Distributed Compressed Sensing Research Articles

Evaluation on improved sparse signal reconstruction algorithm for trusted AI and DCS technology

The improved Sparse Signal Reconstruction (SR) algorithm for Trusted Artificial Intelligence (AI) and Distributed Compressed Sensing (DCS) technology was thoroughly investigated. The study verified its effectiveness and advantages in trusted AI and DCS systems, which have significant implications for enhancing the credibility, security, and performance of signal processing and AI algorithms. The reconstruction performance was evaluated using Orthogonal Matching Pursuit (OMP), Basis Pursuit (BP), and Least Absolute Shrinkage and Selection Operator (LASSO). The analysis primarily focused on runtime, refactoring errors, and the number of successful reconstruction attempts. When K = 4, K = 6, K = 8, and K = 10, OMP outperformed BP and LASSO in terms of successful reconstructions, demonstrating better performance and higher reconstruction precision.

An Adaptive Sparse Channel Estimation Algorithm for OFDM Systems Based on Distributed Compressive Sensing

The current greedy iterative pursuit algorithms for sparse channel estimation in an orthogonal frequency division multiplexing (OFDM) system based on compressive sensing (CS) or distributed CS (DCS) have the disadvantages of relying on channel priori information as halting condition and having a low support searching efficiency. Under DCS framework, this letter proposes a unique halting condition for greedy algorithms by exploiting the delay correlation between adjacent symbol channels. Additionally, we present a segmented pruning strategy that supports to select multiple atoms in a single iteration to improve the support searching efficiency. Simulation results show that our algorithm can achieve more robust sparsity-adaptive channel estimation with reduced computational complexity compared to the traditional methods.

Distributed Compressive Sensing for Wireless Signal Transmission in Structural Health Monitoring: An Adaptive Hierarchical Bayesian Model-Based Approach.

Signal transmission plays an important role in the daily operation of structural health monitoring (SHM) systems. In wireless sensor networks, transmission loss often occurs and threatens reliable data delivery. The massive amount of data monitoring also leads to a high signal transmission and storage cost throughout the system's service life. Compressive Sensing (CS) provides a novel perspective on alleviating these problems. Based on the sparsity of vibration signals in the frequency domain, CS can reconstruct a nearly complete signal from just a few measurements. This can improve the robustness of data loss while facilitating data compression to reduce transmission demands. Extended from CS methods, distributed compressive sensing (DCS) can exploit the correlation across multiple measurement vectors (MMV) to jointly recover the multi-channel signals with similar sparse patterns, which can effectively enhance the reconstruction quality. In this paper, a comprehensive DCS framework for wireless signal transmission in SHM is constructed, incorporating the process of data compression and transmission loss together. Unlike the basic DCS formulation, the proposed framework not only activates the inter-correlation among channels but also provides flexibility and independence to single-channel transmission. To promote signal sparsity, a hierarchical Bayesian model using Laplace priors is built and further improved as the fast iterative DCS-Laplace algorithm for large-scale reconstruction tasks. Vibration signals (e.g., dynamic displacement and accelerations) acquired from real-life SHM systems are used to simulate the whole process of wireless transmission and test the algorithm's performance. The results demonstrate that (1) DCS-Laplace is an adaptative algorithm that can actively adapt to signals with various sparsity by adjusting the penalty term to achieve optimal performance; (2) compared with CS methods, DCS methods can effectively improve the reconstruction quality of multi-channel signals; (3) the Laplace method has advantages over the OMP method in terms of reconstruction performance and applicability, which is a better choice in SHM wireless signal transmission.

Research on Distributed Compressed Sensing With Dynamic Block Sparsity for Underwater Acoustic Channel Estimation

