Comparison of MUSIC Variants for Sparse Arrays

Adhikari, Kaushallya; Drozdenko, Benjamin

doi:10.1109/naecon46414.2019.9058169

Cited by 11 publications

(7 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also is it robust to sensor failure? [81,82] Generally, the aperture of the array is directly related to the maximum number of resolvable sources, and the intersensor spacing is related to possible aliasing of some angle estimates; more details in [27] and references therein. If the linear sparse array can provide the same main lobe beamwidth as a ULA, then it should be able to resolve the same number of sources as the ULA.…”

Section: Two-dimensional Doa Estimation Essentialsmentioning

confidence: 99%

“…If the linear sparse array can provide the same main lobe beamwidth as a ULA, then it should be able to resolve the same number of sources as the ULA. Alternatively, if the linear sparse array has a continuous (hole-free) coarray ULA, then it should be able to resolve the same number of sources as a ULA similar to the coarray [27]. Similar ideas are used for 2D-DoA estimation, where it is preferred to get as close as possible to uniform rectangular coarray.…”

Section: Two-dimensional Doa Estimation Essentialsmentioning

confidence: 99%

“…On the general topic of estimation in signal processing, Zoubir et al [23] presented a tutorial which includes valuable information on DoA estimation, but does not describe specific array geometries. There are also less related comparison studies by Kiani and Pezeshk [24], Khedekar and Mukhopadhyay [25], Ahmed et al [26], Adhikari and Drozdenko [27], and reviews in other areas related to DoA estimation [28,29].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Review of Sparse Sensor Arrays for Two-Dimensional Direction-of-Arrival Estimation

et al. 2021

View full text Add to dashboard Cite

arrays are fundamental to localization applications. Specifically spares arrays can provide superior direction-of-arrival (DoA) estimation performance with limited number of sensors. There has been increased interest in the research community in designing 2D sparse arrays with performance improvement and complexity reduction. The research efforts are uncoordinated resulting in some repetitions and sometimes conflicting claims. After introducing 2D sparse arrays and their importance, this paper establishes the 2D-DoA estimation model and consolidates the performance metrics. An extensive literature overview of sparse arrays for 2D-DoA estimation is presented with an attempt to categorize existing works. The examined arrays include parallel arrays, L-shaped, V-shaped, hourglass, thermos, nested planer, and coprime planner, to name a few. Existing designs are compared in terms of required number of sensors, degrees of freedom (DOF), algorithm used, associated complexity and aperture size. The focus is on describing the sparse arrays, yet some specific details on DoA estimation algorithms are provided for selected array geometries. Fundamental problems of 2D-DoA estimation are outlined and existing solutions to alleviate these problems are discussed. This should be useful in predicting the estimation performance and required complexity; thus, aiding the decision of selecting a sensor geometry for DoA estimation. This review serves as a starting point for researchers interested in exploring or designing new 2D sparse arrays. It also helps to identify the gaps in the field and avoids unnecessary minor design modifications.

show abstract

Section: Two-dimensional Doa Estimation Essentialsmentioning

confidence: 99%

Section: Two-dimensional Doa Estimation Essentialsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Sparse Sensor Arrays for Two-Dimensional Direction-of-Arrival Estimation

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Various aspects of these two processors are compared in depth in [15], [17], [19], with product processing having the advantage of being less vulnerable to crossterms than min processing [15], [17], [20]. Other methods that have been popular for coprime and nested arrays are subspace-based algorithms such as MUSIC and ESPRIT [5], [7], [21]- [29]. For lattice-imposed planar arrays, these methods exhibit very low resolution when compared with a PPO due a narrow filled coarray [30].…”

Section: Introductionmentioning

confidence: 99%

Product Processing for Tapered Sparse Arrays

Sartori¹,

Adhikari²

2021

Preprint

Self Cite

View full text Add to dashboard Cite

The product processor output has recently been introduced as a spatial power spectral density estimate, unifying product arrays such as coprime arrays, nested arrays, and standard uniform line arrays. The expected value and covariance function of this estimate for a white Gaussian process was derived in previous work over these various array configurations. However, this prior work used a uniform taper in all cases. In this paper, we show that when product arrays are windowed with non-uniform tapers, the expected value of the product processor output is the convolution of the true spatial power spectral density with the spatial Fourier transform of the difference coarray. This expected value makes a Fourier transform pair with a spatial autocorrelation estimate obtained by windowing the true autocorrelation function. We also derive the covariance function of the product processor output with non-uniform tapers, and compare these derived statistics for the aforementioned array geometries. Also, in prior work, the moments were provided only for linear arrays; this paper extends the estimation results to multidimensional arrays.

show abstract

“…(1) conventional beamforming-based algorithms and (2) eigenanalysis algorithms [2]- [9]. Algorithms that apply conventional beamforming (CBF) to individual subarrays include product processing and min processing [2]- [8], [10]- [15], [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Minimum Norm Method for Linear and Planar Sparse Arrays

Trosclair¹,

Adhikari²

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Coprime and nested arrays are sparse arrays with enhanced degrees of freedom, which can be exploited in direction of arrival estimation using algorithms such as product processing, min processing, and MUSIC. This paper applies the minimum norm method for direction of arrival estimation. Comparison of the root mean squared errors and probabilities of resolution of the minimum norm method with MUSIC for a given linear coprime or nested array demonstrates the superiority of the minimum norm method. Specifically, minimum norm method exhibits lower mean squared error, narrower peaks at the locations of the true sources, and a lower noise floor in the spatial spectral estimate. This work also formulates two different minimum norm methods for planar sparse arrays: direct and linear. Comparison of the linear minimum norm method with the linear MUSIC for planar arrays also demonstrates higher accuracy of the minimum norm method.

show abstract

Comparison of MUSIC Variants for Sparse Arrays

Cited by 11 publications

References 24 publications

A Review of Sparse Sensor Arrays for Two-Dimensional Direction-of-Arrival Estimation

A Review of Sparse Sensor Arrays for Two-Dimensional Direction-of-Arrival Estimation

Product Processing for Tapered Sparse Arrays

Minimum Norm Method for Linear and Planar Sparse Arrays

Contact Info

Product

Resources

About