CSSF MIMO RADAR: Compressive-Sensing and Step-Frequency Based MIMO Radar

Yu, Yao; Petropulu, Athina P.; Poor, H. Vincent

doi:10.1109/taes.2012.6178074

Cited by 72 publications

(27 citation statements)

References 34 publications

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“…A great attention has been directed to multiple-input/multiple-output (MIMO) radar systems [11,12] (Kalogerias et al 2013;Yu et al 2012b). This situation well fitted to CS reconstruction, since the signals are related to all Tx/Rx pairs which in general lead to a non-uniform thinned sampling in range and angles.…”

Section: Prior Art and Inspirationsmentioning

confidence: 99%

“…This particular approach to CS-based radar increases the measurement resolution and simplifies the radar hardware by eliminating the matched filter stage in the receiver. Another approach to incoherence samples the same target scene using multiple radars on ground (Yu et al 2012b). The incoherence of the measurements can also be achieved by deploying an array of antenna elements.…”

Section: Incoherence Considerationsmentioning

confidence: 99%

See 1 more Smart Citation

Compressed sensing applied to weather radar

Mishra

2014

2014 IEEE Geoscience and Remote Sensing Symposium

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Section: Prior Art and Inspirationsmentioning

confidence: 99%

Section: Incoherence Considerationsmentioning

confidence: 99%

Compressed sensing applied to weather radar

Mishra

2014

2014 IEEE Geoscience and Remote Sensing Symposium

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“…However, the coefficients of the node (2,1), (2,2) and (2,3) were close to zero, so the sparseness of the three nodes was better. But when DCT was done on the node (2,0), the nth DCT transform coefficient was equation (6).…”

Section: Sparse Analysismentioning

confidence: 99%

“…At present, the application of the compressed sensing theory has got involved with many fields, such as: compressed sensing radar [2][3][4][5][6], Distributed Compressed Sensing (DCS) theory [7,8,9], wireless sensor network [10,11], image acquisition equipment development [12,13], medical image processing [14,15], Analog-to-Information [16,17], spectral analysis [18][19], hyperspec-tral image processing and remote sensing image processing [20,21,22]. However research on the compressed sensing of the speech signal is still in the early stage.…”

Section: Introductionmentioning

confidence: 99%

Speech Compression and Reconstruction Algorithm Based on Multi-Scale Wavelet Packet and Compressed Sensing

Cao¹,

Xiong²,

Yuan³

2015

IJIES

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Wavelet transform is widely applied in the field of the speech signal processing, but the wavelet decomposition is only decomposing the low frequency coefficients, so higher resolution can not be obtained for the high frequency components. In order to further decompose the high frequency coefficient, in this paper, the speech signal was decomposed by multi-scale wavelet packet. By analyzing the sparsity of the decomposed coefficients, the reconstruction algorithm based on compressed sensing was proposed by using the sparse representation of speech signal, at the same time, the effect of compression ratio on the reconstruction quality was analyzed. In determining the compression ratio, speech signal reconstruction quality and coding speed were taken into account, so the proposed compressed and reconstruction method had good real-time property. A large number of simulation experiments showed that the proposed method in this paper had excellent reconstruction quality and efficient coding efficiency.

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“…Another A/I model, modulation wideband converter (MWC) [11] can undersample analog multiband signal along with spectrum-blind recovery. CS applications to radar are mainly focused to hard target radar [12] or multipleinput-multiple-output (MIMO) radar [13] exploiting sparsity in the target range, Doppler, or angle space. Similarly, CS-based radar imaging using synthetic aperture radar (SAR) for terrain mapping [14] and ultrawideband radar for through-the-wall target detection [15] are of major interests.…”

Section: Introductionmentioning

confidence: 99%

Sub-Nyquist sampling and detection in Costas coded pulse compression radars

Hanif

Mansoor

Imran

2016

EURASIP J. Adv. Signal Process.

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Modern pulse compression radar involves digital signal processing of high bandwidth pulses modulated with different coding schemes. One of the limiting factors in the radar's design to achieve desired target range and resolution is the need of high rate analog-to-digital (A/D) conversion fulfilling the Nyquist sampling criteria. The high sampling rates necessitate huge storage capacity, more power consumption, and extra processing requirement. We introduce a new approach to sample wideband radar waveform modulated with Costas sequence at a sub-Nyquist rate based upon the concept of compressive sensing (CS). Sub-Nyquist measurements of Costas sequence waveform are performed in an analog-to-information (A/I) converter based upon random demodulation replacing traditional A/D converter. The novel work presents an 8-order Costas coded waveform with sub-Nyquist sampling and its reconstruction. The reconstructed waveform is compared with the conventionally sampled signal and depicts high-quality signal recovery from sub-Nyquist sampled signal. Furthermore, performance of CS-based detections after reconstruction are evaluated in terms of receiver operating characteristic (ROC) curves and compared with conventional Nyquist-rate matched filtering scheme.

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CSSF MIMO RADAR: Compressive-Sensing and Step-Frequency Based MIMO Radar

Cited by 72 publications

References 34 publications

Compressed sensing applied to weather radar

Compressed sensing applied to weather radar

Speech Compression and Reconstruction Algorithm Based on Multi-Scale Wavelet Packet and Compressed Sensing

Sub-Nyquist sampling and detection in Costas coded pulse compression radars

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