Compressed sensing for DOA estimation with fewer receivers than sensors

Gu, Jian-Feng; Zhu, Wei-Ping; Swamy, M.N.S.

doi:10.1109/iscas.2011.5937922

Cited by 28 publications

(19 citation statements)

References 13 publications

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“…while the state variable update stays the same as in (8). So the proposed stochastic gradient descent consists of iteratively carrying out (9) and (8). Now, we outline how to apply the iterative approach explained above to the problem at hand.…”

Section: Stochastic Gradient Descent-based Designmentioning

confidence: 99%

Combining Matrix Design for 2D DoA Estimation with Compressive Antenna Arrays Using Stochastic Gradient Descent

Pawar

Semper

Römer

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Recently, compressive antenna arrays have been considered for direction of arrival (DoA) estimation with reduced hardware complexity. By utilizing compressive sensing, such arrays employ a linear combining network to combine signals from a larger set of antenna elements in the analog RF domain. In this paper, we develop a design approach based on the minimization of error between spatial correlation function (SCF) of the compressive and the uncompressed array resulting in the estimation performance of the two arrays to be as close as possible. The proposed design is based on grid-free stochastic gradient descent (SGD) optimization. In addition to a low computational cost for the proposed method, we show numerically that the resulting combining matrices perform better than the ones generated by a previous approach and combining matrices generated from a Gaussian ensemble.

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Section: Stochastic Gradient Descent-based Designmentioning

confidence: 99%

Combining Matrix Design for 2D DoA Estimation with Compressive Antenna Arrays Using Stochastic Gradient Descent

Pawar

Semper

Römer

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…With the CSA-DoA [15,16,17] method, analog sensor array data are randomly projected onto a lower dimensional subspace in the analog domain before being converted into digital samples using an analog to digital converter (ADC). As such, fewer ADCs are required.…”

Section: Introductionmentioning

confidence: 99%

Wireless Sensor Array Network DoA Estimation from Compressed Array Data via Joint Sparse Representation

Yin

Luo

et al. 2016

Sensors

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A compressive sensing joint sparse representation direction of arrival estimation (CSJSR-DoA) approach is proposed for wireless sensor array networks (WSAN). By exploiting the joint spatial and spectral correlations of acoustic sensor array data, the CSJSR-DoA approach provides reliable DoA estimation using randomly-sampled acoustic sensor data. Since random sampling is performed at remote sensor arrays, less data need to be transmitted over lossy wireless channels to the fusion center (FC), and the expensive source coding operation at sensor nodes can be avoided. To investigate the spatial sparsity, an upper bound of the coherence of incoming sensor signals is derived assuming a linear sensor array configuration. This bound provides a theoretical constraint on the angular separation of acoustic sources to ensure the spatial sparsity of the received acoustic sensor array signals. The Cramtruee´r–Rao bound of the CSJSR-DoA estimator that quantifies the theoretical DoA estimation performance is also derived. The potential performance of the CSJSR-DoA approach is validated using both simulations and field experiments on a prototype WSAN platform. Compared to existing compressive sensing-based DoA estimation methods, the CSJSR-DoA approach shows significant performance improvement.

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“…It has recently been proposed to apply CS to the acquisition of the RF signals that are used for DOA estimation [14,IS]. In par ticular, the CS paradigm can be implemented in the spatial domain by employing M passive antenna elements that are combined into a smaller number of m < M channels using an analog combining net- work.…”

Section: Introductionmentioning

confidence: 99%

On the design of the measurement matrix for Compressed Sensing based DOA estimation

Ibrahim

Roemer

Galdo

2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper we investigate the design of the mea surement matrix for applying Compressed Sensing (CS) to the problem of Direction Of Arrival (DOA) estimation with antenna arrays. So far, it has been suggested to choose the coefficients randomly since this choice satisfies the restricted isometry prop erty (RIP) with a high probability. We demonstrate that this choice may be sub-optimal since it can result in an effective ar ray with significant sidelobes and blind spots. The sidelobes are especially problematic when we use correlation-based greedy al gorithms for the sparse recovery stage as they can lead to de tecting spurious peaks. To address the problem , we introduce a design methodology for constructing a measurement matrix that mitigates these unwanted effects to achieve a better DOA estima tion performance. Numerical results demonstrate the usefulness of our design.

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Compressed sensing for DOA estimation with fewer receivers than sensors

Cited by 28 publications

References 13 publications

Combining Matrix Design for 2D DoA Estimation with Compressive Antenna Arrays Using Stochastic Gradient Descent

Combining Matrix Design for 2D DoA Estimation with Compressive Antenna Arrays Using Stochastic Gradient Descent

Wireless Sensor Array Network DoA Estimation from Compressed Array Data via Joint Sparse Representation

On the design of the measurement matrix for Compressed Sensing based DOA estimation

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