Cognitive random stepped frequency radar with sparse recovery

Huang, Tianyao; Liu, Yimin; Meng, Huadong; Wang, Xiqin

doi:10.1109/taes.2013.120443

Cited by 95 publications

(37 citation statements)

References 37 publications

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“…Liu et al [4] propose the RV-IAP algorithm for joint range-Doppler estimation, which is based on the Orthogonal Matching Pursuit (OMP) method [14]. Since then, many practical CS algorithms for FAR have been developed [6], [15]. This paper focuses on the theoretical analysis of CS methods for FAR in terms of reconstruction performance.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Frequency Agile Radar via Compressed Sensing

Huang

Liu

et al. 2018

IEEE Trans. Signal Process.

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Frequency agile radar (FAR) is known to have excellent electronic counter-countermeasures (ECCM) performance and the potential to realize spectrum sharing in dense electromagnetic environments. Many compressed sensing (CS) based algorithms have been developed for joint range and Doppler estimation in FAR. This paper considers theoretical analysis of FAR via CS algorithms. In particular, we analyze the properties of the sensing matrix, which is a highly structured random matrix. We then derive bounds on the number of recoverable targets. Numerical simulations and field experiments validate the theoretical findings and demonstrate the effectiveness of CS approaches to FAR.

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Section: Introductionmentioning

confidence: 99%

Analysis of Frequency Agile Radar via Compressed Sensing

Huang

Liu

et al. 2018

IEEE Trans. Signal Process.

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“…In TenDSuR, the spatial compression is achieved by deploying a thinned MIMO antenna as in [5,19]. For the spectral thinning of transmit waveforms as in [26,27], TenDSuR employs frequency-diversity waveforms which occupy only a small part of the full bandwidth required for the range resolution of SUMMeR [19]. The sub-Nyquist receiver recovers the target parameters via Xampling leading to temporal compression.…”

Section: Introductionmentioning

confidence: 99%

TenDSuR: Tensor-Based 4D Sub-Nyquist Radar

Mishra

Liu

et al. 2019

IEEE Signal Process. Lett.

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We propose Tensor-based 4D Sub-Nyquist Radar (TenDSuR) that samples in spectral, spatial, Doppler, and temporal domains at sub-Nyquist rates while simultaneously recovering the target's direction, Doppler velocity, and range without loss of native resolutions. We formulate the radar signal model wherein the received echo samples are represented by a partial third-order tensor. We then apply compressed sensing in the tensor domain and use our tensor-OMP and tensor completion algorithms for signal recovery. Our numerical experiments demonstrate joint estimation of all three target parameters at the same native resolutions as a conventional radar but with reduced measurements. Furthermore, tensor completion methods show enhanced performance in off-grid target recovery with respect to tensor-OMP. IndexTerms-sub-Nyquist MIMO radar, tensor decomposition, spectrum sharing, canonical polyadic decomposition (CPD), parallel factor analysis (PARAFAC)

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“…Due to the asymptotically efficient property of the maximum likelihood estimator (MLE), the CRLB can be used to predict the performance of the MLE. In addition, the CRLB has been employed as a criterion for optimal waveform selections [22]- [25].…”

Section: Introductionmentioning

confidence: 99%