Bayesian Compressed Sensing Algorithm Research Articles

Non-unit ultra-high-speed DC line protection method for HVDC grids using dynamic fitting algorithm with data reconstruction

Existing non-unit protection schemes are inevitably influenced by the high fault-resistance in MMC-HVDC grid, which is a serious problem affecting the rapidity and reliability of fault recognition. This study proposes a dynamic fitting of data reconstruction-based protection method to improve the accuracy and rapidity of HVDC grid protection. Due to the propagation characteristics of the initial voltage traveling wave being unaffected by the transition resistance, the self-adapting exponential fitting algorithm is used to recognize the fault waveform. Then, sliding data window with the voltage traveling wave fit by the optimal basis exponential fitting algorithm could avoid the influence of initial value selection on fitting convergence. Meanwhile, the original fault traveling wave is reconstructed by “Bayesian compressed sensing algorithm (BCSA)” in a low sampling rate. This method could reduce the operation response time of the proposed method to satisfy the ultra-high-speed requirement of MMC-HVDC grid and also adapt the limitation of sampling rate in existing protection devices. Practical case studies in PSCAD/EMTDC and protection prototype testing have demonstrated that the proposed method is effective under different fault locations, fault types, and high fault impedance.

High Maneuvering Target Long-time Coherent Integration and Motion Parameters Estimation Based on Bayesian Compressive Sensing

This paper intends to address the long-time coherent integration (LTCI) of high maneuvering targets in low signal-to-noise (SNR) environments with high computational efficiency and accuracy. The high-order motion parameters estimation, considering the acceleration and jerk, is modeled as the under-estimated linear regression and the complex-field Bayesian compressive sensing (BCS) algorithm is introduced to resolve the sparse recovery. To correct the high-order range migration (RM) and reduce the order of Doppler frequency migration (DFM), the adjacent cross correlation (ACCF) is applied. An effective method based on non-coherent integration is proposed to extract the self-term from ACCF result. Further, with the proper design of sensing matrix, the acceleration and jerk motion parameters are estimated by sparse reconstruction based on the complex-field BCS. Compared with the traditional methods based on time-frequency transform, like Lv's distribution (LVD), the proposed BCS algorithm is free from the interference of cross-terms and maintains the super-resolution ability, which provides better performance in parameters estimation and multi-targets discrimination. Finally, the numerical experiments validate the advantages of the proposed method in motion parameters estimation, super-resolution ability and computational efficiency.

Diagnosis of Early Neurological Deterioration after Intravenous Thrombolysis for Patients with Cerebral Ischemic Stroke Using Compressed Sensing-Magnetic Resonance Imaging Algorithm

This study was to explore the risk factors and prognosis of early neurological deterioration (END) after intravenous thrombolysis in patients with cerebral ischemic stroke (CIS) with the guidance of magnetic resonance imaging (MRI) under the compressed sensing-MRI (CSMRI) algorithm. 187 patients with CIS in the hospital were selected and grouped into a deterioration group and a control group according to whether they had END. The CSMRI algorithm was constructed and compared with digital television (DTV) algorithm and Bayesian compressed sensing (BCS) algorithm. It was found that the reconstruction time of CSMRI algorithm in platform I (1134.9 s) and platform II (2615.8 s) was visibly lower than that of DTV algorithm (2634.6 s, 3963.4 s) and BCS algorithm (5631.5 s, 7412.3 s), showing statistically obvious differences (P < 0.05). In addition, the reconstruction efficiency of the CSMRI algorithm was the best. After 4 hours of intravenous thrombolysis, the stroke scale score (12.3 scores) of the deterioration group was much higher than that of the control group (8.4 scores) (P < 0.05). The occlusion of responsible great vessel in the deterioration group (30 cases, 83.33%) was obviously higher in contrast to that in the control group (74 cases, 49%) (P < 0.05). Stroke scale score and occlusion of responsible great vessel were risk factors for EBD after intravenous thrombolysis.

When Sharing Economy Meets IoT

Air pollution is a serious global issue impacting public health and social economy. In particular, exposure to small particulate matter of 2.5 microns or less in diameter (PM2.5) can cause cardiovascular and respiratory diseases, and cancer. Fine-grained urban air quality monitoring is crucial yet difficult to achieve. In this paper, we present the design, implementation, and evaluation of an ambient environment aware system, namely UbiAir, which can support fine-grained urban air quality monitoring through mobile crowdsensing on a bike-sharing system. We have built specific IoT box configured with multiple pollutant sensors and attached on shared bikes to sample micro-scale air quality data in the monitoring space that is split by a scalable grid structure. Both hardware and software data calibration methods are exploited in UbiAir to make the sampled data reliable. Then, we use Bayesian compressive sensing (BCS) as an inference model that leverages the calibrated samples to recover data points without direct measurements and reconstruct an accurate air quality map covering the entire monitoring space. In addition, red envelope based incentive schemes and differential rewarding strategies have been designed in UbiAir, and an adaptive BCS algorithm is proposed to deploy the red envelopes at the most informative positions to facilitate data sampling and inference. We have tested our system on campus with over 100k data measurements collected by 36 students through 18 days. Our real-world experiments show that UbiAir is a light-weight, low-cost, accurate and scalable system for fine-grained air quality monitoring, as compared with other solutions.

