Sparse Scene Research Articles

Affordance Estimation Enhances Artificial Visual Attention: Evidence from a Change-Blindness Study

Visual attention models are typically based on the concept of saliency, a conspicuity measure which considers features such as color, intensity or orientation. Much current research aims at modeling top-down interactions, which highly influence human attentional behavior. Typically, these are in the form of targets to be searched for or general characteristics (gist) of a scene. In humans, it has been shown that objects that afford actions, for example, graspable objects, strongly attract attention. Here, we integrate an artificial attention framework with a measure of affordances estimated from a sparse 3D scene representation. This work contributes further evidence for human attention being biased toward objects of high affordance, which for the first time is measured in an objective way. Furthermore, it demonstrates that artificial attention systems benefit from affordance estimation for predicting human attention. For technical systems, considering affordances provides mid-level influences that are not too specific or too general, but can guide attention toward potential action targets with respect to a system’s physical capabilities. Finally, the change-detection task we employ for model comparison constitutes a new method to evaluate artificial systems with respect to early human vision in natural scene perception.

SAR Image Reconstruction From Undersampled Raw Data Using MaximumA PosterioriEstimation

A method for synthetic aperture radar (SAR) imaging using maximum a posteriori (MAP) estimation based on multiplicative speckle model is presented. The new method uses the total variation (TV) minimization to regularize the solution. The reconstruction of SAR image is formulated as a biconvex optimization problem, which is solved by the alternate convex search (ACS) method. Experiments on Radarsat-1 raw data show that the proposed method can recover most of the structural and texture details of the imaged scene using only a half of raw data. Compared with regular regularization methods for SAR imaging with incomplete data, the proposed method performs much better on less sparse scenes.

Informational analysis for compressive sampling in radar imaging.

Compressive sampling or compressed sensing (CS) works on the assumption of the sparsity or compressibility of the underlying signal, relies on the trans-informational capability of the measurement matrix employed and the resultant measurements, operates with optimization-based algorithms for signal reconstruction and is thus able to complete data compression, while acquiring data, leading to sub-Nyquist sampling strategies that promote efficiency in data acquisition, while ensuring certain accuracy criteria. Information theory provides a framework complementary to classic CS theory for analyzing information mechanisms and for determining the necessary number of measurements in a CS environment, such as CS-radar, a radar sensor conceptualized or designed with CS principles and techniques. Despite increasing awareness of information-theoretic perspectives on CS-radar, reported research has been rare. This paper seeks to bridge the gap in the interdisciplinary area of CS, radar and information theory by analyzing information flows in CS-radar from sparse scenes to measurements and determining sub-Nyquist sampling rates necessary for scene reconstruction within certain distortion thresholds, given differing scene sparsity and average per-sample signal-to-noise ratios (SNRs). Simulated studies were performed to complement and validate the information-theoretic analysis. The combined strategy proposed in this paper is valuable for information-theoretic orientated CS-radar system analysis and performance evaluation.

Guaranteed Stability of Sparse Recovery in Distributed Compressive Sensing MIMO Radar

Low SNR condition has been a big challenge in the face of distributed compressive sensing MIMO radar (DCS-MIMO radar) and noise in measurements would decrease performance of radar system. In this paper, we first devise the scheme of DCS-MIMO radar including the joint sparse basis and the joint measurement matrix. Joint orthogonal matching pursuit (JOMP) algorithm is proposed to recover sparse targets scene. We then derive a recovery stability guarantee by employing the average coherence of the sensing matrix, further reducing the least amount of measurements which are necessary for stable recovery of the sparse scene in the presence of noise. Numerical results show that this scheme of DCS-MIMO radar could estimate targets’ parameters accurately and demonstrate that the proposed stability guarantee could further reduce the amount of data to be transferred and processed. We also show the phase transitions diagram of the DCS-MIMO radar system in simulations, pointing out the problem to be further solved in our future work.

