Spatial filter measurement matrix design for interference/jamming suppression in colocated compressive sensing MIMO radars

2016 ◽  
Vol 52 (11) ◽  
pp. 956-958 ◽  
Author(s):  
Yu Tao ◽  
Gong Zhang ◽  
Yu Zhang
Keyword(s):  
Compressive Sensing ◽  
Spatial Filter ◽  
Measurement Matrix ◽  
Matrix Design

scholarly journals Measurement Matrix Design for Compressive Sensing–Based MIMO Radar

2011 ◽  
Vol 59 (11) ◽  
pp. 5338-5352 ◽  
Author(s):  
Yao Yu ◽  
Athina P. Petropulu ◽  
H. Vincent Poor
Keyword(s):  
Compressive Sensing ◽  
Mimo Radar ◽  
Measurement Matrix ◽  
Matrix Design

A projection matrix design method for MSE reduction in adaptive compressive sensing

2017 ◽  
Vol 141 ◽  
pp. 16-27 ◽  
Author(s):  
Bo Li ◽  
Liang Zhang ◽  
Thia Kirubarajan ◽  
Sreeraman Rajan
Keyword(s):  
Compressive Sensing ◽  
Design Method ◽  
Projection Matrix ◽  
Matrix Design

scholarly journals Direct Under-Sampling Compressive Sensing Method for Underwater Echo Signals and Physical Implementation

2019 ◽  
Vol 9 (21) ◽  
pp. 4596 ◽  
Author(s):  
Tongjing Sun ◽  
Ji Li ◽  
Philippe Blondel
Keyword(s):  
Compressive Sensing ◽  
A Priori ◽  
Original Data ◽  
Measurement Matrix ◽  
Critical Elements ◽  
Under Sampling ◽  
Information Sampling ◽  
Priori Information ◽  
Practical Field ◽  
Dedicated Hardware

Compressive sensing can guarantee the recovery accuracy of suitably constrained signals by using sampling rates much lower than the Nyquist limit. This is a leap from signal sampling to information sampling. The measurement matrix is key to implementation but limited in the acquisition systems. This article presents the critical elements of the direct under-sampling—compressive sensing (DUS–CS) method, constructing the under-sampling measurement matrix, combined with a priori information sparse representation and reconstruction, and we show how it can be physically implemented using dedicated hardware. To go beyond the Nyquist constraints, we show how to design and adjust the sampling time of the A/D circuit and how to achieve low-speed random non-uniform direct under-sampling. We applied our method to data measured with different compression ratios (volume ratios of collected data to original data). It is shown that DUS-CS works well when the SNR is 3 dB, 0 dB, −3 dB, and −5 dB and the compression ratio is 50%, 20%, and 10%, and this is validated with both simulation and actual measurements. The method we propose provides an effective way for compressed sensing theory to move toward practical field applications that use underwater echo signals.

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