Channel estimation plays a crucial role in Internet of Underwater Things networks. Traditional channel estimation algorithms have limited performance improvement under the block-sparsity underwater acoustic channel, and the distributed compressed sensing (DCS) method has not been researched under block-sparsity recovery. In this article, we propose DCS with block sparsity for underwater acoustic channel estimation under short observation length and low SNR scenarios. Specifically, the underwater acoustic channels are partitioned some sub-blocks that have the identical size, and the simultaneous block orthogonal matching pursuit algorithm (SBOMP) is proposed to enhance the channel estimates that have common delays. However, the number of nonzero taps located in a partitioned sub-block does not equal to the size of partitioned sub-blocks, and the channel between adjacent two data blocks may have slightly difference, when the SBOMP algorithm is applied, there would be much estimated noise. In order to address this problem, in this article, we also propose dynamic block sparsity-based SBOMP which is referred to as DSBOMP. The proposed DSBOMP consists of the SBOMP algorithm, first-order derivative of the residual method, and parallel comparison strategy. The first-order derivative is used to remove some negligible taps and dynamically measure the number of significant taps; the parallel comparison strategy is used to check whether the current taps are available. Both simulation and sea trial data indicate that, our proposed SBOMP and DSBOMP algorithms have better channel estimation performance than tradition algorithms under short observation length and low SNR. Moreover, our proposed DSBOMP achieves the best communication performance.

A Noise Reduction Method for Multiple Signals Combining Computed Order Tracking Based on Chirplet Path Pursuit and Distributed Compressed Sensing

A Noise Reduction Method for Multiple Signals Combining Computed Order Tracking Based on Chirplet Path Pursuit and Distributed Compressed Sensing

Machine learning-enabled MIMO-FBMC communication channel parameter estimation in IIoT: A distributed CS approach

Compressed Sensing (CS) is a Machine Learning (ML) method, which can be regarded as a single-layer unsupervised learning method. It mainly emphasizes the sparsity of the model. In this paper, we study an ML-based CS Channel Estimation (CE) method for wireless communications, which plays an important role in Industrial Internet of Things (IIoT) applications. For the sparse correlation between channels in Multiple Input Multiple Output Filter Bank MultiCarrier with Offset Quadrature Amplitude Modulation (MIMO-FBMC/OQAM) systems, a Distributed Compressed Sensing (DCS)-based CE approach is studied. A distributed sparse adaptive weak selection threshold method is proposed for CE. Firstly, the correlation between MIMO channels is utilized to represent a joint sparse model, and CE is transformed into a joint sparse signal reconstruction problem. Then, the number of correlation atoms for inner product operation is optimized by weak selection threshold, and sparse signal reconstruction is realized by sparse adaptation. The experiment results show that the proposed DCS-based method not only estimates the multipath channel components accurately but also achieves higher CE performance than classical Orthogonal Matching Pursuit (OMP) method and other traditional DCS methods in the time-frequency dual selective channels.

Satellite microvibration measurement based on distributed compressed sensing

To reduce the burden of multiple sensors on satellites with limited resources, distributed compressed sensing (DCS) is applied to microvibration measurement. An improved joint recovery algorithm using the recovered spectrum coefficients as the halting condition is proposed. This improvement can address the drawback that the previous algorithms need to anticipate signal sparsity or use residuals that do not reflect recovery accuracy in the frequency domain as the halting condition. In addition, acceleration is adopted in the improved algorithm. The DCS-based microvibration measurement was conducted on a satellite mechanical test model. The results reveal that the DCS measurement has a difference in the order of 1e-10 g2/Hz between the power spectrum density (PSD) of the raw data and the recovery data. With one-fifth sampling points of the raw signal, it can achieve a signal difference of less than 4% in PSD, which demonstrates the viability of satellite microvibration measurement based on DCS.

A novel subspace pursuit of residual correlation step algorithm for distributed compressed sensing

Multi-signal joint reconstruction is critical in distributed compressed sensing (DCS). We propose a joint reconstruction algorithm for subspace pursuit of residual correlation steps to balance reconstruction efficiency and data quality. The algorithm improves on two aspects. Firstly, the residuals are used as the step feedback condition to achieve dynamic step adjustment, reducing the iteration’s overall number. Secondly, based on the mixed-support set model (MSM), the residual non-decreasing surplus condition is set to reconstruct the common and the innovation components of the target signal in groups, which reduces the mixing error in joint reconstruction. This paper compares the performance of six algorithms of the same type under the conditions of Gaussian sparse signal and measured shock wave field sensor network data. The results show that the proposed algorithm can effectively reduce the number of measurements required for reconstruction and improve efficiency while maintaining accuracy.