Compressive-Sensing-Based High-Resolution Polarimetric Through-the-Wall Radar Imaging Exploiting Target Characteristics

In this letter, we consider high-resolution through-the-wall radar imaging (TWRI) using compressive sensing (CS) techniques that exploit the target and sensing characteristics. Many TWRI problems can be cast as inverse scattering involving few targets and, thus, benefit from CS and sparse reconstruction techniques. In particular, recognizing that most indoor targets are spatially extended, we exploit the clustering property of the sparse scene to achieve enhanced imaging capability. In addition, multiple polarization sensing modalities are used to obtain increased observation dimensionality within the group sparsity framework. The recently developed cluster multitask Bayesian CS approach is modified to effectively solve the formulated group and clustered sparse problem. Experimental results are presented to demonstrate the superiority of the proposed approach.

Resolution Analysis of Imaging with $\ell_1$ Optimization

We study array imaging of a sparse scene of point-like sources or scatterers in a homogeneous medium. For source imaging, the sensors in the array are receivers that collect measurements of the wave field. For imaging scatterers, the array probes the medium with waves and records the echoes. In either case the image formation is stated as a sparsity promoting $\ell_1$ optimization problem, and the goal of the paper is to quantify the resolution. We consider both narrow-band and broad-band imaging, and a geometric setup with a small array. We take first the case of the unknowns lying on the imaging grid and derive resolution limits that depend on the sparsity of the scene. Then we consider the general case with the unknowns at arbitrary locations. The analysis is based on estimates of the cumulative mutual coherence and a related concept, which we call the interaction coefficient. The latter complements recent results in compressed sensing by deriving deterministic resolution limits that account for worst-case scenarios in terms of locations of the unknowns in the imaging region, and also by interpreting the results in some cases where uniqueness of the solution does not hold. We demonstrate the theoretical predictions with numerical simulations.

SAR image reconstruction by expectation maximization based matching pursuit

Synthetic Aperture Radar (SAR) provides high resolution images of terrain and target reflectivity. SAR systems are indispensable in many remote sensing applications. Phase errors due to uncompensated platform motion degrade resolution in reconstructed images. A multitude of autofocusing techniques has been proposed to estimate and correct phase errors in SAR images. Some autofocus techniques work as a post-processor on reconstructed images and some are integrated into the image reconstruction algorithms. Compressed Sensing (CS), as a relatively new theory, can be applied to sparse SAR image reconstruction especially in detection of strong targets. Autofocus can also be integrated into CS based SAR image reconstruction techniques. However, due to their high computational complexity, CS based techniques are not commonly used in practice. To improve efficiency of image reconstruction we propose a novel CS based SAR imaging technique which utilizes recently proposed Expectation Maximization based Matching Pursuit (EMMP) algorithm. EMMP algorithm is greedy and computationally less complex enabling fast SAR image reconstructions. The proposed EMMP based SAR image reconstruction technique also performs autofocus and image reconstruction simultaneously. Based on a variety of metrics, performance of the proposed EMMP based SAR image reconstruction technique is investigated. The obtained results show that the proposed technique provides high resolution images of sparse target scenes while performing highly accurate motion compensation.

Decoupled ISAR imaging using RSFW based on twice compressed sensing

Random stepped-frequency radar without delay-Doppler coupling can suppress the range ambiguity and become less sensitive to electronic countermeasures. Considering its inherent randomness, this paper focuses on sidelobe reduction in inverse synthetic aperture radar imaging for sparse target scenes based on the compressed sensing (CS) theory. First, precise motion compensation and a high-resolution range profile (HRRP) with a low sidelobe are simultaneously achieved by the CS scheme for each train containing fewer pulses. Then, we analyze the disadvantages of conventional cross-range compression algorithms, which cannot guarantee high-quality focusing performance because there may be some false HRRPs caused by the uncertainty of the CS theory or some other factors. Finally, the modified correlation coefficient is defined to discard a large percentage of those uncorrelated HRRPs and the cross-range resolution is achieved by using the CS theory again. The validity of this decoupled imaging algorithm is demonstrated by some simulation and experimental results, which indicate that the approach is capable of precise estimation of scattering centers and effective suppression of a high sidelobe.

