Direction-of-Arrival Estimation Based on Quantized Matrix Recovery

Huang, Xiaodong; Bi, Suzhi; Liao, Bin

doi:10.1109/lcomm.2019.2957083

Cited by 12 publications

(5 citation statements)

References 20 publications

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“…The studies in [45][46][47][48][49] presuppose the deterministic signal model. The authors in [45] developed an algorithm for reconstruction of the unquantized array measurements from one-bit samples followed by MUSIC to determine DOAs. The ML estimation was deployed in [46] for finding DoAs from one-bit data.…”

Section: A Relevant Workmentioning

confidence: 99%

“…where E(θ1), E(θ2) and E(θ1, θ2) are given in (52) and (45). The analytical expression on the right-hand side of (63) enables us to predict the minimum source separation required for achieving a particular probability of resolution.…”

Section: E Resolution Probabilitymentioning

confidence: 99%

“…The problem of DoA estimation from one-bit quantized data has been studied in the literature presuming both the deterministic signal model [6] and the stochastic signal model [7]. The studies in [45][46][47][48][49] presuppose the deterministic signal model. The authors in [45] developed an algorithm for reconstruction of the unquantized array measurements from one-bit samples followed by MUSIC to determine DOAs.…”

Section: A Relevant Workmentioning

confidence: 99%

See 2 more Smart Citations

One-Bit DoA Estimation via Sparse Linear Arrays

Sedighi

Shankar

Soltanalian

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Direction of Arrival (DoA) estimation using Sparse Linear Arrays (SLAs) has recently gained considerable attention in array processing thanks to their capability to provide enhanced degrees of freedom in resolving uncorrelated source signals. Additionally, deployment of onebit Analog-to-Digital Converters (ADCs) has emerged as an important topic in array processing, as it offers both a low-cost and a low-complexity implementation. In this paper, we study the problem of DoA estimation from one-bit measurements received by an SLA. Specifically, we first investigate the identifiability conditions for the DoA estimation problem from one-bit SLA data and establish an equivalency with the case when DoAs are estimated from infinite-bit unquantized measurements. Towards determining the performance limits of DoA estimation from one-bit quantized data, we derive a pessimistic approximation of the corresponding Cramér-Rao Bound (CRB). This pessimistic CRB is then used as a benchmark for assessing the performance of one-bit DoA estimators. We also propose a new algorithm for estimating DoAs from one-bit quantized data. We investigate the analytical performance of the proposed method through deriving a closed-form expression for the covariance matrix of the asymptotic distribution of the DoA estimation errors and show that it outperforms the existing algorithms in the literature. Numerical simulations are provided to validate the analytical derivations and corroborate the resulting performance improvement.

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Section: A Relevant Workmentioning

confidence: 99%

Section: E Resolution Probabilitymentioning

confidence: 99%

Section: A Relevant Workmentioning

confidence: 99%

See 1 more Smart Citation

One-Bit DoA Estimation via Sparse Linear Arrays

Sedighi

Shankar

Soltanalian

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…As for radar applications with low-resolution ADCs/DACs, the works mainly concentrated on DoA estimation. For instance, an algorithm is proposed in [29] to reconstruct the unquantized measurements from one-bit samples, followed by the MUSIC method to estimate the DOAs of targets. In addition, The maximum likelihood (ML) is developed in [30] for finding DoA and velocity of a target from one-bit data.…”

Section: Introductionmentioning

confidence: 99%

Relative Entropy-Based Constant-Envelope Beamforming for Target Detection in Large-Scale MIMO Radar With Low-Resoultion ADCs

Cheng¹,

Wu²,

Wang³

et al. 2023

Preprint

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Hybrid digital/analog architecture and lowresolution analog-to-digital/digital-to-analog converters (ADCs /DACs) are two low-cost implementations for large-scale millimeter wave (mmWave) systems. In this paper, we investigate the problem of constant-envelope transmit beamforming for large-scale multiple-input multiple-output (MIMO) radar system, where the transmit array adopts a hybrid digital/analog architecture with a small number of RF chains and the receive array adopts a fully digital architecture with low-resolution ADCs. We derive the relative entropy between the probability density functions associated with the two test hypotheses under low-resolution ADCs. We formulate our optimization problem by maximizing the relative entropy, subject to the constant envelope and orthogonality constraints. To suboptimally solve the resultant problem, a two-stage framework is developed. In the first stage, we optimize the transmit power at the directions of the target and clutter. In the second stage, an efficient iterative algorithm based on majorization-minimization is presented to obtain the constant-envelope beamformer according to the attained transmit power. Specifically, we apply a quadratic function as the minorizer, leading to a low-complexity solution at each iteration. In addition, to further facilitate low-cost implementation of the constant-envelope beamformer, we consider the problem of one-bit beamforming design and propose an efficient iterative method based on the Nesterov-like gradient method to solve it. Numerical simulations are provided to demonstrate the effectiveness of the proposed schemes.

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“…However, use of highresolution ADCs is typically expensive and power-hungry [16]. Hence, to reduce energy consumption and production costs, DoA estimation with binary measurements collected by onebit ADCs has been recently proposed and discussed in the literature [17][18][19][20][21][22][23][24][25][26][27]. One-bit ADCs represent each sample of the analog array observations with only a single bit offering, an exceedingly high sampling rate at a low production cost and very low power consumption [16].…”

Section: Introductionmentioning

confidence: 99%

DoA Estimation Using Low-Resolution Multi-Bit Sparse Array Measurements

Sedighi

Shankar

Soltanalian

et al. 2021

IEEE Signal Process. Lett.

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This letter studies the problem of Direction of Arrival (DoA) estimation from low-resolution few-bit quantized data collected by Sparse Linear Array (SLA). In such cases, contrary to the one-bit quantization case, the well known arcsine law cannot be employed to estimate the covaraince matrix of unquantized array data. Instead, we develop a novel optimizationbased framework for retrieving the covaraince matrix of unquantized array data from low-resolution few-bit measurements. The MUSIC algorithm is then applied to an augmented version of the recovered covariance matrix to find the source DoAs. The simulation results show that increasing the sampling resolution to 2 or 4 bits per samples could significantly increase the DoA estimation performance compared to the one-bit sampling regime while the power consumption and implementation costs is still much lower in comparison to the high-resolution sampling implementations.

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Direction-of-Arrival Estimation Based on Quantized Matrix Recovery

Cited by 12 publications

References 20 publications

One-Bit DoA Estimation via Sparse Linear Arrays

One-Bit DoA Estimation via Sparse Linear Arrays

Relative Entropy-Based Constant-Envelope Beamforming for Target Detection in Large-Scale MIMO Radar With Low-Resoultion ADCs

DoA Estimation Using Low-Resolution Multi-Bit Sparse Array Measurements

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