## 1 Introduction  
The MUSIC algorithm (Multiple Signal Classification) is known as one of the best algorithms for estimating multipath angles. It decomposes the covariance matrix into eigenvalues to obtain eigenvectors of the signal and the noise. Its advantage is good resolution. One of the downsides is that you must use a lot of data. In addition, the signals cannot be changed during treatment, and they must be decorrelated. It is an algorithm that allows active spatio-temporal separation of multipath. At first, it was developed in the time domain, but later it became improved thanks to the spatial dimension that was added. However, as an active algorithm, it needs periodic sending of a known signal. The DOA allows base stations to locate users, to allow reuse of the same communication frequencies within the same cell by forming separate beams for each of these users, without interference or interference that could deteriorate the quality of communication. This method is called Spatial Division Multiple Access or Spatial Division Multiple Access. In addition, precise knowledge of the directions and arrival times would be sufficient to properly estimate the propagation channel without having to resort to a preamble as is done in mobile radio communication. Another example of application is the very precise identification of directing a phone call after an emergency so that the rescue team can be dispatched to the appropriate location. This last example is even more practical, because unlike the technologies currently in use such as GPS and the triangulation of base stations,one would be able to clearly identify an apartment in a building block even with several floors [1; 2; 3]. Several algorithms for determining the direction of arrival of beams relative to the antenna array already exist, and each has its advantages and disadvantages. The general operating principle of these algorithms is to obtain, from the data collected, a spectrum or a pseudo-spectrum indicating, depending on the direction of observation, the importance or not of a source in this direction. Among these algorithms, there is lane formation which gives an estimate of the power coming from the target direction. On the other hand, the spectrum obtained is much less precise than those with high-resolution algorithms. The maximum likelihood is a high-resolution algorithm based on iterative calculations of conditional probabilities, but effective in the case of coherent interference. Considered the most widely used for DOA applications, the MUSIC algorithm [4] is highly accurate even in the presence of noise. MUSIC exploits the subspaces generated by the eigenvectors resulting from a decomposition upstream of the covariance matrix of the signals received. Its main weakness besides the heavy computing time is the mediocrity of the estimate in the presence of interference from a coherent wave to the signal incident to the network. To correct this imperfection, the technique of spatial smoothing [5] is often combined with MUSIC [6; 7]. The list is not exhaustive, there are more algorithms such as Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) [4; 5; 6; 7; 8], Capon and even others, based on neural networks ideal for real-time applications [9; 10]. The latter, however, give less precise results. All these methods justify their existence through the compromise between precision and the heaviness of the computation time for applications requiring real-time processing. This last limitation, namely the heavy computing time, is less and less limiting due to the development of very high-density integrated circuits, grouped under the acronym of VLSI (Very Large-Scale Integration). These are endowed with incomparable computing power from structures based on parallelism and allowing extremely short operating cycles [11; 12; 13]. In [14] the authors propose to further decrease the execution time of this parallel method.