights
The applications of wireless technology have spread into several fields, including sensor networks, environmental monitoring and public security [1,2,3]
Since the incident signals on a sensor array are intrinsically sparse in the spatial domain, the exploitation of Compressive Sensing (CS) has been extended to include the DOA estimation problem in array signal processing, where application of these methods improved estimation accuracy with fewer measurements and robustness to noise as presented in CS-Multiple Signal Classification (MUSIC) [26] and subspace-augmented (SA-) MUSIC [27]
Many applications call for a high resolution and low complexity angle of arrival (AOA) estimation method in these conditions. The contribution of this present paper is to propose a simple and low complexity method that can be used to estimate the DOAs of signals efficiently; the proposed method is called the propagator direct data acquisition (PDDA) method
Summary
The applications of wireless technology have spread into several fields, including sensor networks, environmental monitoring and public security [1,2,3]. Since the incident signals on a sensor array are intrinsically sparse in the spatial domain, the exploitation of Compressive Sensing (CS) has been extended to include the DOA estimation problem in array signal processing, where application of these methods improved estimation accuracy with fewer measurements and robustness to noise as presented in CS-MUSIC [26] and subspace-augmented (SA-) MUSIC [27] These approaches offer good robustness to noise and correlated signals, their estimation performance is degraded unless there is a prior knowledge of the number of arrival signals [28]. The proposed method is implemented by using linear and circular sensor arrays to perform 1D and 2D estimation; many numerical examples are presented to show its performance It is compared with several popular and recent AOA methods in terms of numbers of snapshots collected, behaviour with SNR, correlation of signals sources and execution time. E {·} represents the statistical expectation. (·)T and (·)H indicates to the transposition and conjugate transposition of a vector or a matrix. (.̂) represents the estimated value
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