Adaptive Compressive Sensing and Processing of Delay-Doppler Radar Waveforms

Wireless Visual Sensor Networks (WVSNs) have gained significant importance in the last few years and have emerged in several distinctive applications. The main aim is to design low power WVSN surveillance application using adaptive Compressive Sensing (CS) which is expected to overcome the WVSN resource constraints such as memory limitation, communication bandwidth and battery constraints. In this paper, an adaptive block CS technique is proposed and implemented to represent the high volume of captured images in a way for energy efficient wireless transmission and minimum storage. Furthermore, to achieve energy-efficient target detection and tracking with high detection reliability and robust tracking, to maximize the lifetime of sensor nodes as they can be left for months without any human interactions. Adaptive CS is expected to dynamically achieve higher compression rates depending on the sparsity nature of different datasets, while only compressing relative blocks in the image that contain the target to be tracked instead of compressing the whole image. Hence, saving power and increasing compression rates. Least mean square adaptive filter is used to predicts target's next location to investigate the effect of CS on the tracking performance. The tracking is achieved in both indoor and outdoor environments for single/multi targets. Results have shown that with adaptive block CS up to 20 % measurements of data are required to be transmitted while preserving the required performance for target detection and tracking.

The focus of this paper is to develop an innovative approach to receiver design for wideband signals with resonant frequency components. It is known that there are certain wideband signals that occupy quite large bandwidths but may have dominant amplitudes in certain frequency bands. Many studies have applied signal processing techniques to high bandwidth signals to lower the sampling rate. Compressive sensing (CS) is one such method and has been shown to succeed but only in a special case. The CS approach requires the signal to be sparse in some domain. In this work, we focus on signal classes where previously developed reconstruction and detection techniques may fail. In other words, we focus on signals that have large bandwidths with dominant frequency components, but are not necessarily sparse in time. We show that these signals can be effectively sampled by a lower sampling rate compared to what is required by the Nyquist-Shannon sampling theorem. In our design resonant frequency components are channelized into separate receiver paths (subchains) and sampled at a lower sampling rate than is required for the entire wideband signal. Both the signal and noise energy are reduced, although not equally, by the receiver. Noise energy outside of the selected bands is greatly attenuated while these dominant bands are filtered into subchains for processing. It is shown that the signal's sum squared error (SSE) improves and the probability of detection performance experiences little or no degradation as the effective noise power is attenuated by the proposed method.

We develop a novel algorithm for range and velocity joint estimating in randomized stepped frequency radar. By exploiting sparseness of the targets and invoking compressed sensing (CS) theory, higher resolution can be achieved. However, in actual radar system, some previous CS methods probably suffer from off-grid point effect, which causes performance degradation of the methods. We produce an adaptive CS method to mitigate the off-grid point effect. The performance of the proposed algorithm on accuracy and resolution of range-velocity joint estimate are illustrated by simulations and field experiments.

In this paper, we propose a lightweight and adaptive compressed sensing method based on deep learning for edge devices (LACSLE) that generate and transmit sensor data using wireless connection. To transmit data efficiently, edge devices should change the compression rate adaptively according to data sparsity. However, a large amount of calculation is required to optimize the compression ratio due to the repeated processes of basis conversion and L1-norm minimization required for calculating the reconstruction error. To address this problem, LACSLE optimizes the compression ratio employed in compressed sensing in real time to maximize the data transmission efficiency using deep learning. In LACSLE, a deep learning model is trained in advance to map sensor data to an optimal compression ratio using either supervised learning (LACSLE-SL) or reinforcement learning (LACSLE-RL). The model is installed on an edge device and used to estimate an optimal compression ratio. The performance evaluation of LACSLE demonstrated that the proposed method allows to significantly reduce the computational delay of the optimal compression ratio estimation while reducing both the compression ratio and reconstruction error.

Hardware Performance Counter-based (HPC) runtime checking is an effective way to identify malicious behaviors of malware and detect malicious modifications to a legitimate program’s control flow. To reduce the overhead in the monitored system which has limited storage and computing resources, we present a “sample-locally-analyze-remotely” technique. The sampled HPC data are sent to a remote server for further analysis. To minimize the I/O bandwidth required for transmission, the fine-grained HPC profiles are compressed into much smaller vectors with Compressive Sensing. The experimental results demonstrate an 80% I/O bandwidth reduction after applying Compressive Sensing, without compromising the detection and identification capabilities.