Low-complexity sparse reconstruction for high-resolution multi-static passive SAR imaging*

Abstract Bi-static passive synthetic aperture radar (SAR) systems using ground broadcast and wireless network signals suffer from a low spatial resolution due to the narrow bandwidths and low carrier frequencies. By exploiting multiple distributed illuminators, multi-static passive radar has the possibility of producing high-resolution SAR images. In this paper, a two-stage image formation approach, which combines the Fourier transform and sparse reconstruction strategies, is proposed to process multi-static SAR data. This method exploits the group sparsity of the sparse scene, i.e., the observations associated with different bi-static pairs share the same support of the sparse scene but correspond to aspect-dependent scattering coefficients. Such observations are described as a number of generally disjoint sub-bands in the two-dimensional spatial frequency domain. In each sub-band, the sampling satisfies the Nyquist criterion, whereas different sub-bands are sparsely distributed. In the proposed approach, Fourier-based reconstruction is applied to the sub-band data to produce the coarse-resolution images, which are then combined to produce a high-resolution image through the exploitation of sparse reconstruction techniques. The proposed approach greatly improves the imaging quality as compared to Fourier-based reconstruction, whereas it exhibits significant reduction of the computational complexity when compared to direct application of sparse reconstruction techniques. The exploitation of block sparsity-based techniques also permits practical treatment of the angle-dependent target scattering characteristics in SAR image reconstruction. The advantages of the proposed approach are delineated using analysis and simulations.

Adaptive millimeter-wave synthetic aperture imaging for compressive sampling of sparse scenes.

We apply adaptive sensing techniques to the problem of locating sparse metallic scatterers using high-resolution, frequency modulated continuous wave W-band RADAR. Using a single detector, a frequency stepped source, and a lateral translation stage, inverse synthetic aperture RADAR reconstruction techniques are used to search for one or two wire scatterers within a specified range, while an adaptive algorithm determined successive sampling locations. The two-dimensional location of each scatterer is thereby identified with sub-wavelength accuracy in as few as 1/4 the number of lateral steps required for a simple raster scan. The implications of applying this approach to more complex scattering geometries are explored in light of the various assumptions made.

High-resolution range-Doppler processing by coherent block-sparse estimation

This work contemplates advanced signal processing techniques for narrow band pulsed radar systems. If we assume a sparse scene of point-like targets and formulate the data model for the received signal in Fourier space, we can produce a block sparse estimation problem for the range profile spectra in all relevant Doppler channels. The range profile spectra are jointly estimated by coherent versions of block matching pursuit and basis pursuit algorithms. Then, in each Doppler channel, we compute the delays using a parametric high-resolution method. The mixed sparse/parametric approach overcomes the disadvantages of matched filters concerning resolution and ambiguities and has less computational complexity than full two-dimensional sparse estimation methods. We show results from numerical simulations and experimental measurements recorded with a passive bistatic radar using Global System for Mobil Communications (GSM) base stations as illuminators of opportunity.

Information theoretic bounds for compressed sensing in SAR imaging

Compressed sensing (CS) is a new framework for sampling and reconstructing sparse signals from measurements significantly fewer than those prescribed by Nyquist rate in the Shannon sampling theorem. This new strategy, applied in various application areas including synthetic aperture radar (SAR), relies on two principles: sparsity, which is related to the signals of interest, and incoherence, which refers to the sensing modality. An important question in CS-based SAR system design concerns sampling rate necessary and sufficient for exact or approximate recovery of sparse signals. In the literature, bounds of measurements (or sampling rate) in CS have been proposed from the perspective of information theory. However, these information-theoretic bounds need to be reviewed and, if necessary, validated for CS-based SAR imaging, as there are various assumptions made in the derivations of lower and upper bounds on sub-Nyquist sampling rates, which may not hold true in CS-based SAR imaging. In this paper, information-theoretic bounds of sampling rate will be analyzed. For this, the SAR measurement system is modeled as an information channel, with channel capacity and rate-distortion characteristics evaluated to enable the determination of sampling rates required for recovery of sparse scenes. Experiments based on simulated data will be undertaken to test the theoretic bounds against empirical results about sampling rates required to achieve certain detection error probabilities.

