scholarly journals DCS-based MBSBL joint reconstruction of multi-sensors data for energy-efficient telemonitoring of human activity

2018 ◽  
Vol 14 (3) ◽  
pp. 155014771876761
Author(s):  
Jianning Wu ◽  
Jiajing Wang ◽  
Yun Ling
Keyword(s):  
Energy Efficiency ◽  
Compressed Sensing ◽  
Sparse Representation ◽  
Human Activity ◽  
Energy Efficient ◽  
Bayesian Learning ◽  
Sparse Bayesian Learning ◽  
Distributed Compressed Sensing ◽  
Joint Reconstruction ◽  
Sparse Representation Classification

The joint reconstruction of nonsparse multi-sensors data with high quality is a challenging issue in human activity telemonitoring. In this study, we proposed a novel joint reconstruction algorithm combining distributed compressed sensing with multiple block sparse Bayesian learning. Its basic idea is that based on the joint sparsity model, the distributed compressed sensing technique is first applied to simultaneously compress the multi-sensors data for gaining the high-correlation information regarding activity as well as the energy efficiency of sensors, and then, the multiple block sparse Bayesian learning technique is employed to jointly recover nonsparse multi-sensors data with high fidelity by exploiting the joint block sparsity. The multi-sensors acceleration data from an open wearable action recognition database are selected to assess the practicality of our proposed technique. The sparse representation classification model is used to classify activity patterns using the jointly reconstructed data in order to further examine the effectiveness of our proposed method. The results showed that when compression rates are selected properly, our proposed technique can gain the best joint reconstruction performance as well as energy efficiency of sensors, which greatly contributes to the best sparse representation classification–based activity classification performance. This has a great potential for energy-efficient telemonitoring of human activity.

scholarly journals A Novel STAP Algorithm for Airborne MIMO Radar Based on Temporally Correlated Multiple Sparse Bayesian Learning

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Hanwei Liu ◽  
Yongshun Zhang ◽  
Yiduo Guo ◽  
Qiang Wang ◽  
Yifeng Wu
Keyword(s):  
Sparse Representation ◽  
Bayesian Learning ◽  
Multiple Input Multiple Output ◽  
Time Spectrum ◽  
Weight Vector ◽  
Mimo Radar ◽  
Sparse Bayesian Learning ◽  
Multiple Measurement Vectors ◽  
Input Multiple Output ◽  
Temporally Correlated

In a heterogeneous environment, to efficiently suppress clutter with only one snapshot, a novel STAP algorithm for multiple-input multiple-output (MIMO) radar based on sparse representation, referred to as MIMOSR-STAP in this paper, is presented. By exploiting the waveform diversity of MIMO radar, each snapshot at the tested range cell can be transformed into multisnapshots for the phased array radar, which can estimate the high-resolution space-time spectrum by using multiple measurement vectors (MMV) technique. The proposed approach is effective in estimating the spectrum by utilizing Temporally Correlated Multiple Sparse Bayesian Learning (TMSBL). In the sequel, the clutter covariance matrix (CCM) and the corresponding adaptive weight vector can be efficiently obtained. MIMOSR-STAP enjoys high accuracy and robustness so that it can achieve better performance of output signal-to-clutter-plus-noise ratio (SCNR) and minimum detectable velocity (MDV) than the single measurement vector sparse representation methods in the literature. Thus, MIMOSR-STAP can deal with badly inhomogeneous clutter scenario more effectively, especially suitable for insufficient independent and identically distributed (IID) samples environment.

scholarly journals Multitarget Direct Localization Using Block Sparse Bayesian Learning in Distributed MIMO Radar

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Bin Sun ◽  
Haowen Chen ◽  
Xizhang Wei ◽  
Xiang Li
Keyword(s):  
Sparse Representation ◽  
Bayesian Learning ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Sparse Bayesian Learning ◽  
Localization Accuracy ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Direct Localization ◽  
Correct Target

The target localization in distributed multiple-input multiple-output (MIMO) radar is a problem of great interest. This problem becomes more complicated for the case of multitarget where the measurement should be associated with the correct target. Sparse representation has been demonstrated to be a powerful framework for direct position determination (DPD) algorithms which avoid the association process. In this paper, we explore a novel sparsity-based DPD method to locate multiple targets using distributed MIMO radar. Since the sparse representation coefficients exhibit block sparsity, we use a block sparse Bayesian learning (BSBL) method to estimate the locations of multitarget, which has many advantages over existing block sparse model based algorithms. Experimental results illustrate that DPD using BSBL can achieve better localization accuracy and higher robustness against block coherence and compressed sensing (CS) than popular algorithms in most cases especially for dense targets case.

Energy Efficient Sparse Bayesian Learning using Learned Approximate Message Passing

2021 ◽  
Author(s):  
Christo Kurisummoottil Thomas ◽  
Rakesh Mundlamuri ◽  
Chandra R Murthy ◽  
Marios Kountouris
Keyword(s):  
Energy Efficient ◽  
Message Passing ◽  
Bayesian Learning ◽  
Sparse Bayesian Learning ◽  
Approximate Message Passing

scholarly journals Robust Face Recognition via Block Sparse Bayesian Learning

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Taiyong Li ◽  
Zhilin Zhang
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Bayesian Learning ◽  
State Of The Art ◽  
Basis Functions ◽  
Training Dataset ◽  
Sparse Bayesian Learning ◽  
Sparse Model ◽  
Test Face ◽  
Robust Face Recognition

Face recognition (FR) is an important task in pattern recognition and computer vision. Sparse representation (SR) has been demonstrated to be a powerful framework for FR. In general, an SR algorithm treats each face in a training dataset as a basis function and tries to find a sparse representation of a test face under these basis functions. The sparse representation coefficients then provide a recognition hint. Early SR algorithms are based on a basic sparse model. Recently, it has been found that algorithms based on a block sparse model can achieve better recognition rates. Based on this model, in this study, we use block sparse Bayesian learning (BSBL) to find a sparse representation of a test face for recognition. BSBL is a recently proposed framework, which has many advantages over existing block-sparse-model-based algorithms. Experimental results on the Extended Yale B, the AR, and the CMU PIE face databases show that using BSBL can achieve better recognition rates and higher robustness than state-of-the-art algorithms in most cases.

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