Direction-of-Arrival Estimation Using a Mixed $\ell _{2,0}$ Norm Approximation

Hyder, Md Mashud; Mahata, Kaushik

doi:10.1109/tsp.2010.2050477

Cited by 299 publications

(208 citation statements)

References 31 publications

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“…where is a parameter controlling the trade-off between the sparsity of X k and the error kX k 0X k k 2 ; f (1) defines some (differentiable) function that enforces the joint sparsity constraint R(X k ) = R(X l ) for k 6 = l. An elegant example of f (1) is given in [28], which exploits the property that when the joint sparsity constraint is satisfied, the number of nonzero rows of the bigger matrix [X 0 ; ...;X K01 ] will be minimized. Accordingly, the following function is coined:…”

Section: Aliasing-free Ssr Recoverymentioning

confidence: 99%

Aliasing-Free Wideband Beamforming Using Sparse Signal Representation

Tang

Blacquière

Leus

2011

IEEE Trans. Signal Process.

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Abstract-Sparse signal representation (SSR) is considered to be an appealing alternative to classical beamforming for direction-of-arrival (DOA) estimation. For wideband signals, the SSR-based approach constructs steering matrices, referred to as dictionaries in this paper, corresponding to different frequency components of the target signal. However, the SSR-based approach is subject to ambiguity resulting from not only spatial aliasing, just like in classical beamforming, but also from the over-completeness of the dictionary, which is typical to SSR. We show that the ambiguity caused by the over-completeness of the dictionary can be alleviated by using multiple measurement vectors. In addition, by considering the uniform linear array (ULA) structure, we argue that if the target signal contains at least two frequencies, whose absolute difference phrased in wavelengths is larger than twice the array spacing, the spatial aliasing corresponding to these frequencies will be completely distinct. These properties enable us to adapt the existing algorithms to extract the target DOAs without ambiguity.Index Terms-Direction-of-arrival estimation, multiple-dictionary, orthogonal matching pursuit, sparse signal representation, spatial aliasing, uniform linear array, wideband beamforming.

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Section: Aliasing-free Ssr Recoverymentioning

confidence: 99%

Aliasing-Free Wideband Beamforming Using Sparse Signal Representation

Tang

Blacquière

Leus

2011

IEEE Trans. Signal Process.

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“…A number of joint sparse representation methods specific to wideband DOA estimation has been studied in the literature [87], [88], [148]. Some important of these are briefly described below.…”

Section: Compressive Doa Estimationmentioning

confidence: 99%

Application of Compressive Sensing in Cognitive Radio Communications: A Survey

Sharma

Lagunas

Chatzinotas

et al. 2016

IEEE Commun. Surv. Tutorials

205

112

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Abstract-Compressive Sensing (CS) has received much attention in several fields such as digital image processing, wireless channel estimation, radar imaging, and Cognitive Radio (CR) communications. Out of these areas, this survey paper focuses on the application of CS in CR communications. Due to the underutilization of the allocated radio spectrum, spectrum occupancy is usually sparse in different domains such as time, frequency and space. Such a sparse nature of the spectrum occupancy has inspired the application of CS in CR communications. In this regard, several researchers have already applied the CS theory in various settings considering the sparsity in different domains. In this direction, this survey paper provides a detailed review of the state of the art related to the application of CS in CR communications. Starting with the basic principles and the main features of CS, it provides a classification of the main usage areas based on the radio parameter to be acquired by a wideband CR. Subsequently, we review the existing CSrelated works applied to different categories such as wideband sensing, signal parameter estimation and Radio Environment Map (REM) construction, highlighting the main benefits and the related issues. Furthermore, we present a generalized framework for constructing the REM in compressive settings. Finally, we conclude this survey paper with some suggested open research challenges and future directions.

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“…Here we extend the narrowband model from [10], [11] to a wideband model and derive a new wideband direction of arrival estimation method which is the novelty of this paper. The received wideband signal is first decomposed into a set of independent narrowband signals using Discrete Fourier Transform (DFT) ( [2], [3]) or narrowband filters ( [6]). For each of the different narrowband signals the covariance matrix is calculated and a sparse narrowband model based on individual sparsity is created.…”

Section: This Paper Is a Revised And Expanded Version Of The Paper Prmentioning

confidence: 99%

“…Section IV concludes the paper. DFT with a sufficient number of frequency bins as in [2], [3] or narrowband filters followed by additional conversion to baseband as in [6]. …”

Section: This Paper Is a Revised And Expanded Version Of The Paper Prmentioning

confidence: 99%