Clutter Removal in Sub-Nyquist Radar

Eldar, Yonina C.; Levi, Roei; Cohen, Aharon

doi:10.1109/lsp.2014.2351827

Cited by 15 publications

(25 citation statements)

References 9 publications

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“…The Doppler focusing approach achieves this minimal number of samples. Finally, Doppler focusing is able to deal with certain models of clutter and target dynamic range by adding a simple windowing operation in the sum (40) and by pre-whitening in frequency [59].…”

Section: Fast Time Xamplingmentioning

confidence: 99%

Sub-Nyquist Radar Systems: Temporal, Spectral, and Spatial Compression

Cohen

Eldar

2018

IEEE Signal Process. Mag.

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Section: Fast Time Xamplingmentioning

confidence: 99%

Sub-Nyquist Radar Systems: Temporal, Spectral, and Spatial Compression

Cohen

Eldar

2018

IEEE Signal Process. Mag.

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“…MWC has been considered to be an effective approach for compressed sampling and signal reconstruction with its applications on radar, broadband communication, and cognitive radio spectrum sensing [12][13][14].…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

“…, 푚 , 푚 ). Then, is calculated by (12) and is listed in a descending order. The index of maximum value in is the estimated sparsity order̂.…”

Section: Estimation Of Sparsity Ordermentioning

confidence: 99%

“…The number of the subbands is chosen in the range [2,12] with 2 as its advancing step. The parameters are SNR = 20 dB, = 20, 푖 ∈ {1, 2, 3, 4, 5, 6}, and 푖 ∈ {0.4, 0.7, 0.2, 0.9, 1.2, 1.5} s. As shown in Figure 10, since the number of the bands is directly related to the sparsity order of the signal, with the increase of , the signal is not sparse enough and the success rate of reconstruction for three algorithms is reduced.…”

Section: The Successful Probability Of the Reconstructionmentioning

confidence: 99%

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Adaptive and Blind Wideband Spectrum Sensing Scheme Using Singular Value Decomposition

Bai

Zhao

et al. 2017

Wireless Communications and Mobile Computing

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The Modulated Wideband Converter (MWC) can provide a sub-Nyquist sampling for continuous analog signal and reconstruct the spectral support. However, the existing reconstruction algorithms need a priori information of sparsity order, are not self-adaptive for SNR, and are not fault tolerant enough. These problems affect the reconstruction performance in practical sensing scenarios. In this paper, an Adaptive and Blind Reduced MMV (Multiple Measurement Vectors) Boost (ABRMB) scheme based on singular value decomposition (SVD) for wideband spectrum sensing is proposed. Firstly, the characteristics of singular values of signals are used to estimate the noise intensity and sparsity order, and an adaptive decision threshold can be determined. Secondly, optimal neighborhood selection strategy is employed to improve the fault tolerance in the solver of ABRMB. The experimental results demonstrate that, compared with ReMBo (Reduce MMV and Boost) and RPMB (Randomly Projecting MMV and Boost), ABRMB can significantly improve the success rate of reconstruction without the need to know noise intensity and sparsity order and can achieve high probability of reconstruction with fewer sampling channels, lower minimum sampling rate, and lower approximation error of the potential of spectral support.

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“…In the aforementioned methods, the clutter has been ignored or not properly addressed. In [12], the authors have extended the proposed approach in [9] by using the clutter covariance matrix to mitigate clutter in the DF method. In this paper, by using more efficient sampling techniques at receiver and proper sparse matrix recovery algorithms, we show that the performance of CS‐based radar in the presence of clutter can be significantly improved, compared with [9].…”

Section: Introductionmentioning

confidence: 99%