DOA estimation using one-bit quantized measurements

Bar-Shalom, Ofer; Weiss, A.J.

doi:10.1109/taes.2002.1039405

Cited by 166 publications

(85 citation statements)

References 12 publications

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“…where ℜ{z} and ℑ{z} denote the real part and imaginary part of a complex-valued number z, respectively. As a pioneer work for one-bit DOA estimation, Bar-shalom and Weiss [9] proposed to reconstruct the unqunatized covari-…”

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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“…Thus, we can interpret the problem as DOA estimation in one-bit ADCs. In [8], DOA estimators for one-bit ADCs were proposed using the arcsine law, which relates the covariance matrices of the quantized and unquantized received signals using arcsine [12], [13]. In this paper, motivated by the approach of [8], we propose the MIPS DOA estimate of θ 0 .…”

Section: A Doa Estimatormentioning

confidence: 99%

“…The one-bit spatial smoothing MUSIC (SS MUSIC) proposed in [9], which also relies on the arcsine law, considers DOA estimation in sparse arrays. The drawback of the DOA estimators in [8], [9] is that many independent observations are needed to reconstruct the covariance matrix. The work in [10] proposed the gridless one-bit DOA estimator based on the support vector machine (SVM), which is cumbersome in practice since the coarse DOA estimate based on the SVM should be refined using the Taylor expansion to obtain the gridless DOA estimate.…”

Section: Introductionmentioning

confidence: 99%

Dominant Channel Estimation via MIPS for Large-Scale Antenna Systems with One-Bit ADCs

Kim

Lee

Choi

2018

2018 IEEE Global Communications Conference (GLOBECOM)

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In large-scale antenna systems, using one-bit analogto-digital converters (ADCs) has recently become important since they offer significant reductions in both power and cost. However, in contrast to high-resolution ADCs, the coarse quantization of one-bit ADCs results in an irreversible loss of information. In the context of channel estimation, studies have been developed extensively to combat the performance loss incurred by onebit ADCs. Furthermore, in the field of array signal processing, direction-of-arrival (DOA) estimation combined with one-bit ADCs has gained growing interests recently to minimize the estimation error. In this paper, a channel estimator is proposed for one-bit ADCs where the channels are characterized by their angular geometries, e.g., uniform linear arrays (ULAs). The goal is to estimate the dominant channel among multiple paths. The proposed channel estimator first finds the DOA estimate using the maximum inner product search (MIPS). Then, the channel fading coefficient is estimated using the concavity of the log-likelihood function. The limit inherent in one-bit ADCs is also investigated, which results from the loss of magnitude information.

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“…Particularly, 1-bit sampling and signal recovery have been extensively studied in the context of recently introduced one-bit Compressive Sensing (CS) problem as well [20]- [28]. More specifically, the task of recovering the frequency and phase of temporal and spatial sinusoidal signals utilizing only 1-bit information with fixed quantization thresholds has been extensively investigated in [16] and [17], respectively. On the other hand, the recovery of general signals with highdimensional parameters from sign comparison information were considered in [18] and [19].…”

Section: Introductionmentioning

confidence: 99%

Signal Recovery From 1-Bit Quantized Noisy Samples via Adaptive Thresholding

Khobahi

Soltanalian

2018

2018 52nd Asilomar Conference on Signals, Systems, and Computers

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In this paper, we consider the problem of signal recovery from 1-bit noisy measurements. We present an efficient method to obtain an estimation of the signal of interest when the measurements are corrupted by white or colored noise. To the best of our knowledge, the proposed framework is the pioneer effort in the area of 1-bit sampling and signal recovery in providing a unified framework to deal with the presence of noise with an arbitrary covariance matrix including that of the colored noise. The proposed method is based on a constrained quadratic program (CQP) formulation utilizing an adaptive quantization thresholding approach, that further enables us to accurately recover the signal of interest from its 1-bit noisy measurements. In addition, due to the adaptive nature of the proposed method, it can recover both fixed and time-varying parameters from their quantized 1-bit samples.

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DOA estimation using one-bit quantized measurements

Cited by 166 publications

References 12 publications

One-Bit MUSIC

One-Bit MUSIC

Dominant Channel Estimation via MIPS for Large-Scale Antenna Systems with One-Bit ADCs

Signal Recovery From 1-Bit Quantized Noisy Samples via Adaptive Thresholding

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