Joint temporal-sparse recovery approach for estimation of underwater acoustic MIMO channels

2016 ◽  
Author(s):  
Yue-Hai Zhou ◽  
Feng Tong ◽  
Dong-Sheng Chen
Keyword(s):  
Sparse Recovery ◽  
Mimo Channels ◽  
Underwater Acoustic ◽  
Recovery Approach

scholarly journals Radar Array Diagnosis from Undersampled Data Using a Compressed Sensing/Sparse Recovery Technique

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
S. Costanzo ◽  
A. Borgia ◽  
G. Di Massa ◽  
D. Pinchera ◽  
M. D. Migliore
Keyword(s):  
Compressed Sensing ◽  
Sparse Recovery ◽  
Accurate Diagnosis ◽  
Small Set ◽  
Recovery Technique ◽  
Radar Applications ◽  
Experimental Validations ◽  
Slotted Waveguide ◽  
Recovery Approach ◽  
Undersampled Data

A Compressed Sensing/Sparse Recovery approach is adopted in this paper for the accurate diagnosis of fault array elements from undersampled data. Experimental validations on a slotted waveguide test array are discussed to demonstrate the effectiveness of the proposed procedure in the failures retrieval from a small set of measurements with respect to the number of radiating elements. Due to the sparsity feature of the proposed formulation, the method is particularly appealing for the diagnostics of large arrays, typically adopted for radar applications.

scholarly journals Joint Frame Synchronization and Channel Estimation: Sparse Recovery Approach and USRP Implementation

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 39041-39053 ◽  
Author(s):  
Ozgur Ozdemir ◽  
Chethan Kumar Anjinappa ◽  
Ridha Hamila ◽  
Naofal Al-Dhahir ◽  
Ismail Guvenc
Keyword(s):  
Channel Estimation ◽  
Sparse Recovery ◽  
Frame Synchronization ◽  
Recovery Approach

A Sparse Recovery Approach for Pattern Correction of Active Arrays in Presence of Element Failures

2015 ◽  
Vol 14 ◽  
pp. 1027-1030 ◽  
Author(s):  
Marco Donald Migliore ◽  
Daniele Pinchera ◽  
Mario Lucido ◽  
Fulvio Schettino ◽  
Gaetano Panariello
Keyword(s):  
Sparse Recovery ◽  
Recovery Approach

scholarly journals Clutter Suppression for Indoor Self-Localization Systems by Iteratively Reweighted Low-Rank Plus Sparse Recovery

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6842
Author(s):  
Jesús Sánchez-Pastor ◽  
Udaya S. K. P. Miriya Miriya Thanthrige ◽  
Furkan Ilgac ◽  
Alejandro Jiménez-Sáez ◽  
Peter Jung ◽  
...  
Keyword(s):  
Frequency Response ◽  
Sparse Recovery ◽  
Low Rank ◽  
Clutter Suppression ◽  
Tag Identification ◽  
High Q ◽  
Potential Applications ◽  
Passive Rfid ◽  
Recovery Approach ◽  
Time Gating

Self-localization based on passive RFID-based has many potential applications. One of the main challenges it faces is the suppression of the reflected signals from unwanted objects (i.e., clutter). Typically, the clutter echoes are much stronger than the backscattered signals of the passive tag landmarks used in such scenarios. Therefore, successful tag detection can be very challenging. We consider two types of tags, namely low-Q and high-Q tags. The high-Q tag features a sparse frequency response, whereas the low-Q tag presents a broad frequency response. Further, the clutter usually showcases a short-lived response. In this work, we propose an iterative algorithm based on a low-rank plus sparse recovery approach (RPCA) to mitigate clutter and retrieve the landmark response. In addition to that, we compare the proposed approach with the well-known time-gating technique. It turns out that RPCA outperforms significantly time-gating for low-Q tags, achieving clutter suppression and tag identification when clutter encroaches on the time-gating window span, whereas it also increases the backscattered power at resonance by approximately 12 dB at 80 cm for high-Q tags. Altogether, RPCA seems a promising approach to improve the identification of passive indoor self-localization tag landmarks.

scholarly journals Compressed Sensing/Sparse-Recovery Approach for Improved Range Resolution in Narrow-Band Radar

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Sandra Costanzo
Keyword(s):  
Compressed Sensing ◽  
Narrow Band ◽  
Mathematical Formulation ◽  
Sparse Recovery ◽  
Range Resolution ◽  
Biological Objects ◽  
Resolution Capability ◽  
Experimental Validations ◽  
Recovery Approach ◽  
Recovery Procedure

A compressed sensing/sparse-recovery procedure is adopted to obtain enhanced range resolution capability from the processing of data acquired with narrow-band SFCW radars. A mathematical formulation for the proposed approach is reported and validity limitations are fully discussed, by demonstrating the ability to identify a great number of targets, up to 20, in the range direction. Both numerical and experimental validations are presented, by assuming also noise conditions. The proposed method can be usefully applied for the accurate detection of parameters with very small variations, such as those involved in the monitoring of soil deformations or biological objects.

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