Estimating the direction of arrival (DOA) of source signals is an important research interest in application areas including radar, sonar, and wireless communications. In this paper, the problem of DOA estimation is addressed on concentric circular antenna arrays (CCA) in detail as an alternative to the well-known geometries of the uniform linear array (ULA) and uniform circular array (UCA). We define the steering matrix of the CCA geometry and investigate the performance analysis of the array in the DOA-estimation problem by simulations that are realized through varying the parameters of signal-to-noise ratio, number of sensors, and resolution angle of sensor arrays by using the MUSIC (Multiple Signal Classification) algorithm. The results present that CCA geometries provide higher angle resolutions compared to UCA geometries and require less physical area for the same number of sensor elements. However, as a cost-increasing effect, higher computational power is needed to estimate the DOA of source signals in CCAs compared to ULAs. 1. Introduction The problem of estimating the direction of arrival (DOA) of source signals by sensor arrays has been widely researched for applications such as radar, sonar, and wireless communication technologies. In radar applications as part of military systems, estimating DOAs of signals is crucial to differentiate targets, whereas in communications DOA information provides spatial diversity to increase the number of users communicating simultaneously [1]. In case of multisource signals, it is inevitable to employ sensor array configurations to estimate the DOA of each signal. Increasing the number of sensors in an array provides a higher signal-to-noise ratio (SNR) by processing the signals received from the sensors in parallel [2]. In DOA estimation applications the source signals are usually modeled as narrowband, or in case of wideband signals, and they may be processed as a composition of narrowband signals [3, 4]. There are various techniques in DOA estimation, most of them are either model-based or eigen-analysis-based ones. Model-based techniques, including least mean square (LMS) and sample matrix inversion based algorithms, may have higher computational complexity [5]. On the other hand, eigen-analysis-based techniques rely on the phase differences of signals impinging on array elements. One of these methods is the well-known MUSIC (Multiple Signal Classification) algorithm that depends on the signal subspace separation by using eigen-analysis techniques [6, 7]. The algorithm has already been widely employed in
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