This PDF file contains the front matter associated with SPIE Proceedings Volume 9484 including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

A general echo model is derived for the synthetic aperture radar (SAR) imaging with high resolution based on the scalar form of Maxwell's equations. After the consideration of the general echo model in frequency domain, a compressive sensing (CS) matrix is constructed from random partial Fourier matrices for processing the range CS SAR imaging. Simulations validate the orthogonality of the proposed CS matrix and the CS SAR imaging based on the general echo model.

In this paper, we consider moving target localization in urban environments using a multiplicity of dual-frequency radars. Dual-frequency radars offer the benefit of reduced complexity and fast computation time, thereby permitting real-time indoor target localization and tracking. The multiple radar units are deployed in a distributed system configuration, which provides robustness against target obscuration. We develop the dual-frequency signal model for the distributed radar system under phase errors and employ a joint sparse scene reconstruction and phase error correction technique to provide accurate target location and velocity estimates. Simulation results are provided that validate the performance of the proposed scheme under both full and reduced data volumes.

Passive Bistatic Radar (PBR) systems use illuminators of opportunity, such as FM, TV, and DAB broadcasts. The most common illuminator of opportunity used in PBR systems is the FM radio stations. Single FM channel based PBR systems do not have high range resolution and may turn out to be noisy. In order to enhance the range resolution of the PBR systems algorithms using several FM channels at the same time are proposed. In standard methods, consecutive FM channels are translated to baseband as is and fed to the matched filter to compute the range-Doppler map. Multichannel FM based PBR systems have better range resolution than single channel systems. However superious sidelobe peaks occur as a side effect. In this article, we linearly predict the surveillance signal using the modulated and delayed reference signal components. We vary the modulation frequency and the delay to cover the entire range-Doppler plane. Whenever there is a target at a specific range value and Doppler value the prediction error is minimized. The cost function of the linear prediction equation has three components. The first term is the real-part of the ordinary least squares term, the second-term is the imaginary part of the least squares and the third component is the l2-norm of the prediction coefficients. Separate minimization of real and imaginary parts reduces the side lobes and decrease the noise level of the range-Doppler map. The third term enforces the sparse solution on the least squares problem. We experimentally observed that this approach is better than both the standard least squares and other sparse least squares approaches in terms of side lobes. Extensive simulation examples will be presented in the final form of the paper.

This paper addresses the problem of scene reconstruction in conjunction with wall-clutter mitigation for com- pressed multi-view through-the-wall radar imaging (TWRI). We consider the problem where the scene behind- the-wall is illuminated from different vantage points using a different set of frequencies at each antenna. First, a joint Bayesian sparse recovery model is employed to estimate the antenna signal coefficients simultaneously, by exploiting the sparsity and inter-signal correlations among antenna signals. Then, a subspace-projection technique is applied to suppress the signal coefficients related to the wall returns. Furthermore, a multi-task linear model is developed to relate the target coefficients to the image of the scene. The composite image is reconstructed using a joint Bayesian sparse framework, taking into account the inter-view dependencies. Experimental results are presented which demonstrate the effectiveness of the proposed approach for multi-view imaging of indoor scenes using a reduced set of measurements at each view.

We simulate and study various computational techniques for image reconstruction applied to a multiplexed Mid-wave-IR Imager. The imager consists of two arms where one is dedicated to high resolution imaging using a lower resolution Focal Plane Array and the other is a single measurement Multispectral imager which uses dispersive optics. We exploit the compressibility of images to estimate a high resolution image and its corresponding lower resolution multispectral data cube using standard computational techniques.

