Joint Compressive Sensing Research Articles

A Joint Data Mapping and Detection for High Performance Generalized Spatial Modulation

This letter proposes a novel joint data mapping generalized spatial modulation (JDM-GSM) scheme to remove the restriction on the number of active transmit-antenna (TA) combinations in conventional GSM systems. By increasing the number of active TA combinations, the constellation size for signal mapping can be reduced. Consequently, the overall bit error rate (BER) performance of the GSM system can be improved. To reduce the complexity of optimal maximum-likelihood (ML) detector, a joint compressed sensing (JCS) detector for JDM-GSM scheme is proposed. Simulation results and theoretical analysis show that the proposed scheme can be adapted to multiple-input multiple-output (MIMO) systems and achieves better BER performance than that of the conventional GSM.

Exploiting multi-scale signal information in joint compressed sensing recovery of multi-channel ECG signals

Abstract Computational complexity and power consumption are prominent issues in wireless telemonitoring applications involving physiological signals. Compressed sensing (CS) has emerged as a promising framework to address these challenges because of its energy-efficient data reduction procedure. In this work, a CS-based approach is studied for joint compression/reconstruction of multi-channel electrocardiogram (MECG) signals. The MECG signals share spatially correlated cardiac information across the channels, which is exploited in a joint CS framework for improved signal recovery. Weighted mixed-norm minimization (WMNM)-based joint sparse recovery algorithms are proposed, which can successfully recover the signals from all the channels simultaneously by utilizing the joint sparsity of MECG signals in wavelet domain. The proposed algorithms exploit multi-scale signal information through a multi-scale weighting approach. Under this strategy, weights are designed based on the diagnostic information contents of each wavelet subband/scale. In particular, clinically relevant information is captured in the form of subband energy, entropy, and amplitude decay, and based on this weighting rules are defined at each wavelet scale. Such a weighting approach emphasizes nonzero wavelet coefficients having high diagnostic importance during joint CS reconstruction. Insignificant coefficients are deemphasized simultaneously, resulting in a sparser solution. Simulation results using Physikalisch-Technische Bundesanstalt (PTB), Common Standards for Electrocardiography (CSE) and Massachusetts Institute of Technology-Boston's Beth Israel Hospital (MIT-BIH) MECG databases show that the proposed methods can achieve superior reconstruction quality with a lower number of measurements compared to their non-weighted counterpart and other existing CS-based methods. Reduction in the required number of measurements reduces computational burden and directly translates into higher compression efficiency, resulting in low energy consumption in CS-based telemonitoring systems. The diagnostic reconstruction quality of the algorithm is validated using diagnostic distortion measures like wavelet energy-based diagnostic distortion (WEDD) and with a post-reconstruction classification task. It achieves a classification accuracy of 73.2% even when MECG signals are jointly reconstructed using only about 10% of compressed measurements, validating the diagnosability of the reconstructed signals even at very low data rates.

Compressive SAR raw data with principal component analysis

Abstract The theory of synthetic aperture radar (SAR) signal model is briefly introduced which is constructed with a series of echo signals in range direction. The procedure of principal component analysis (PCA) is presented which is used as transformation basis to sparsify the signals. The joint compressive sensing (CS) and PCA algorithm is devised to realize SAR raw data compressive measurement. A SAR raw data for a point target is simulated and used to verify the performance of the joint CS and PCA algorithm. The numerical experimental results demonstrate that the PCA method has good sparse performance and the joint CS and PCA algorithm is possible to online compressively measure the SAR raw data.