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

Ouelha, Samir; Bey, Abdeldjalil Aïssa El; Boashash, Boualem

doi:10.1109/tsp.2017.2718974

Cited by 31 publications

(17 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The direction of arrival (DOA) is one of the most challenging problems for locating and tracking multiple moving sources in different areas, such as radar [8], sonor [9], and mobile communications. The location of sources with distributed sensor arrays can be achieved by estimating the direction of arrival of signals from terminal sources [10,11].…”

Section: Introductionmentioning

confidence: 99%

“…Various techniques are used to estimate the direction of arrival, among which are high-resolution methods such as Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT) [12], Multiple Signal Classification (MUSIC) [13], Weighted Subspace Fitting (WSF) [14], The Minimum Variance Distortionless Response (MVDR or capon) [15] and beamspace, these techniques have attracted the most interest and has been the subject of many studies [10][11][12][13][14][15], however, none of these studies reached the best performance in smart antennas.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Performance analysis of direction of arrival algorithms for Smart Antenna

Boustani¹,

Baghdad²,

Sahel³

et al. 2019

IJECE

View full text Add to dashboard Cite

<p>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.</p>

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance analysis of direction of arrival algorithms for Smart Antenna

Boustani¹,

Baghdad²,

Sahel³

et al. 2019

IJECE

View full text Add to dashboard Cite

show abstract

“…Blind source separation (BSS) aims at recovering the source signals from several observations received by a set of sensors without any prior knowledge on the mixing process or the sources. As a branch of BSS, underdetermined BSS (UBSS) has become a critical research problem in the recent years, wherein the number of sensors is less than the number of sources . Sparse component analysis (SCA) is the main method for handling the problems facing UBSS .…”

Section: Introductionmentioning

confidence: 99%

“…As a branch of BSS, underdetermined BSS (UBSS) has become a critical research problem in the recent years, wherein the number of sensors is less than the number of sources. [1][2][3] Sparse component analysis (SCA) is the main method for handling the problems facing UBSS. [1][2][3][4][5][6] Most existing SCA algorithms are composed of two steps [4][5][6][7][8][9] : estimating the mixing matrix and then restoring the sources.…”

Section: Introductionmentioning

confidence: 99%

“…[1][2][3] Sparse component analysis (SCA) is the main method for handling the problems facing UBSS. [1][2][3][4][5][6] Most existing SCA algorithms are composed of two steps [4][5][6][7][8][9] : estimating the mixing matrix and then restoring the sources. For the first step, assuming that the sources are sufficiently sparse or satisfy the W-disjoint orthogonality condition in the whole time-frequency (TF) region, the potential function clustering and degenerate unmixing estimation technique (DUET) algorithms developed in the works of Bofill and Zibulevsky, 4 Jourjine et al, 7 and Yilmaz and Rickard 8 were the first to solve the problem of UBSS effectively.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Novel underdetermined blind source separation algorithm based on compressed sensing and K‐SVD

Ruan

Guo

Gao

2018

Trans Emerging Tel Tech

View full text Add to dashboard Cite

In this paper, a novel two‐stage underdetermined blind source separation (UBSS) algorithm based on eigenvalue decomposition and compressed sensing (CS) is proposed. UBSS techniques can separate the complex mixed signals into multiple single signal components, which are widely applied in the field of electronic surveillance, spectrum management, radar applications, etc. Our major contributions are summarized as follows: (1) deriving a single source points detection algorithm based on eigenvalue decomposition; (2) proposing a novel hierarchical coupling dictionary training method based on K‐means singular value decomposition; and (3) using the two‐stage method to realize the separation of radar signals under the condition of unknown prior information. In the first stage, we present a single source point detection method involving eigenvalue decomposition to estimate the unknown mixing matrix. In this method, the eigenvector corresponding to the maximum eigenvalue is estimated and clustered as a vector of mixing matrix. The second stage is the recovery of sources from the observed signals utilizing the mixing matrix estimated by the first step. In the second stage, we first build the unified model of UBSS and CS, thus the reconstruction algorithm in the CS field can be applied to UBSS. Then, a hierarchical coupling dictionary training method based on K‐means singular value decomposition is proposed. The dictionary training method obtains the prior training signals by pre‐separation and employs the hierarchical coupling idea to train the dictionary efficiently. Simulations illustrate the validity of the method and show that the proposed method outperforms the traditional methods in source separation, especially in real‐world noncooperative cases without prior knowledge.

show abstract

Direction of Arrival Estimation by Combining Robust Spatial Time–Frequency Distributions and Spatial Filtering

Khan

Ali

2022

Circuits Syst Signal Process

View full text Add to dashboard Cite

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

Cited by 31 publications

References 31 publications

Performance analysis of direction of arrival algorithms for Smart Antenna

Performance analysis of direction of arrival algorithms for Smart Antenna

Novel underdetermined blind source separation algorithm based on compressed sensing and K‐SVD

Direction of Arrival Estimation by Combining Robust Spatial Time–Frequency Distributions and Spatial Filtering

Contact Info

Product

Resources

About