Driven to distraction? The effect of simulated red light running camera flashes on attention and oculomotor control

Do similar factors influence the allocation of attention in visually sparse and abstract laboratory paradigms and complex real-world scenes? To explore this question we conducted a series of experiments that examined whether the flash that accompanies a Red Light Running Camera (RLRC) can capture observers' attention away from important roadway changes. Inhibition of Return (IOR) and eye movement direction served as indices of the spatial allocation of attention. In two experiments, participants were slower to respond to the brake lights of a vehicle in a driving scene when an RLRC flash occurred nearby or were slower to initiate eye movements to brake light signals (IOR effects). In a third experiment, we found evidence that less prevalent RLRC flashes disrupted eye movement control. Results suggest that attention can be misdirected as a result of RLRC flashes and provide additional evidence that findings from simple laboratory paradigms can predict the allocation of attention in complex settings that are more familiar to observers.

Activity-Based Scene Decomposition for Topology Inference of Video Surveillance Network

The topology inference is the study of spatial and temporal relationships among cameras within a video surveillance network. We propose a novel approach to understand activities based on the visual coverage of a video surveillance network. In our approach, an optimal camera placement scheme is firstly presented by using a binary integer programming algorithm in order to maximize the surveillance coverage. Then, each camera view is decomposed into regions based on the Histograms of Color Optical Flow (HCOF), according to the spatial-temporal distribution of activity patterns observed in a training set of video sequences. We conduct experiments by using hours of video sequences captured at an office building with seven camera views, all of which are sparse scenes with complex activities. The results of real scene experiment show that the features of histograms of color optic flow offer important contextual information for spatial and temporal topology inference of a camera network.

Efficient ℓ q regularisation algorithm with range–azimuth decoupled for SAR imaging

The synthetic aperture radar (SAR) imaging methods based on compressive sensing endure great computational complexity since they have to deal with a large measurement matrix when processing the raw data. An efficient l q regularisation algorithm for SAR imaging is proposed, which uses a decoupling operator constructed by means of chirp scaling. It can reduce the computational complexity from the square order to the linear logarithm order compared with the conventional l q regularisation-based imaging algorithm while preserving the equivalent imaging quality. The proposed algorithm is efficient in the sense that it allows reconstruction of the sparse scenes with the sub-sampling data.

A robust compressive sensing based technique for reconstruction of sparse radar scenes

Pulse-Doppler radar has been successfully applied to surveillance and tracking of both moving and stationary targets. For efficient processing of radar returns, delay–Doppler plane is discretized and FFT techniques are employed to compute matched filter output on this discrete grid. However, for targets whose delay–Doppler values do not coincide with the computation grid, the detection performance degrades considerably. Especially for detecting strong and closely spaced targets this causes miss detections and false alarms. This phenomena is known as the off-grid problem. Although compressive sensing based techniques provide sparse and high resolution results at sub-Nyquist sampling rates, straightforward application of these techniques is significantly more sensitive to the off-grid problem. Here a novel parameter perturbation based sparse reconstruction technique is proposed for robust delay–Doppler radar processing even under the off-grid case. Although the perturbation idea is general and can be implemented in association with other greedy techniques, presently it is used within an orthogonal matching pursuit (OMP) framework. In the proposed technique, the selected dictionary parameters are perturbed towards directions to decrease the orthogonal residual norm. The obtained results show that accurate and sparse reconstructions can be obtained for off-grid multi target cases. A new performance metric based on Kullback–Leibler Divergence (KLD) is proposed to better characterize the error between actual and reconstructed parameter spaces. Increased performance with lower reconstruction errors are obtained for all the tested performance criteria for the proposed technique compared to conventional OMP and ℓ1 minimization techniques.