Hyperspectral imagery involves capturing and processing a tremendous amount of data, which sets severe system resource requirements. This has motivated the application of compressive sensing for different spectroscopic and spectroscopic imager systems. Several new compressive hyperspectral architectures have been designed to stretch the common limitations of classical systems. However, the application of the compressive sensing framework involves design of system architectures that differ significantly from the conventional ones. Since compressive sensing differs essentially from conventional sensing, it cannot be implemented for hyperspectral imaging by simply modifying one of the components of a conventional hyperspectral system, rather it requires a complete new design. In this work we present a comparison between four compressive hyperspectral architectures to conventional architectures. The compressive hyperspectral sensing compared are: Coded Aperture Snapshot Spectral Imaging (CASSI), Compressive HS Imaging by Separable Spatial And Spectral Operators (CHISSS), (Liquid-crystal Compressive spectral Imager) LiCSI and (Spectral Single-Pixel) SSP systems. Those methods are compared to conventional spatial/spectral scanning hyperspectral such as pushbroom, whiskbroom and color filter techniques. A fundamental comparison between these architectures is presented in terms of optical system volume and radiometric efficiency.

We present a compressive spectral polarization imager based on a prism which is rotated to different angles as the measurement shots are taken, and a colored detector with a micropolarizer array. The prism shears the scene along one spatial axis according to its wavelength components. The scene is then projected to different locations on the detector as measurement shots are taken. Composed of 0°, 45°, 90°, 135° linear micropolarizers, the pixels of the micropolarizer array matched to that of the colored detector, thus the first three Stokes parameters of the scene are compressively sensed. The four dimensional (4D) data cube is thus projected onto the two dimensional (2D) FPA. Designed patterns for the micropolarizer and the colored detector are applied so as to improve the reconstruction problem. The 4D spectral-polarization data cube is reconstructed from the 2D measurements via nonlinear optimization with sparsity constraints. Computer simulations are performed and the performance of designed patterns is compared with random patterns.

This paper reports an overview of recent results in compressive imaging and detection using a single-pixel camera. These applications use a digital micromirror device to spatially modulate the light from an observed scene using binary sensing patterns. The patterns are obtained from a special Hadamard matrix that contains blocks of rows of which each has a common local signature pattern. The blocks partition the Hadamard spectrum, thus permitting analysis of the scene in terms of these local signature patterns. In contrast, Hadamard patterns are typically described in terms of their sequency, which is a global property of each individual row. The proposed local-signature, row-block point of view can be beneficial since it permits us to adaptively select the best blocks with which to sense the signal/scene of interest, or to select the best blocks based on a priori information. As a result, in imaging applications more fine-scale detail can be extracted from the scene, and in detection applications fewer false positives can result. Note, this signature row-block partitioning is a general mathematical technique that can be applied to the Kronecker product of any two matrices, of any size. For example, in our imaging application, we extend this idea to a Hadamard matrix that is not a power of two, yet whose block-signatures possess the familiar Sylvester-Walsh power-of-two sequency patterns.

We consider the problem of foreground and background extraction from compressed-sensed (CS) surveillance video. We propose, for the first time in the literature, a principal component analysis (PCA) approach that computes the low-rank subspace of the background scene directly in the CS domain. Rather than computing the conventional L₂-norm-based principal components, which are simply the dominant left singular vectors of the CS measurement matrix, we compute the principal components under an L₁-norm maximization criterion. The background scene is then obtained by projecting the CS measurement vector onto the L₁ principal components followed by total-variation (TV) minimization image recovery. The proposed L₁-norm procedure directly carries out low-rank background representation without reconstructing the video sequence and, at the same time, exhibits significant robustness against outliers in CS measurements compared to L₂-norm PCA.

In this paper, a compressing and reconstruction method for a noise video based on Compressed Sensing (CS) theory is proposed. At first, the CS theory is presented. Then the noise video is estimated from noisy measurement by solving the convex minimization problem. The video recovery algorithms based on gradient-based method is used to compressing and reconstructing the noise signal. And a compressive sensing algorithm with gradient-based method is proposed. At last, the performance of the proposed approach is shown and compared with some conventional algorithms. Our method can obtain best results in terms of peak signal noise ratio (PSNR) than those achieved by common methods with only a little runtime.