The emerging compressive sampling (CS) theory makes processing ultra-wide-band (UWB) signal at a low sampling rate possible if the underlying signal has a sparse representation in a certain basis. The feasibility of model based compressive sampling for ultra-wide-band (UWB) signal is investigated. In this paper, a multichannel compressive sampling architecture is developed to capture UWB signal at a rate much lower than Nyquist rate. The proposed framework considers sub-Nyquist sampling stream of delayed and weighted versions of a known signal with finite support in time domain. A basis function is constructed to realize sparse signal representation. To reduce the hardware cost, a segmented architecture is suggested. In addition, a joint signal recovery algorithm is presented. Experimental results indicate that, with this system, a UWB signal sampled at about 4% of Nyquist rate still can be recovered with overwhelming probability.

The research of frame synchronisation, which processing is based on the sampling at Nyquist rate in traditional method, has been become the hot topic over the past few decades. In the light of compressed sensing theory that sampling received signals below the Nyquist rate, a frame synchronisation method with correlation rule is proposed in this paper. Firstly, the synchronisation metrics of frame synchronisation is expressed as compressed mode by developing the sparsity of the synchronisation metric amplitudes over observation window. The measurement matrix for compressive sampling is then projected, and compressive sampling for the received signal is employed according to the projected measurement matrix. Finally, we reconstruct the synchronisation metrics from the compressive sampling by utilising compressive sampling matching pursuit (CoSaMP) algorithm and obtain the estimate of frame synchronisation with the reconstructed synchronisation metrics. Compared to the traditional methods, the analysis and simulation results show that the sampling rate in proposed method is significantly reduced while obtaining comparative correct synchronisation probability when received signal-to-noise ratio (SNR) is relatively high. Simultaneously, for the reason that the correct synchronisation probability is not particularly sensitive to the iteration number of the CoSaMP algorithm for frame synchronisation, low reconstruction complexity is also obtained in the proposed method.

The emerging theory of compressed sensing (CS) has led to the remarkable result that signals having a sparse representation in some known basis can be represented (with high probability) by a small sample set, taken from random projections of the signal. Notably, this sample set can be smaller than that required by the ubiquitous Nyquist sampling theorem. Much like the generalized Nyquist sampling theorem dictates that the sampling rate can be further reduced for the representation of bandlimited signals, this paper points to similar results for the sampling density in CS. In particular, it is shown that if additional spectral information of the underlying sparse signals is known, colored random projections can be used in CS in order to further reduce the number of measurements needed. Such a priori information is often available in signal processing applications and communications. Algorithms to design colored random projection vectors are developed. Further, an adaptive CS sampling method is developed for applications where non-uniform spectral characteristics of the signal are expected but are not known a priori.

Compressive Sensing (CS) is a stable and robust technique that allows for the sub-sampling of data at a given data rate: ‘compressive sampling’ or ‘compressive sensing’ at rates smaller than the Nyquist sampling rate. It makes it possible to create standalone and net-centric applications with fewer resources required in Internet of Things (IoT). CS-based signal and information acquisition/compression paradigm combines the nonlinear reconstruction algorithm and random sampling on a sparse basis that provides a promising approach to compress signal and data in information systems. In this paper, we investigates how CS can provide new insights into coexisting heterogeneous IoT environments. First, we briefly introduce the CS theory with respect to the sampling through providing a compressed sampling process with low computation costs. Then, a CS-based framework is proposed for IoT, in which the hub nodes measure, transmit, and store the sampled data into the fusion center. Then, an efficient cluster-sparse reconstruction algorithm is proposed for in-network compression aiming at more accurate data reconstruction and lower energy efficiency. Therefore, compression should be performed locally at each Access Point (AP) and reconstruction is executed jointly to consider dependencies in the acquired data by the final fusion center.

Compressive sampling (CS) is an attractive method to implement analog-to-information conversion (AIC) for a sub-Nyquist radar, where random demodulation (RD) is the most successful AIC. However, RD only considers the sparse characteristic of one single radar pulse, and its mixing circuit still works at the Nyquist sampling rate. To exploit the strong correlation of radar echo pulses, this letter presents a pulse repetition rate compressive sampler for radar target detection. It can acquire these highly correlated pulses by setting a different sampling start time for each pulse and compresses each pulse into one single sample, which means that the low compressive sampling rate is equal to the pulse repetition rate. Compared with RD, this new system reduces the complexity of the hardware circuit and improves the signal compression ratio. Experimental results verify that the proposed radar system outperforms RD by achieving target detection from compressive samples without signal recovery.

