One-Bit compressive sampling with time-varying thresholds for multiple sinusoids

Gianelli, Christopher; Xu, Luzhou; Li, Jian; Stoica, Petre

doi:10.1109/camsap.2017.8313172

Cited by 32 publications

(20 citation statements)

References 23 publications

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“…Similarly, Gianelli et al [32] describe the properties of the maximum likelihood estimator (MLE) of the parameters of a noisy single-sinusoidal signal after binary quantization. The analysis is extended to the binary quantization of multiple sinusoids in [33], where a nonlinear least squares estimator is described to solve the problem. Finally, numerical methods for the estimation of the likelihood function may resort to iterative procedures such as the expectation-maximization approach [34]- [36].…”

Section: State Of the Artmentioning

confidence: 99%

Quick Estimation of Periodic Signal Parameters From 1-Bit Measurements

Carbone

Schoukens

Moschitta

2020

IEEE Trans. Instrum. Meas.

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Section: State Of the Artmentioning

confidence: 99%

Quick Estimation of Periodic Signal Parameters From 1-Bit Measurements

Carbone

Schoukens

Moschitta

2020

IEEE Trans. Instrum. Meas.

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“…For given ω, the above optimization problem is convex in { a k } K k=1 , { b k } K k=1 and λ. Therefore, for fixed ω, globally optimal methods can be employed to find the minimizing values of { a k } K k=1 , { b k } K k=1 and λ [26], [34]. With this fact in mind, the ML estimator can be summarized as follows.…”

Section: Maximum Likelihood Estimation and 1brelaxmentioning

confidence: 99%

“…Specifically, 1bMMRELAX is more than an order of magnitude faster than 1bRELAX, while maintaining similar sinusoidal parameter estimation accuracy. Regarding certain penalized/sparse algorithms proposed in the literature for sinusoidal parameter estimation from signed measurements [34], [36], we do not consider them in this comparative study because they are slow for large values of N and have worse performance than 1bRELAX [34]. Example 2: To further illustrate the resolution capability and estimation accuracy of 1bCLEAN, 1bMMRELAX and 1bRELAX, we consider a signal composed of two sinusoids with a small frequency separation and the same amplitudes and phases.…”

Section: B Sinusoidal Parameter Estimationmentioning

confidence: 99%

Sinusoidal Parameter Estimation From Signed Measurements Via Majorization–Minimization Based RELAX

Ren

Zhang

et al. 2019

IEEE Trans. Signal Process.

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We consider the problem of sinusoidal parameter estimation using signed observations obtained via one-bit sampling with fixed as well as time-varying thresholds. In a previous paper, a relaxation-based algorithm, referred to as 1bRELAX, has been proposed to iteratively maximize the likelihood function. However, the exhaustive search procedure used in each iteration of 1bRELAX is time-consuming. In this paper, we present a majorization-minimization (MM) based 1bRELAX algorithm, referred to as 1bMMRELAX, to enhance the computational efficiency of 1bRELAX. Using the MM technique, 1bMMRELAX maximizes the likelihood function iteratively using simple FFT operations instead of the more computationally intensive search used by 1bRELAX. Both simulated and experimental results are presented to show that 1bMMRELAX can significantly reduce the computational cost of 1bRELAX while maintaining its excellent estimation accuracy.

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“…andλ as functions of ω. The details on the implementation of the K-dimensional frequency search can be found in [24].…”

Section: A Maximum Likelihood Estimationmentioning

confidence: 99%

“…Note that as the number of sinusoids K and the size of the signed measurement matrix increase, this method can become computationally prohibitive and thus, more efficient algorithms, such as the relaxation based methods in [18], [24], may be considered.…”

Section: A Maximum Likelihood Estimationmentioning

confidence: 99%

RFI Mitigation for One-Bit UWB Radar Systems

Zhang

Ren

Gianelli

et al. 2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

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Radio frequency interference (RFI) mitigation is critical to the proper operation of ultra-wideband (UWB) radar systems since RFI can severely degrade the radar imaging capability and target detection performance. In this paper, we address the RFI mitigation problem for one-bit UWB radar systems. A one-bit UWB system obtains its signed measurements via a low-cost and high rate sampling scheme, referred to as the Continuous Time Binary Value (CTBV) technology. This sampling strategy compares the signal to a known threshold varying with slow-time and therefore can be used to achieve a rather high sampling rate and quantization resolution with rather simple and affordable hardware. This paper establishes a proper data model for the RFI sources and proposes a novel RFI mitigation method for the one-bit UWB radar system that uses the CTBV sampling technique. Specifically, we first model the RFI sources as a sum of sinusoids with frequencies fixed during the coherent processing interval (CPI) and we exploit the sparsity of the RFI spectrum. We extend a majorization-minimization based 1bRELAX algorithm, referred to as 1bMMRELAX, to estimate the RFI source parameters from the signed measurements obtained by using the CTBV sampling strategy. We also devise a new fast frequency initialization method based on the Alternating Direction Method of Multipliers (ADMM) methodology for the extended 1bMMRELAX algorithm to significantly improve its computational efficiency. Moreover, an ADMM-based sparse method is introduced to recover the desired radar echoes using the estimated RFI parameters. Both simulated and experimental results are presented to demonstrate that our proposed algorithm outperforms the existing digital integration method, especially for severe RFI cases.

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One-Bit compressive sampling with time-varying thresholds for multiple sinusoids

Cited by 32 publications

References 23 publications

Quick Estimation of Periodic Signal Parameters From 1-Bit Measurements

Quick Estimation of Periodic Signal Parameters From 1-Bit Measurements

Sinusoidal Parameter Estimation From Signed Measurements Via Majorization–Minimization Based RELAX

RFI Mitigation for One-Bit UWB Radar Systems

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