Model-based decentralized Bayesian algorithm for distributed compressed sensing

In this paper, a novel model-based distributed compressive sensing (DCS) algorithm is proposed. DCS exploits the inter-signal correlations and has the capability to jointly recover multiple sparse signals. Proposed approach is a Bayesian decentralized algorithm which uses the type 1 joint sparsity model (JSM-1) and exploits the intra-signal correlations, as well as the inter-signal correlations. Compared to the conventional DCS algorithm, which only exploit the joint sparsity of the signals, the proposed approach takes the intra- and inter-scale dependencies among the wavelet coefficients into account to enable the utilization of the individual signal structure. Furthermore, the Bessel K-form (BKF) is used as the prior distribution which has a sharper peak at zero and heavier tails than the Gaussian distribution. The variational Bayesian (VB) inference is employed to perform the posterior distributions and acquire a closed-form solution for model parameters. Simulation results demonstrate that the proposed algorithm have good recovery performance in comparison with state-of the-art techniques.

Exploiting the inter-correlation of structural vibration signals for data loss recovery: A distributed compressive sensing based approach

Compressive Sensing (CS) is a novel signal sampling technique that can enhance the resiliency of the data transmission process in Structural Health Monitoring (SHM) systems by projecting the raw signal into another domain and transmitting the projected signal instead of the original one. The original signal can later be recovered in the fusion center using the received projected vector and the corresponding projection matrix. In this study, data loss recovery of multiple signals with variable loss ratio is investigated. Unlike previously proposed CS-based single-signal approaches, the inter-correlation across vibration signals is exploited in this study. The inter-correlation type for vibration signals is characterized as having common sparse supports (the same location of non-zero elements in the sparse domain) and is dealt with in the context of Distributed Compressive Sensing (DCS). In this approach, vibration signals are encoded separately without extra inter-sensor communication overhead and are decoded jointly. Adopting the DCS-based approach enabled handling non-uniform data loss pattern across channels by allowing the use of a different projection matrix for each channel. Furthermore, a new projection matrix, named Permuted Random Demodulator (PRD), is proposed that not only reduces the coherence of the sensing matrix and enhances the reconstruction accuracy, but also makes the proposed approach robust to continuous data loss. The performance of the proposed method is evaluated using acceleration responses of a real-life bridge structure under traffic excitation. Modal parameters of the bridge are also identified using the recovered signals with reasonable accuracy.

On the energy self-sustainability of IoT via distributed compressed sensing

This paper advocates the use of the distributed compressed sensing (DCS) paradigm to deploy energy harvesting (EH) Internet of Thing (IoT) devices for energy self-sustainability. We consider networks with signal/energy models that capture the fact that both the collected signals and the harvested energy of different devices can exhibit correlation. We provide theoretical analysis on the performance of both the classical compressive sensing (CS) approach and the proposed distributed CS (DCS)-based approach to data acquisition for EH IoT. Moreover, we perform an in-depth comparison of the proposed DCS- based approach against the distributed source coding (DSC) system. These performance characterizations and comparisons embody the effect of various system phenomena and parameters including signal correlation, EH correlation, network size, and energy availability level. Our results unveil that, the proposed approach offers significant increase in data gathering capability with respect to the CS-based approach, and offers a substantial reduction of the mean-squared error distortion with respect to the DSC system.

A novel multi-focus image fusion method based on distributed compressed sensing

Multi-focus image fusion aims to produce an all-in-focus image by merging multiple partially focused images of the same scene. The main work is identifying the focused region and then composing all the focused regions. In this paper, a novel efficient multi-focus image fusion method based on distributed compressed sensing (DCS) is proposed. Firstly, the low-frequency and high-frequency images are obtained by comparing the variance of the source images, which are further utilized to get the low-frequency and high-frequency dictionaries. Secondly, DCS using joint sparsity model-1 (JSM-1) is applied to reconstruct the precise high-frequency images. Thirdly, the decision map is obtained based on all the high-frequency images and then improved by the morphological processing. Finally, the focused pixels are chosen from the source images through the decision map. Experimental results indicate that the proposed DCS-based method can be competitive with or even outperform some state-of-the-art methods in terms of both visual and quantitative metric evaluations.