Compressive sensing (CS) theory proves that signals can be recovered from far fewer random measurements than that suggested by Nyquist-Shannon theorem. This advantage of CS is extremely useful in some data processing applications, especially in video processing. Considering that nonlocal patch-based CS methodology has achieved an impressive performance in image CS field, it motivates us to generalize the idea into the video CS filed. In this paper, by taking account of the spatiotemporal information, we propose a novel strategy to reconstruct the non-key frames of a video sequence by introducing the nonlocal similar patch group from some key frames. Experimental results illustrate that our proposed nonlocal patch-based video CS recovery algorithm can extremely exploit the information of the key frames, and achieve a better performance compared with some existing popular video CS recovery algorithms. It is noted that our idea to construct a spatiotemporal comprehensive patch group may be combined with any nonlocal patch-based CS method for video reconstruction, which leads to various of nonlocal patch-based compressive sensing video recovery algorithms.

Compressed Sensing (CS) is an emerging theory which can sample the sparse signal or compressible signal via sub-Nyquist sampling rate and reconstruct the original signal with small amount of measurements. Since speech signal is sparse in Discrete Cosine Transform (DCT) domain, a kind of adaptive Bayesian Compressed Sensing (BCS) based on speech signal is proposed in this paper. In the one hand, our proposed method exploits the difference of energy within different speech frame to allot measurements adaptively for each speech frame aim to promote the quality of recovery speech signal. In the other hand, the position information of sparse coefficient in each speech frame is also utilized by our proposed method to recover its neighboring speech frame for reducing the recovery time of speech signal. The experimental results prove that our proposed method is surely effective and practical.

In this research, a unique colour image reconstruction method based on Compressive Sensing is presented, aimed at enhancing efficiency and quality. The proposed method incorporates deep learning techniques, adaptive measurement rate setting, and salient region detection. The process begins with the collection of an input RGB images, followed by pre-processing to convert them to the Y+ colour space., including optimized Faster R-CNN (OFRCNN), are employed to detect and segment salient regions in the Y-channel. An adaptive measurement rate setting using Deep Q-Network (DQN) dynamically adjusts measurement rates based on input data characteristics. To optimize the Y-channel compression, a Dual attention-based CS Network (CSNet) model is applied. Moreover, adaptive weighted reconstruction is introduced, utilizing salient region masks and a hybrid the Walrus Optimization Algorithm (WaOA) and mountain gazelle optimizer (MGO), called Hybrid Walrus with Mountain Gazelle Optimizer (HWMGO). Finally, the restored Y-channel is combined with the reconstructed U and V channels to reconstruct full-colour in the RGB colour space. Experimental evaluations on a diverse dataset demonstrate the effectiveness of the proposed algorithm in accurately reconstructing high-quality colour images from CS data. This research contributes to the advancement of colour image reconstruction techniques, offering a comprehensive and efficient framework for various applications.

Compressive Imaging (CI) is a potential sensing technology for energy-efficient visual sensors, and its rate-distortion performance can be improved by adaptive Compressive Sensing (CS) of image. However, due to the unavailability of original image, it is a challenge for CI-based adaptive CS to extract an effective feature from measurements to evaluate the sparsity of image. In view of that, this paper presents an entropy-assisted adaptive CS system, whose merit is its definition of the sensed entropy without the original image. Based on sensed entropy, each image block is allocated sufficient measuring resources, guaranteeing a cost-effective reconstruction of image. Experimental results show that the proposed entropy-assisted adaptive CS system provides better objective and subjective recovery qualities with a low measuring and recovering complexity.

The first task of GPS receiver is to acquire the satellite signal, which tests the all likely 2-D hypotheses of the code phase and the Doppler frequency of the incoming GPS signal. Compressive sensing is a new signal sampling theory, for sparse signal, the signal can be sampled at a lower sampling rate than Nyquist sampling theory. In this paper, a new GPS signal acquisition method is proposed based on compressive sensing. We designed a GPS signal sparse representation dictionary according to the satellite; the position of sparse spike corresponds to the code phase and Doppler frequency. The modified greedy acquisition algorithm that is different from the compressive sensing signal reconstruction, which is used to acquire the GPS satellite. The numerical simulations illustrate that the new method can efficiently acquire the satellite with artificial or real GPS signal. The new method can compress the GPS signal greatly and reduce the transmission energy consumption accordingly, which is hoped to be applied to aircraft or animal monitoring that have little interference noise and need to record GPS data for a long time.