In this paper, we use transmit/receive co-prime arrays for direction finding of mixed coherent and uncorrelated targets based on the concept of sum coarrays. The sum coarray is defined as the set of pair-wise sums of the position vectors of the elements in the transmit and receive apertures. The data measurements from the transmit/receive co-prime array are used to emulate observations at a virtual array, whose element positions are given by the sum coarray corresponding to the co-prime array. A group sparse reconstruction problem is formulated employing multiple snapshots, where each group corresponding to a specific angle of arrival extends across all the time samples. The number of resolvable targets for the proposed sparsity-based approach is limited by the number of elements in the sum coarray. Supporting simulation results are provided, which validate the effectiveness of the proposed direction finding method.

This paper proposes a new approach for seeing through obscurants in a severe degraded visual environment (DVE). The proposed method allows extraction of hidden information from the raw sensor data via computational imaging technologies. We show that although to the human eye a sensor captures a zero-visibility view of an object through dense obscurants, it is possible to recover the hidden visual information of the object and display its visual cue to the aircraft pilot to aid in landing and maneuvering. The proposed approach uses a compressive sensing algorithm incorporating an over-complete dictionary composed of pose transformation of the targeted object. Information on the recovered image is used in a feedback loop to further remove perception noise and maintain tracking of the object from a moving aircraft. The proposed algorithm is sensor agnostic and can be applied to images taken from Infrared, LIDAR, or RF imagery sensors. We quantify the upper bound of the DVE noise in which the proposed method is effective via simulation results.

Emerging methods for the spectral analysis of graphs are analyzed in this paper, as graphs are currently used to study interactions in many fields from neuroscience to social networks. There are two main approaches related to the spectral transformation of graphs. The first approach is based on the Laplacian matrix. The graph Fourier transform is defined as an expansion of a graph signal in terms of eigenfunctions of the graph Laplacian. The calculated eigenvalues carry the notion of frequency of graph signals. The second approach is based on the graph weighted adjacency matrix, as it expands the graph signal into a basis of eigenvectors of the adjacency matrix instead of the graph Laplacian. Here, the notion of frequency is then obtained from the eigenvalues of the adjacency matrix or its Jordan decomposition. In this paper, advantages and drawbacks of both approaches are examined. Potential challenges and improvements to graph spectral processing methods are considered as well as the generalization of graph processing techniques in the spectral domain. Its generalization to the time-frequency domain and other potential extensions of classical signal processing concepts to graph datasets are also considered. Lastly, it is given an overview of the compressive sensing on graphs concepts.

This paper considers local sparse reconstruction of time-frequency signatures of windowed non-stationary radar returns. These signals can be considered instantaneously narrow-band, thus the local time-frequency behavior can be recovered accurately with incomplete observations. The typically employed sinusoidal dictionary induces competing requirements on window length. It confronts converse requests on the number of measurements for exact recovery, and sparsity. In this paper, we use chirp dictionary for each window position to determine the signal instantaneous frequency laws. This approach can considerably mitigate the problems of sinusoidal dictionary, and enable the utilization of longer windows for accurate time-frequency representations. It also reduces the picket fence by introducing a new factor, the chirp rate α. Simulation examples are provided, demonstrating the superior performance of local chirp dictionary over its sinusoidal counterpart.

The compressive line sensing (CLS) active imaging system was proposed and validated through a series of test-tank experiments. As an energy-efficient alternative to the traditional line-scan serial image, the CLS system will be highly beneficial for long-duration surveillance missions using unmanned, power-constrained platforms such as unmanned aerial or underwater vehicles. In this paper, the application of an active spatial light modulator (SLM), the individually addressable laser diode array, in a CLS imaging system is investigated. In the CLS context, active SLM technology can be advantageous over passive SLMs such as the digital micro-mirror device. Initial experimental results are discussed.