Distributed Compressive Sensing: Performance Analysis With Diverse Signal Ensembles

Distributed compressive sensing (DCS) is a framework that considers joint sparsity within signal ensembles along with multiple measurement vectors (MMVs). However, current theoretical bounds of the probability of perfect recovery for MMVs are derived to be essentially identical to that of a single MV (SMV); this is because characteristics of the signal ensemble are ignored. In this paper, we introduce two key ingredients, called “Euclidean distances between signals” and “decay rate of signal ensemble,” to conduct a performance analysis of a deterministic signal model under the MMVs framework. We show that, by taking the size of signal ensembles into consideration, MMVs indeed exhibit better performance than SMV. Although our extension can be broadly applied to CS algorithms with MMVs, a case study conducted on a greedy solver, which is commonly known as simultaneous orthogonal matching pursuit (SOMP), will be explored in this paper. When incorporated with our concept by modifying the steps of support detection and signal estimation, we show that the performance of SOMP will be improved to a meaningful extent, especially for short Euclidean distances between signals. Performance of the modified SOMP is verified to meet our theoretical prediction. Moreover, we design a new method based on modified SOMP algorithms for a key application known as cooperative spectrum sensing (CSS). The simulation results demonstrate that our method can benefit from more than one measurement vector, especially when the length of the measurement vectors is smaller than the sparsity of the signals, which is where traditional CS algorithms fail.

Backtracking based Joint-Sparse Signal Recovery for Distributed Compressive Sensing

In Distributed Compressive Sensing (DCS), the Joint Sparsity Model (JSM) refers to an ensemble of signals being jointly sparse. In [4], a joint reconstruction scheme was proposed using a single linear program. However, for reconstruction of any individual sparse signal using that scheme, the computational complexity is high. In this paper, we propose a dual-sparse signal reconstruction method. In the proposed method, if one signal is known apriori, then any other signal in the ensemble can be efficiently estimated using the proposed method, exploiting the dual-sparsity. Simulation results show that the proposed method provides fast and efficient recovery.

Exploiting joint sparsity for far-field microphone array sound source localization

The presence of far-field noise and reverberation poses significant challenges to the conventional microphone array sound source localization approaches. Consider the sparsity contained in the source direction vector, source localization can be transformed into a compressed sensing (CS) problem by constructing the redundancy frequency domain room impulse response (RIR) matrix as CS measurement matrix. In this paper a new sparse recovery model is derived by decomposing the RIR into delay response term and reverberation response term to facilitate reverberation mitigation via frequency domain accumulation. Furthermore, as the source direction vector of adjacent speech frames tends to exhibit similar sparse pattern, namely, the direction of source can be assumed to keep static within this short period, thus there exists substantial correlation of spatial sparsity among adjacent speech frames. In this paper, under the framework of distributed compressed sensing (DCS), multiple source direction vectors are treated as sparse solutions with common spatial support to derive a joint sparse recovery algorithm for far-field source localization. The experimental results obtained in the context of a uniform circle array (UCA) show that the proposed algorithm is capable of yielding better estimation performance compared with the traditional algorithms.

A MIMO-SAR Tomography Algorithm Based on Fully-Polarimetric Data.

A fully-polarimetric unitary multiple signal classification (UMUSIC) tomography algorithm is proposed, which can be used for acquiring high-resolution three-dimensional (3D) imagery, in a polarimetric multiple-input multiple-output synthetic aperture radar (MIMO-SAR) with a small number of baselines. In terms of the elevation resolution, UMUSIC provides an improvement over standard MUSIC by utilizing the conjugate of the complex sample data and converting the complex covariance matrix into a real matrix. The combination of UMUSIC and fully-polarimetric data permits a further reduction of the noise of the sample covariance matrix, which is obtained through pixel averaging of multiple two-dimensional (2D) images. Considering the consistency of four polarizations, this algorithm not only makes scattering centers have the same estimated height in four polarizations, but it also improves the estimation accuracy. Simulation results show that this algorithm outperforms the popular distributed compressed sensing (DCS). Image processing of measured data of an aircraft model using a multiple-input multiple-output synthetic aperture radar (MIMO-SAR) with six baselines is presented to validate the proposed algorithm.

