scholarly journals Guaranteed Stability of Sparse Recovery in Distributed Compressive Sensing MIMO Radar

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Yu Tao ◽  
Gong Zhang ◽  
Jindong Zhang
Keyword(s):  
Compressive Sensing ◽  
Matching Pursuit ◽  
Mimo Radar ◽  
Radar System ◽  
Sensing Matrix ◽  
Joint Measurement ◽  
Low Snr ◽  
The Face ◽  
Distributed Compressive Sensing ◽  
Average Coherence

Low SNR condition has been a big challenge in the face of distributed compressive sensing MIMO radar (DCS-MIMO radar) and noise in measurements would decrease performance of radar system. In this paper, we first devise the scheme of DCS-MIMO radar including the joint sparse basis and the joint measurement matrix. Joint orthogonal matching pursuit (JOMP) algorithm is proposed to recover sparse targets scene. We then derive a recovery stability guarantee by employing the average coherence of the sensing matrix, further reducing the least amount of measurements which are necessary for stable recovery of the sparse scene in the presence of noise. Numerical results show that this scheme of DCS-MIMO radar could estimate targets’ parameters accurately and demonstrate that the proposed stability guarantee could further reduce the amount of data to be transferred and processed. We also show the phase transitions diagram of the DCS-MIMO radar system in simulations, pointing out the problem to be further solved in our future work.

scholarly journals CHAOTIC COMPRESSIVE SENSING OF TV –UHF BAND IN IRAQ USING CHEBYSHEV GRAM SCHMIDT SENSING MATRIX

2021 ◽  
Vol 1 (1) ◽  
pp. 134-145
Author(s):  
Hadeel S. Abed ◽  
Hikmat N. Abdullah
Keyword(s):  
Compressive Sensing ◽  
Probability Of Detection ◽  
Main Step ◽  
Mutual Coherence ◽  
Sensing Matrix ◽  
Recovery Algorithm ◽  
Low Snr ◽  
Chebyshev Map ◽  
Recovery Error ◽  
Three Stages

Cognitive radio (CR) is a promising technology for solving spectrum sacristy problem. Spectrum sensing  is the main step of CR.  Sensing the wideband spectrum produces more challenges. Compressive sensing (CS) is a technology used as spectrum sening  in CR to solve these challenges. CS consists of three stages: sparse representation, encoding and decoding. In encoding stage sensing matrix are required, and it plays an important role for performance of CS. The design of efficient sensing matrix requires achieving low mutual coherence . In decoding stage the recovery algorithm is applied to reconstruct a sparse signal. İn this paper a new chaotic matrix is proposed based on Chebyshev map and modified gram Schmidt (MGS). The CS based proposed matrix is applied for sensing  real TV signal as a PU. The proposed system is tested under two types of recovery algorithms. The performance of CS based proposed matrix is measured using recovery error (Re), mean square error (MSE), and probability of detection (Pd) and evaluated by comparing it with Gaussian, Bernoulli and chaotic matrix in the literature. The simulation results show that the proposed system has low Re and high Pd under low SNR values and has low MSE with high compression.

Compressive Sensing-Based Angle Estimation for MIMO Radar with Multiple Snapshots

2013 ◽  
Vol 347-350 ◽  
pp. 1028-1032
Author(s):  
Jian Feng Li ◽  
Xiao Fei Zhang ◽  
Tong Hu
Keyword(s):  
Compressive Sensing ◽  
Complexity Analysis ◽  
Matching Pursuit ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Eigenvalue Decomposition ◽  
Angle Estimation ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Esprit Algorithm

The issue of angle estimation for multiple-input multiple-output (MIMO) radar is studied and an algorithm for the estimation based on compressive sensing with multiple snapshots is proposed. The dimension of received signal is reduced to make the computation burden lower, and then the noise sensitivity is reduced by the eigenvalue decomposition (EVD) of the covariance matrix of the reduced-dimensional signal. Finally the signal subspace obtained from the eigenvectors is realigned to apply the orthogonal matching pursuit (OMP) for angle estimation. The angle estimation performance of the proposed algorithm is better than that of estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm, and reduced-dimension Capon. Furthermore, the proposed algorithm works well for coherent targets, and requires no knowledge of the noise. The complexity analysis and simulation results verify the effectiveness of the algorithm.

Distributed Compressive Sensing

2009 ◽  
Author(s):  
Dror Baron ◽  
Marco F. Duarte ◽  
Michael B. Wakin ◽  
Shriram Sarvotham ◽  
Richard G. Baraniuk
Keyword(s):  
Compressive Sensing ◽  
Distributed Compressive Sensing

Characterization of Alpine Snowpacks Using a Low Complexity Portable MIMO Radar System

2020 ◽  
Author(s):  
Lekhmissi Harkati ◽  
Ray Abdo ◽  
Stephane Avrillon ◽  
Laurent Ferro-Famil ◽  
Isabelle Gouttevin ◽  
...  
Keyword(s):  
Low Complexity ◽  
Mimo Radar ◽  
Radar System

A 77 GHz FMCW MIMO radar system based on 65nm CMOS cascadable 2T3R transceiver

2020 ◽  
Vol 64 (1) ◽  
Author(s):  
Taikun Ma ◽  
Zipeng Chen ◽  
Jianxi Wu ◽  
Wei Zheng ◽  
Shufu Wang ◽  
...  
Keyword(s):  
Mimo Radar ◽  
Radar System ◽  
77 Ghz

scholarly journals A Broadband Spectrum Sensing Algorithm in TDCS Based on ICoSaMP Reconstruction

2018 ◽  
Vol 173 ◽  
pp. 03073
Author(s):  
Liu Yang ◽  
Ren Qinghua ◽  
Xu Bingzheng ◽  
Li Xiazhao
Keyword(s):  
Time Use ◽  
Matching Pursuit ◽  
Reconstruction Algorithm ◽  
Transform Domain ◽  
Low Snr ◽  
System Noise ◽  
Broadband Spectrum ◽  
Matching Pursuit Algorithm ◽  
The Mean ◽  
Occupancy Rates

In order to solve the problem that the wideband compressive sensing reconstruction algorithm cannot accurately recover the signal under the condition of blind sparsity in the low SNR environment of the transform domain communication system. This paper use band occupancy rates to estimate sparseness roughly, at the same time, use the residual ratio threshold as iteration termination condition to reduce the influence of the system noise. Therefore, an ICoSaMP(Improved Compressive Sampling Matching Pursuit) algorithm is proposed. The simulation results show that compared with CoSaMP algorithm, the ICoSaMP algorithm increases the probability of reconstruction under the same SNR environment and the same sparse degree. The mean square error under the blind sparsity is reduced.

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