Joint 2D DOA and Doppler frequency estimation for L-shaped array using compressive sensing

A joint two-dimensional (2D) direction-of-arrival (DOA) and radial Doppler frequency estimation method for the L-shaped array is proposed in this paper based on the compressive sensing (CS) framework. Revised from the conventional CS-based methods where the joint spatial-temporal parameters are characterized in one large scale matrix, three smaller scale matrices with independent azimuth, elevation and Doppler frequency are introduced adopting a separable observation model. Afterwards, the estimation is achieved by L 1 -norm minimization and the Bayesian CS algorithm. In addition, under the L-shaped array topology, the azimuth and elevation are separated yet coupled to the same radial Doppler frequency. Hence, the pair matching problem is solved with the aid of the radial Doppler frequency. Finally, numerical simulations corroborate the feasibility and validity of the proposed algorithm.

A Fast Sparse Recovery Algorithm via Resolution Approximation for LASAR 3D Imaging

Compressed sensing (CS) algorithms are used for linear array synthetic aperture radar (LASAR) three-dimensional (3D) imaging. However, it is difficult to obtain imaging results with both high computational efficiency and promising imaging quality. Because of the high-dimensional matrix-operations, the computational complexity of several CS algorithms is huge such as the iterative adaptive approach (IAA), bayesian compressed sensing (BCS), and sparsity bayesian recovery via iterative minimum (SBRIM) algorithm. Besides, the greedy pursuit algorithms such as the orthogonal matching pursuit (OMP) algorithm cannot acquire ideal imaging results on account of the preset sparsity of the imaging scene. To solve the problem, we present a fast sparse recovery algorithm via resolution approximation (FSRARA) in this paper. Firstly, the whole imaging scene is divided into 3D scattering units with large spacing, and SBRIM algorithm is used to obtain its low-resolution imaging results quickly. Secondly, the low-resolution imaging results are conducted image segmentation by the fuzzy c-means (FCM) clustering algorithm to extract the possible targets' areas coarsely. Then we re-divide the imaging scene by higher imaging resolution and extract the possible targets' areas according to the coarsely possible targets' areas. FSRARA achieves improved computational efficiency with low-dimensional matrix-operations on the possible targets' areas instead of the high-dimensional one on the whole imaging scene. Meanwhile, FSRARA performs better in suppressing the false targets and sidelobe interference and improves the imaging quality than the SBRIM algorithm. Simulation and experimental results prove that FSRARA improves the computational efficiency by hundreds of times at most than SBRIM algorithm and its computational efficiency is higher than smoothed $L_{0} $ norm (SL0), IAA, and BCS algorithm. Besides, FSRARA improves the imaging quality compared with OMP, IAA, SL0, BCS, and SBRIM algorithms.

Channel Estimation for mmWave MIMO With Transmitter Hardware Impairments

This letter considers the problem of channel estimation for millimeter wave (mmWave) multiple-input multiple-output systems under a transmitter impairments model. Specifically, taking the transmitter hardware impairments into account, the performance of conventional pilots-based channel estimation scheme will be degraded due to the destroyed training pilots. By exploiting the sparsity of mmWave channel in the angular domain, a new channel estimation algorithm based on the Bayesian compressive sensing (BCS) and least square estimation (LSE) is proposed. First, the expectation maximization algorithm is presented to solve the BCS problem, and the refined measurement matrix and the support of the channel vector are obtained. Next, the channel gain coefficients are estimated by using the LSE. Simulation results show that the proposed algorithm can achieve better performance compared with the conventional BCS and orthogonal matching pursuit algorithm.

Bayesian compressive sensing for approximately sparse signals and application to structural health monitoring signals for data loss recovery

The theory and application of compressive sensing (CS) have received a lot of interest in recent years. The basic idea in CS is to use a specially-designed sensor to sample signals that are sparse in some basis (e.g. wavelet basis) directly in a compressed form, and then to reconstruct (decompress) these signals accurately using some inversion algorithm after transmission to a central processing unit. However, many signals in reality are only approximately sparse, where only a relatively small number of the signal coefficients in some basis are significant and the remaining basis coefficients are relatively small but they are not all zero. In this case, perfect reconstruction from compressed measurements is not expected. In this paper, a Bayesian CS algorithm is proposed for the first time to reconstruct approximately sparse signals. A robust treatment of the uncertain parameters is explored, including integration over the prediction-error precision parameter to remove it as a “nuisance” parameter, and introduction of a successive relaxation procedure for the required optimization of the basis coefficient hyper-parameters. The performance of the algorithm is investigated using compressed data from synthetic signals and real signals from structural health monitoring systems installed on a space-frame structure and on a cable-stayed bridge. Compared with other state-of-the-art CS methods, including our previously-published Bayesian method, the new CS algorithm shows superior performance in reconstruction robustness and posterior uncertainty quantification, for approximately sparse signals. Furthermore, our method can be utilized for recovery of lost data during wireless transmission, even if the level of sparseness in the signal is low.