Motivated by the need to reduce monetary and energy consumption costs of wireless sensor networks in undertaking output-only/operational modal analysis of engineering structures, this paper considers a multi-coset analog-toinformation converter for structural system identification from acceleration response signals of white noise excited linear damped structures sampled at sub-Nyquist rates. The underlying natural frequencies, peak gains in the frequency domain, and critical damping ratios of the vibrating structures are estimated directly from the sub-Nyquist measurements and, therefore, the computationally demanding signal reconstruction step is by-passed. This is accomplished by first employing a power spectrum blind sampling (PSBS) technique for multi-band wide sense stationary stochastic processes in conjunction with deterministic non-uniform multi-coset sampling patterns derived from solving a weighted least square optimization problem. Next, modal properties are derived by the standard frequency domain peak picking algorithm. Special attention is focused on assessing the potential of the adopted PSBS technique, which poses no sparsity requirements to the sensed signals, to derive accurate estimates of modal structural system properties from noisy sub- Nyquist measurements. To this aim, sub-Nyquist sampled acceleration response signals corrupted by various levels of additive white noise pertaining to a benchmark space truss structure with closely spaced natural frequencies are obtained within an efficient Monte Carlo simulation-based framework. Accurate estimates of natural frequencies and reasonable estimates of local peak spectral ordinates and critical damping ratios are derived from measurements sampled at about 70% below the Nyquist rate and for SNR as low as 0db demonstrating that the adopted approach enjoys noise immunity.

The presence of multiple modes in guided-wave structural health monitoring has been usually considered a nuisance and a variety of methods have been devised to ensure the presence of a single mode. However, valuable information regarding the nature of defects can be gleaned by including multiple modes in image recovery. In this paper, we propose an effective approach for localizing defects in thin plates, which involves inversion of a multimodal Lamb wave based model by means of sparse reconstruction. We consider not only the direct symmetric and anti-symmetric fundamental propagating Lamb modes, but also the defect-spawned mixed modes arising due to asymmetry of defects. Model-based dictionaries for the direct and spawned modes are created, which take into account the associated dispersion and attenuation through the medium. Reconstruction of the region of interest is performed jointly across the multiple modes by employing a group sparse reconstruction approach. Performance validation of the proposed defect localization scheme is provided using simulated data for an aluminum plate.

Traditional methods in seismic acquisition require sources and geophones that are uniformly located along a spatial line, using the Nyquist sampling rate. Depending on the area to be explored, it can be necessary to use seismic surveys with large offsets, or decrease the separation between adjacent geophones to improve the resolution, which generates very high volumes of data. It makes the exploration process more difficult and particularly expensive. This work presents the reconstruction of a compressive set of seismic traces acquired using the compressive sensing paradigm where the pair of sources and geophones are randomly located along the spatial line. The recovery of the wavefield from compressive measurements is feasible due to the capabilities of Curvelets on representing wave propagators with only a small set of coefficients. The method first uses the compressive samples to find a sparse vector representation of each pixel in a 2-D Curvelet dictionary. The sparse vector representation is estimated by solving a sparsity constrained optimization problem using the Gradient Projection for Sparse Reconstruction (GPSR) method. The estimated vector is then used to compute the seismic velocity profiles via acoustic Full Waveform Inversion (FWI). Simulations of the reconstructed image gathers and the resulting seismic velocity profiles illustrate the performance of the method. An improvement in the resulting images is obtained in comparison with traditional F-K filtering used in seismic data processing when traces are missing.

Spectrum analysis of speech signals is important for their detection, recognition, and separation. Speech signals are nonstationary with time-varying frequencies which, when analyzed by Fourier analysis over a short time window, exhibit harmonic spectra, i.e., the fundamental frequencies are accompanied by multiple associated harmonic frequencies. With proper modeling, such harmonic signal components can be cast as group sparse and solved using group sparse signal reconstruction methods. In this case, all harmonic components contribute to effective signal detection and fundamental frequency estimation with improved reliability and spectrum resolution. The estimation of the fundamental frequency signature is implemented using the block sparse Bayesian learning technique, which is known to provide high-resolution spectrum estimations. Simulation results confirm the superiority of the proposed technique when compared to the conventional STFT-based methods.

