Range Estimation and Range-Doppler Imaging Using Signed Measurements in LFMCW Radar

Zhang, Rong; Li, Changheng; Li, Jian; Wang, Gang

doi:10.1109/taes.2019.2907395

Cited by 17 publications

(6 citation statements)

References 29 publications

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“…Without loss of generality, we consider an LFMCW singleinput single-output radar. A simplified model for the received signal (after dechirping and pre-processing) is as follows (see e.g., [11]):…”

Section: A Lfmcw Radarmentioning

confidence: 99%

“…One-bit sampling is a promising technique to mitigate the aforementioned ADC problems because of its low cost and low power consumption advantages. Due to these attractive properties, one-bit sampling has been considered for radar sensing [11,12], spectral estimation [13][14][15], as well as massive MIMO communications [16][17][18]. The conventional one-bit sampling considered in, e.g., [19][20][21], compares the original signal with a zero threshold, which results in a loss of signal amplitude information.…”

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confidence: 99%

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Weighted SPICE Algorithms for Range-Doppler Imaging Using One-Bit Automotive Radar

Shang,

Li,

Stoica

2021

Preprint

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We consider the problem of range-Doppler imaging using one-bit automotive LFMCW 1 or PMCW radar that utilizes one-bit ADC sampling with time-varying thresholds at the receiver. The one-bit sampling technique can significantly reduce the cost as well as the power consumption of automotive radar systems. We formulate the one-bit LFMCW/PMCW radar range-Doppler imaging problem as one-bit sparse parameter estimation. The recently proposed hyperparameter-free (and hence user friendly) weighted SPICE algorithms, including SPICE, LIKES, SLIM and IAA, achieve excellent parameter estimation performance for data sampled with high precision. However, these algorithms cannot be used directly for one-bit data. In this paper we first present a regularized minimization algorithm, referred to as 1bSLIM, for accurate range-Doppler imaging using onebit radar systems. Then, we describe how to extend the SPICE, LIKES and IAA algorithms to the one-bit data case, and refer to these extensions as 1bSPICE, 1bLIKES and 1bIAA. These onebit hyperparameter-free algorithms are unified within the one-bit weighted SPICE framework. Moreover, efficient implementations of the aforementioned algorithms are investigated that rely heavily on the use of FFTs. Finally, both simulated and experimental examples are provided to demonstrate the effectiveness of the proposed algorithms for range-Doppler imaging using one-bit automotive radar systems.Index Terms-One-bit sampling, time-varying threshold, automotive radar, range-Doppler imaging, hyperparameter-free sparse parameter estimation, weighted SPICE for one-bit data. I. INTRODUCTIONM ILLIMETER wave radar is widely used in diverse applications including advanced driver assistance automotive systems and fully autonomous vehicles [1]. Compared with other sensing systems, such as cameras, lidars and ultrasonics, radar systems can provide better performance, especially in poor lighting or adverse weather conditions [2].The most commonly used probing waveform in existing commercial automotive radar systems is the LFMCW waveform, which reduces the system cost by allowing lowrate ADCs at the radar receivers. An LFMCW radar system transmits a series of chirps, which are reflected by the targets

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Section: A Lfmcw Radarmentioning

confidence: 99%

mentioning

confidence: 99%

Weighted SPICE Algorithms for Range-Doppler Imaging Using One-Bit Automotive Radar

Shang,

Li,

Stoica

2021

Preprint

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“…The expectation maximization (EM) algorithm is incorporated to estimate the noise variance automatically for bit-depth greater than 1. In [22], based on signed measurements of LFMCW radar, range estimation and range-Doppler imaging are studied through the maximum likelihood approach. In order to reduce the computational complexity, a relaxation-based approach, referred to as the One-bit RELAX algorithm, is proposed, which uses the grid refinement to avoid grid mismatch issue.…”

Section: A Related Workmentioning

confidence: 99%

Gridless variational Bayesian inference of line spectral from quantized samples

2021

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Efficient estimation of line spectral from quantized samples is of significant importance in information theory and signal processing, e.g., channel estimation in energy efficient massive MIMO systems and direction of arrival estimation. The goal of this paper is to recover the line spectral as well as its corresponding parameters including the model order, frequencies and amplitudes from heavily quantized samples. To this end, we propose an efficient grid-less Bayesian algorithm named VALSE-EP, which is a combination of the variational line spectral estimation (VALSE) and expectation propagation (EP). The basic idea of VALSE-EP is to iteratively approximate the challenging quantized model of line spectral estimation as a sequence of simple pseudo unquantized models so that the VALSE can be applied. Note that the noise in the pseudo linear model is heteroscedastic, i.e., different components having different variances, and a variant of the VALSE is re-derived to obtain the final VALSE-EP. Moreover, to obtain a benchmark performance of the proposed algorithm, the Cramér Rao bound (CRB) is derived. Finally, numerical experiments on both synthetic and real data are performed, demonstrating the near CRB performance of the proposed VALSE-EP for line spectral estimation from quantized samples.

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“…There have been several studies on radar systems that employ few-bit ADCs. For example, references 3,4,5 look at identifying targets and estimating parameters for frequency modulated continuous wave (FMCW) radars that use few-bit quantization. The issue of employing variable time thresholds for one-bit radar sensing is also covered in references 6,7 .…”

Section: Introductionmentioning

confidence: 99%

Sparse Bayesian learning-based parameter estimation for PMCW-MIMO radars with few-bit quantization

You,

Xi,

Chen

2024

Fifteenth International Conference on Signal Processing Systems (ICSPS 2023)

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In this work, we consider target parameter estimation of phase modulated continuous wave (PMCW) multiple-input multiple-output (MIMO) radar systems with few-bit quantization observations. We formulate the parameter problem as a sparse recovery problem and then jointly estimate the targets' amplitudes, time delays, Doppler shifts, and directions under the generalized sparse Bayesian learning (Gr-SBL) framework. Under this framework, this proposed algorithm decomposes the original nonlinear problem into a sequence of standard linear model (SLM) problems. Therefore, we can apply the standard sparse Bayesian learning (SBL) algorithm to solve the above SLM. Numerical results demonstrate the effectiveness of the proposed Gr-SBL algorithm for the parameter estimation of a PMCW MIMO radar systems with few-bit analog-to-digital converters (ADCs).

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Range Estimation and Range-Doppler Imaging Using Signed Measurements in LFMCW Radar

Cited by 17 publications

References 29 publications

Weighted SPICE Algorithms for Range-Doppler Imaging Using One-Bit Automotive Radar

Weighted SPICE Algorithms for Range-Doppler Imaging Using One-Bit Automotive Radar

Gridless variational Bayesian inference of line spectral from quantized samples

Sparse Bayesian learning-based parameter estimation for PMCW-MIMO radars with few-bit quantization

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