Bayesian Inverse Synthetic Aperture Radar Imaging by Exploiting Sparse Probing Frequencies

This letter proposes a Bayesian compressed sensing (BCS) method for high-resolution inverse synthetic aperture radar (ISAR) imaging when the basis matrix depends on an unknown rotation rate. In the proposed method, the radar transmits only a few probing frequencies instead of a wideband signal. In doing so, both the rotation rate and a high-resolution ISAR image can be simultaneously retrieved by jointly applying the BCS algorithm using Gaussian prior and a gradient-based search algorithm to exploit the sparse probing frequencies. The effectiveness of the proposed method is verified by experimental results with both simulated data and real data.

Robust Bayesian Compressive Sensing for Signals in Structural Health Monitoring

AbstractIn structural health monitoring (SHM) systems for civil structures, massive amounts of data are often generated that need data compression techniques to reduce the cost of signal transfer and storage, meanwhile offering a simple sensing system. Compressive sensing (CS) is a novel data acquisition method whereby the compression is done in a sensor simultaneously with the sampling. If the original sensed signal is sufficiently sparse in terms of some orthogonal basis (e.g., a sufficient number of wavelet coefficients are zero or negligibly small), the decompression can be done essentially perfectly up to some critical compression ratio; otherwise there is a trade‐off between the reconstruction error and how much compression occurs. In this article, a Bayesian compressive sensing (BCS) method is investigated that uses sparse Bayesian learning to reconstruct signals from a compressive sensor. By explicitly quantifying the uncertainty in the reconstructed signal from compressed data, the BCS technique exhibits an obvious benefit over existing regularized norm‐minimization CS methods that provide a single signal estimate. However, current BCS algorithms suffer from a robustness problem: sometimes the reconstruction errors are very large when the number of measurements K are a lot less than the number of signal degrees of freedom N that are needed to capture the signal accurately in a directly sampled form. In this article, we present improvements to the BCS reconstruction method to enhance its robustness so that even higher compression ratios can be used and we examine the trade‐off between efficiently compressing data and accurately decompressing it. Synthetic data and actual acceleration data collected from a bridge SHM system are used as examples. Compared with the state‐of‐the‐art BCS reconstruction algorithms, the improved BCS algorithm demonstrates superior performance. With the same acceptable error rate based on a specified threshold of reconstruction error, the proposed BCS algorithm works with relatively large compression ratios and it can achieve perfect lossless compression performance with quite high compression ratios. Furthermore, the error bars for the signal reconstruction are also quantified effectively.

Double‐level binary tree Bayesian compressed sensing for block structured sparse signals

Sparsity is one of the key points in the compressed sensing (CS) theory, which provides a sub-Nyquist sampling paradigm. Nevertheless, apart from sparsity, structures on the sparse patterns such as block structures and tree structures can also be exploited to improve the reconstruction performance and further reduce the sampling rate in CS framework. Based on the fact that the block structure is also sparse for a widely studied block sparse signal, in this study, a double-level binary tree (DBT) hierarchical Bayesian model is proposed under the Bayesian CS (BCS) framework. The authors exploit a recovery algorithm with the proposed DBT structured model, and the block clustering in the proposed algorithm can be achieved fastly and correctly using the Markov Chain Monte Carlo method. The experimental results demonstrate that, compared with most existing CS algorithms for block sparse signals, our proposed DBT-based BCS algorithm can obtain good recovery results with less time consuming.

A New Way of Ultra-Wideband Channel Estimation Based on Bayesian Compressive Sensing

In this paper, in order to solve the problem that the sampling rate in ultra-wideband (UWB) channel estimation is too high, we discuss the applicability of Bayesian Compressive Sensing (BCS) used in UWB channel estimation. We solve the problem by using the time domain sparse of the impulse response of the UWB channel and establishing the probability model of the Compressive Sensing (CS) measurement. We accomplish the channel estimation by optimizing maximum a posteriori (MAP) of the channel. The simulation results show that the proposed scheme needs a very low sampling rate to recover the channel accurately. And the BCS algorithm has a better performance than the basis pursuit (BP) algorithm and the traditional least square (LS) algorithm in bit error rate (BER).