The main drawback of current healthcare systems is the location-specific nature of the system due to the use of fixed/wired biomedical sensors. Since biomedical sensors are usually driven by a battery, power consumption is the most important factor determining the life of a biomedical sensor. They are also restricted by size, cost, and transmission capacity. Therefore, it is important to reduce the load of sampling by merging the sampling and compression steps to reduce the storage usage, transmission times, and power consumption in order to expand the current healthcare systems to Wireless Healthcare Systems (WHSs). In this work, we present an implementation of a low-power biomedical sensor using analog Compressed Sensing (CS) framework for sparse biomedical signals that addresses both the energy and telemetry bandwidth constraints of wearable and wireless Body-Area Networks (BANs). This architecture enables continuous data acquisition and compression of biomedical signals that are suitable for a variety of diagnostic and treatment purposes. At the transmitter side, an analog-CS framework is applied at the sensing step before Analog to Digital Converter (ADC) in order to generate the compressed version of the input analog bio-signal. At the receiver side, a reconstruction algorithm based on Restricted Isometry Property (RIP) condition is applied in order to reconstruct the original bio-signals form the compressed bio-signals with high probability and enough accuracy. We examine the proposed algorithm with healthy and neuropathy surface Electromyography (sEMG) signals. The proposed algorithm achieves a good level for Average Recognition Rate (ARR) at 93% and reconstruction accuracy at 98.9%. In addition, The proposed architecture reduces total computation time from 32 to 11.5 seconds at sampling-rate=29 % of Nyquist rate, Percentage Residual Difference (PRD)=26 %, Root Mean Squared Error (RMSE)=3 %.

In compressed sensing MRI, it is very important to design sampling pattern for random sampling. For example, SAKE (simultaneous auto-calibrating and k-space estimation) is a parallel MRI reconstruction method using random undersampling. It formulates image reconstruction as a structured low-rank matrix completion problem. Variable density (VD) Poisson discs are typically adopted for 2D random sampling. The basic concept of Poisson disc generation is to guarantee samples are neither too close to nor too far away from each other. However, it is difficult to meet such a condition especially in the high density region. Therefore the sampling becomes inefficient. In this paper, we present an improved random sampling pattern for SAKE reconstruction. The pattern is generated based on a conflict cost with a probability model. The conflict cost measures how many dense samples already assigned are around a target location, while the probability model adopts the generalized Gaussian distribution which includes uniform and Gaussian-like distributions as special cases. Our method preferentially assigns a sample to a k-space location with the least conflict cost on the circle of the highest probability. To evaluate the effectiveness of the proposed random pattern, we compare the performance of SAKEs using both VD Poisson discs and the proposed pattern. Experimental results for brain data show that the proposed pattern yields lower normalized mean square error (NMSE) than VD Poisson discs.

In this work, we present an advanced K-means clustering algorithm based on Compressed Sensing theory (CS) in combination with the K-Singular Value Decomposition (K-SVD) method for Clustering of long-term recording of surface Electromyography (sEMG) signals. The long-term monitoring of sEMG signals aims at recording of the electrical activity produced by muscles which are very useful procedure for treatment and diagnostic purposes as well as for detection of various pathologies. The proposed algorithm is examined for three scenarios of sEMG signals including healthy person (sEMG-Healthy), a patient with myopathy (sEMG-Myopathy), and a patient with neuropathy (sEMG-Neuropathr), respectively. The proposed algorithm can easily scan large sEMG datasets of long-term sEMG recording. We test the proposed algorithm with Principal Component Analysis (PCA) and Linear Correlation Coefficient (LCC) dimensionality reduction methods. Then, the output of the proposed algorithm is fed to K-Nearest Neighbours (K-NN) and Probabilistic Neural Network (PNN) classifiers in order to calclute the clustering performance. The proposed algorithm achieves a classification accuracy of 99.22%. This ability allows reducing 17% of Average Classification Error (ACE), 9% of Training Error (TE), and 18% of Root Mean Square Error (RMSE). The proposed algorithm also reduces 14% clustering energy consumption compared to the existing K-Means clustering algorithm.