Code Optimization and Angle-Doppler Imaging for ST-CDM LFMCW MIMO Radar Systems

Shang, Xiaolei; Cheng, Yuanbo

doi:10.1109/lsp.2022.3210834

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…Subsequently, [27] derived the closed-form expression for the Cramér-Rao bound (CRB) and demonstrated that the subspace-based methods can be utilized without any modification when using a uniform linear array (ULA). In [28,29], the authors performed a CRB study in PMCW MIMO radar. To address the channel estimation problem, a gridless atomic norm based approach was proposed in [30].…”

Section: Introductionmentioning

confidence: 99%

CRB Analysis for Mixed-ADC PMCW MIMO Radar

Cheng¹,

Shang²,

Liu³

2021

2021 CIE International Conference on Radar (Radar)

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Phase-modulated continuous-wave (PMCW) multiple-input multiple-output (MIMO) radar systems are known to possess excellent mutual interference mitigation capabilities, but require costly and power-hungry high sampling rate and high-precision analog-to-digital converters (ADC's). To reduce cost and power consumption, we consider a mixed-ADC architecture, in which most receive antenna outputs are sampled by one-bit ADC's, and only one or a few outputs by high-precision ADC's. We first derive the Cramér-Rao bound (CRB) for the mixed-ADC based PMCW MIMO radar to characterize the best achievable performance of an unbiased target parameter estimator. The CRB analysis demonstrates that the mixed-ADC architecture with a relatively small number of high-precision ADC's and a large number of one-bit ADC's allows us to drastically reduce the hardware cost and power consumption while still maintain a high dynamic range needed for autonomous driving applications. We also introduce a two-step estimator to realize the computationally efficient maximum likelihood (ML) estimation of the target parameters. We formulate the angle-Doppler imaging problem as a sparse parameter estimation problem, and a computationally efficient majorization-minimization (MM) based estimator of sparse parameters, referred to as mLIKES, is devised for accurate angle-Doppler imaging. This is followed by using a relaxationbased approach to cyclically refine the results of mLIKES for accurate off-grid target parameter estimation. Numerical examples are provided to demonstrate the effectiveness of the proposed algorithms for angle-Doppler imaging using mixed-ADC based PMCW MIMO radar.

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