Novel Adaptive Distributed Compressed Sensing Algorithm for Estimating Channels in Doubly-Selective Fading OFDM Systems

Doubly-selective (DS) fading channel is often occurred in many orthogonal frequency division multiplexing (OFDM) communication systems, such as high-speed rail communication systems and underwater acoustic (UWA) wireless networks. It is challenging to provide an accurate and fast estimation over the doubly-selective channel, due to the strong Doppler shift. This paper addresses the doubly selective channel estimation problem based on complex exponential basis expansion model (CE-BEM) in OFDM systems from the perspective of distributed compressive sensing (DCS). We propose a novel DCS-based improved sparsity adaptive matching pursuit (DCS-IMSAMP) algorithm. The advantage of the proposed algorithm is that it can exploit the joint channel sparsity information using dynamic threshold, variable step size and tailoring mechanism. Simulation results show that the proposed algorithm achieves 5dB performance gain with faster operation speed, in comparison with traditional DCS-based sparsity adaptive matching pursuit (DCS-SAMP) algorithm.

Study on communication channel estimation by improved SOMP based on distributed compressed sensing

Wireless communication channel usually show the feature of time-varying; however, the time-varying channel has the characteristic that the channel structure within the adjacent time slots having serious time correlation. Therefore, how to use the time slow-changing characteristics of the channel to design the suitable channel state information acquisition method is of great significance to further improve communication performance with low communication bit error rate (BER) for OFDM communication system. The distributed compressed sensing (DCS) is proposed for the phenomenon that multiple sparse signals with time correlation. Based on the DCS theory framework, this article will re-build a time-domain channel estimation method with joint structure by improving the synchronous orthogonal matching pursuit (simultaneous orthogonal matching pursuit, SOMP) algorithm to get better channel information acquisition performance. Simulation results demonstrate the effectiveness of the proposed channel estimation method. Compared with the conventional compressed sensing-based channel estimations which perform at each time separately, the method proposed has better performance in terms of BER.

Spectrum-Efficient Distributed Compressed Sensing Based Channel Estimation for OFDM Systems Over Doubly Selective Channels

In this paper, we deal with channel estimation (CE) for high-mobility orthogonal frequency division multiplexing (OFDM) systems. To make the numerous (unknown) estimation for the high-mobility OFDM systems practicable, the channels are assumed to be time- and frequency-selective–or doubly selective (DS) and approximated by a basis expansion model (BEM). As the DS channel requires the distributed acquisition of multiple correlated signals in the delay-Doppler channel domain, we proceed to estimate jointly sparse BEM coefficient vectors over a DS channel as against numerous channel coefficients. On account of channel time-variation, the resulting channel matrix in the frequency domain exhibits (approximately banded) pseudo-circular structure, which gives rise to a diagonally dominant yet full matrix rather than a diagonal matrix and thus induces inter-channel interference (ICI). On the premise of this observation, we propose a new pilot design scheme that identifies the optimal pilot placement and values for each pilot cluster to combat ICI. Furthermore, to obtain a channel estimator consistent with the jointly sparse delay-Doppler [i.e., two dimensional (2D)] channel model, an algorithm namely, distributed compressed sensing (DCS)-based stage determined matching pursuit (DCS-SdMP), is proposed. Our claims are supported by simulation results, which are obtained considering Jakes’ channels with fairly high Doppler spreads, which show the superiority of the proposed schemes over other different methods of CE.

Decentralized Distributed Compressed Sensing Algorithm for Wireless Sensor Networks

Distributed Compressed Sensing (DCS) is an effective method to reduce data transmission and energy consumption in wireless sensor networks (WSN). To further enhance the data compression ratio in clustered WSNs, a new data collection method which named Decentralized Distributed Compressed Sensing (DDCS) is proposed in this paper. This method can be divided into three parts: cluster head election, data compression, and joint reconstruction. In the cluster head elections, sparsity is introduced as a reference factor. In data compression, the cluster head data is used as the common data of sensors in the same cluster. After remove the common data, member sensors in the same cluster only compress and transmits their specific data. In joint reconstruction, using residual correlation to reconstruct the signal through the OMP algorithm. Simulation results show that the performance of DDCS is better than traditional DCS. It can greatly reduce the amount of data transmission, the cluster head energy consumption, and the network delay at the cluster head.