Performance analysis of direction of arrival algorithms for Smart Antenna

This paper presents the performance analysis of the direction of arrival estimation algorithms such as Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT), Multiple Signal Classification (MUSIC), Weighted Subspace Fitting (WSF), The Minimum Variance Distortionless Response (MVDR or capon) and beamspace. These algorithms are necessary to overcome the problem of detecting the arrival angles of the received signals in wireless communication. Therefore, these algorithms are evaluated and compared according to several constraints required in smart antenna system parameters, as the number of array elements, number of samples (snapshots), and number of the received signals. The main purpose of this study is to obtain the best estimation of the direction of arrival, which can be perfectly implemented in a smart antenna system. In this context, the ROOT-Weighted Subspace Fitting algorithm provides the most accurate detection of arrival angles in each of the proposed scenarios

The impact of noise on detecting the arrival angle using the root-WSF algorithm

This article discusses three standards of Wi-Fi: traditional, current and next-generation Wi-Fi. These standards have been tested for their ability to detect the arrival angle of a noisy system. In this study, we chose to work with an intelligent system whose noise becomes more and more important to detect the desired angle of arrival. However, the use of the weighted subspace fitting (WSF) algorithm was able to detect all angles even for the 5th generation Wi-Fi without any problem, and therefore proved its robustness against noise

Improving DOA Estimation Algorithms Using High-Resolution Quadratic Time-Frequency Distributions

This paper addresses the problem of direction of arrival (DOA) estimation and blind source separation (BSS) for nonstationary signals in the underdetermined case. These two problems are strongly related to the mixing matrix estimation problem. To deal with the nonstationary characteristics of signals, this study uses high-resolution quadratic time-frequency distributions (TFDs) to reduce cross-terms while keeping a good resolution for the construction of spatial TFDs. The main contributions of this paper are two-fold. First, the formulation of a statistical test for the noise thresholding step improves robustness and avoids the use of empirical parameters; this test performs multisource selection of the time-frequency points where the signal of interest is present. Second, an algorithm based on image processing methods performs an auto-source selection for mixing matrix estimation. The results on simulated signals demonstrate an improvement of 10 dB in terms of normalized mean square error for BSS and 7% in terms of relative error for DOA estimation over standard methods. 1 2017 IEEE.Scopu