DOA Estimation From One-Bit Compressed Array Data via Joint Sparse Representation

Yu, Kai; Zhang, Yimin D.; Bao, Ming; Hu, Yu-Hen; Wang, Zhi

doi:10.1109/lsp.2016.2572307

Cited by 65 publications

(17 citation statements)

References 21 publications

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“…By exploiting the signal sparsity in space domain, the one-bit DOA estimation problem is formulated as a sparse recovery problem in [16]. On the other hand, the signal sparsity in both space and frequency domains has been utilized in [17]. More recently, a one-bit DOA estimator based on a fixed point continuation reconstruction algorithm is devised in [18].…”

Section: Introductionmentioning

confidence: 99%

One-Bit MUSIC

Huang

Liao

2019

IEEE Signal Process. Lett.

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In this letter, we consider the problem of directionof-arrival (DOA) estimation with one-bit quantized array measurements. With analysis, it is shown that, under mild conditions the one-bit covariance matrix can be approximated by the sum of a scaled unquantized covariance matrix and a scaled identity matrix. Although the scaling parameters unknown because of the extreme quantization, they do not affect the subspace-based DOA estimators. Specifically, the signal and noise subspaces can be straightforwardly determined through the eigendecomposition of the one-bit covariance matrix, without pre-processing such as unquantized covariance matrix reconstruction. With so-obtained subspaces, the most classical multiple signal classification (MU-SIC) technique can be applied to determine the signal DOAs. The resulting method is thus termed as one-bit MUSIC. Thanks to the simplicity of this method, it can be very easily implemented in practical applications, whereas the DOA estimation performance is comparable to the case with unquantized covariance matrix reconstruction, as demonstrated by various simulations.Index Terms-One-bit quantization, direction-of-arrival (DOA) estimation, multiple signal classification (MUSIC).

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Section: Introductionmentioning

confidence: 99%

One-Bit MUSIC

Huang

Liao

2019

IEEE Signal Process. Lett.

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“…With compressive sensing, it only needs 20% of the data to recover the real signal, such as temperature and light [ 15 ]. Furthermore, conducting DOA estimation using 20% data is also sufficient [ 38 ]. To verify the effectiveness of our CS-based method, we first conduct simulations over a link in the transitional region, in which the packet reception rates (single data transmission) are 20%.…”

Section: Performance Evaluationmentioning

confidence: 99%

Efficient Sparse Signal Transmission over a Lossy Link Using Compressive Sensing

Cao

et al. 2015

Sensors

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Reliable data transmission over lossy communication link is expensive due to overheads for error protection. For signals that have inherent sparse structures, compressive sensing (CS) is applied to facilitate efficient sparse signal transmissions over lossy communication links without data compression or error protection. The natural packet loss in the lossy link is modeled as a random sampling process of the transmitted data, and the original signal will be reconstructed from the lossy transmission results using the CS-based reconstruction method at the receiving end. The impacts of packet lengths on transmission efficiency under different channel conditions have been discussed, and interleaving is incorporated to mitigate the impact of burst data loss. Extensive simulations and experiments have been conducted and compared to the traditional automatic repeat request (ARQ) interpolation technique, and very favorable results have been observed in terms of both accuracy of the reconstructed signals and the transmission energy consumption. Furthermore, the packet length effect provides useful insights for using compressed sensing for efficient sparse signal transmission via lossy links.

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“…[10] derived the covariance matrix of one-bit measurements by using the arcsine theorem of statistical theory and then achieved accurate DOA estimation. Other improved algorithms, can be found in [11], [12] and [13]. Unfortunately, a massive number of physical antennas is still need to obtain high estimation accuracy.…”

Section: Introductionmentioning

confidence: 99%