Gridless Parameter Estimation for One-Bit MIMO Radar With Time-Varying Thresholds

Xi, Feng; Xiang, Yijian; Chen, Shengyao; Nehorai, Arye

doi:10.1109/tsp.2020.2970343

Cited by 58 publications

(33 citation statements)

References 62 publications

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“…However, the unknown N m has no effect on the DOA estimation since the following proposed method is based on the normalized covariance matrix R x S,m . If N m is required, we can utilize time-varying threshold-based one-bit measurement methods as in [33] and [34].…”

Section: B Reconstruction Of Unquantized Covariance Matrixmentioning

confidence: 99%

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Direction Finding of Electromagnetic Sources on a Sparse Cross-Dipole Array Using One-Bit Measurements

et al. 2020

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Sparse array arrangement has been widely used in vector-sensor arrays because of increased degree-of-freedoms for identifying more sources than sensors. For large-size sparse vector-sensor arrays, one-bit measurements can further reduce the receiver system complexity by using low-resolution ADCs. In this paper, we present a sparse cross-dipole array with one-bit measurements to estimate Direction of Arrivals (DOA) of electromagnetic sources. Based on the independence assumption of sources, we establish the relation between the covariance matrix of one-bit measurements and that of unquantized measurements by Bussgang Theorem. Then we develop a Spatial-Smooth MUSIC (SS-MUSIC) based method, One-Bit MUSIC (OB-MUSIC), to estimate the DOAs. By jointly utilizing the covariance matrices of two dipole arrays, we find that OB-MUSIC is robust against polarization states. We also derive the Cramer-Rao bound (CRB) of DOA estimation for the proposed scheme. Furthermore, we theoretically analyze the applicability of the independence assumption of sources, which is the fundamental of the proposed and other typical methods, and verify the assumption in typical communication applications. Numerical results show that, with the same number of sensors, one-bit sparse cross-dipole arrays have comparable performance with unquantized uniform linear arrays and thus provide a compromise between the DOA estimation performance and the system complexity.INDEX TERMS Cross-dipole, sparse arrays, one-bit measurements, Cramer-Rao bound, DOA estimation.

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Section: B Reconstruction Of Unquantized Covariance Matrixmentioning

confidence: 99%

“…Using the results in [34] and [33], the Fisher Information Matrix (FIM) for one-bit data measured by snapshot z is where…”

Section: √ π Xmentioning

confidence: 99%

Direction Finding of Electromagnetic Sources on a Sparse Cross-Dipole Array Using One-Bit Measurements

et al. 2020

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“…This introduces difficulties in the recovery of the signal amplitude. On the other hand, recent works have shown enhanced estimation performance for the signal parameters by employing time-varying thresholds [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Modified Arcsine Law for One-Bit Sampled Stationary Signals with Time-Varying Thresholds

Eamaz

Yeganegi

Soltanalian

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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One-bit quantization has attracted considerable attention in signal processing for communications and sensing. The arcsine law is a useful relation often used to estimate the normalized covariance matrix of zero-mean stationary input signals when they are sampled by one-bit analog-to-digital converters (ADCs)-practically comparing the signals with a given threshold level. This relation, however, only considers a zero threshold which can cause a remarkable information loss. For the first time in the literature, this paper introduces an approach to extending the arcsine law to the case where one-bit ADCs apply time-varying thresholds. In particular, the proposed method is shown to accurately recover the variance and autocorrelation of the stationary signals of interest.

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“…These methods have been shown to achieve finer resolution than conventional delay-and-sum methods by utilizing the sparsity of radar scene and assuming that many-bit ADCs are applied. Recent works also consider the sparse estimation algorithms with coarsely quantized observations and show that excellent estimation performance can be achieved [11][12][13][14][15][16][17][18]. However, most of the works only consider 1-bit quantization and do not This work was supported in part by the NSF under Grant No.…”

Section: Introductionmentioning

confidence: 99%

Sparse Parameter Estimation for PMCW MIMO Radar Using Few-Bit ADCs

Wong

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this work, we consider target parameter estimation of phase-modulated continuous-wave (PMCW) multiple-input multiple-output (MIMO) radars with few-bit analog-to-digital converters (ADCs). We formulate the estimation problem as a sparse signal recovery problem and modify the fast iterative shrinkage-thresholding algorithm (FISTA) to solve it. The 2,1 -norm is adopted to promote the sparsity in the range-Doppler-angle domain. Simulation results show that using few-bit ADCs can achieve comparable performance to many-bit ADCs when targets are widely separated. However, if targets are spaced closely, performance losses can occur when 1-bit ADCs are applied.

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Gridless Parameter Estimation for One-Bit MIMO Radar With Time-Varying Thresholds

Cited by 58 publications

References 62 publications

Direction Finding of Electromagnetic Sources on a Sparse Cross-Dipole Array Using One-Bit Measurements

Direction Finding of Electromagnetic Sources on a Sparse Cross-Dipole Array Using One-Bit Measurements

Modified Arcsine Law for One-Bit Sampled Stationary Signals with Time-Varying Thresholds

Sparse Parameter Estimation for PMCW MIMO Radar Using Few-Bit ADCs

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