## 1 Introduction  
The applications of wireless technology have spread into several fields, including sensor networks, environmental monitoring and public security [1; 2; 3]. In light of these developments, several technological policies have been created to satisfy different demands. In localization systems, the classical problem in array signal processing is finding the direction or location of sources/emitters that send signals. Thus, the incident signals on an antenna array are processed digitally to extract various types of information including their angle of arrival (AOAs) [4]. Note that the term DOA (for direction of arrival) is also used in the literature [5; 6; 7]. Since rescue and search services always need accurate locations of the electromagnetic beacon sources, the U.S. Federal Communications Commission (FCC) has passed a mandatory rule for location accuracy on wireless emergency calls requiring an error of no more than 125 m [8]. Additionally, accurate AOA estimation helps mobile communications and wireless positioning systems to improve their performance significantly. A useful system that has recently appeared is smart antenna technology, which seeks efficient methods for direction finding and adaptive beam forming, integrating these algorithms with Multiple Input Multiple Output (MIMO) technology [9]. As the performance of MIMO is critically related to the propagation environment, it is vital to model the space-time path accurately in order to achieve good MIMO systems [10; 11]. This technology is found especially useful in mobile communication systems through enhancing the coverage, extending the range and also increasing the capacity of systems compared to the classic antenna array set-up [12]. The AOA technique is an essential tool in controlling the space-time path, and requires accurate determination of direction of arrival at each measurement point. This aids the beamforming approach to steer the array's beams towards wanted directions while suppressing noise and interference source signals [13]. Even small inaccuracies in estimation of angles of arrival can increase the bit error rate (BER) or cause large errors in direction when locating and tracking mobile stations, which explains the current research interest in this area [14; 15]. During the past few decades, several AOA estimation methods have been used in smart antenna systems for mobile, emergency tracking position applications. The Capon or minimum variance distortion-less response (MVDR) method [16] finds the DOAs signals by estimating the power of arriving signals from one direction and considering all other signals as interference. The performance estimation of the Capon algorithm declines sharply if the source signals are correlated or when the number of snapshots is less than the number of sensors. Subspace methods, based on decomposition of the CM or observation data into signal and noise subspaces, give better estimation resolution compared with previous types. Examples of these algorithms are Multiple Signal Classification (MUSIC), the Minimum Norm method and Estimation of Signal Parameters via the Rotational Invariance Technique (ESPRIT). All these algorithms require a singular value decomposition (SVD) of the received data matrix, or an eigenvalue decomposition (EVD) of the correlation matrix. This type of algorithm provides better resolution and performance compared with the examples discussed above. MUSIC is a well-known method due to its high resolution advantage [17], however, it makes high computational demands. A Minimum Norm algorithm was proposed by [PERSON] [18] and then developed by [PERSON] and [PERSON] [19]. This method finds an optimum array weights vector that minimizes the norm of the array output, making the assumption that the first element in the weights vector is unity and other elements are zero. This algorithm has lower complexity than MUSIC but it is usable only with uniform linear arrays.