The Effect of Walking and Teleportation on Spatial Updating in Virtual and Real Scenes

Intuitively, it seems as if we should be able to point accurately to the location of a target object within a room even if we were teleported to a different location and the object removed from view. We measured the precision of pointing to a previously-seen object in a real room, a virtual room with same dimensions (presented in immersive virtual reality) and a sparse virtual scene consisting only of long thin poles at the same locations as the target object and room corners. Participants viewed the target object from one location, walked to another so that the object passed out of view, then turned in complete darkness to point at the location of the previously-viewed target. In a separate experiment, participants viewed a sparse scene consisting of long thin poles (including a target) and had to point to the location of the absent target after teleportation to a new location within the scene. Pointing precision in this case was dramatically reduced (σ ≈ 34°) compared to the conditions in which participants walked in the real room, virtual room or sparse scene. In the latter three conditions, pointing precision was very similar (σ ≈ 15°) despite the removal of prominent distance cues in the sparse condition. Our results show that spatial updating after teleportation is substantially poorer than when walking between two locations. [Supported by Microsoft Research and Wellcome Trust]

Moving Target Imaging for MIMO Radar Using CS-SLIM

Multiple-input Multiple-output (MIMO) radar can achieve superior performance through waveform diversity over conventional phased-array radar systems. Many methods were proposed to provide accurate MIMO angle-range-Doppler images of moving targets. Sparse Learning via Iterative Minimization (SLIM) can provide more accurate estimates in that case. However, when using the Bayesian Information Criterion (BIC) to determine the user parameter automatically for a less sparse scene, SLIM has high complexity. In this paper, we propose an improved approach as Sparse Learning via Iterative Minimization based on Compressive Sampling (CS-SLIM) for targets imaging. CS-SLIM can provide targets imaging as almost accurate as but with a lower computational burden than SLIM. The measurement matrix and the computational complexity for CS-SLIM are discussed. Mean Squared Errors (MSEs) at various measurement samples are provided to demonstrate the performance of the proposed approach in sparse scenes

Cooperative Spectrum Sensing Algorithm Based on CS-SLIM Iterative Minimization Sparse Learning

Dynamic spectrum management is a key technology in cognitive wireless sensor network (C-WSN), in which spectrum sensing plays an important role. In this paper, we propose an improved approach as sparse learning via iterative minimization based on compressive sampling (CS-SLIM) for wideband spectrum detection. CS-SLIM can provide wideband spectrum detection with almost the same accuracy but a lower computational burden than that of SLIM. The measurement matrix and the computational complexity for CS-SLIM are discussed. Mean squared errors (MSEs) at various measurement samples are provided to demonstrate the performance of the proposed approach in sparse scenes. It is also proved that the algorithm is suitable for sparse signal reconstruction and wideband spectrum sensing in C-WSN.

Waveform design for compressively sampled ultrawideband radar

Compressive sensing makes it possible to recover sparse target scenes from under-sampled measurements when uncorrelated random-noise waveforms are used as probing signals. The mathematical theory behind this assertion is based on the fact that Toeplitz and circulant random matrices generated from independent identically distributed (i.i.d) Gaussian random sequences satisfy the restricted isometry property. In real systems, waveforms have smooth, nonideal autocorrelation functions, thereby degrading the performance of compressive sensing algorithms. Compressive sensing requires the system matrix to have particular properties. Incorporating prior information into the target scene either to enhance imaging or to mitigate nonidealities can result in system matrices that are not suitable for compressive sensing. We can overcome this problem by designing appropriate transmit waveforms. We extend the existing theory to incorporate such nonidealities into the analysis of compressive recovery. As an example we consider the problem of tailoring waveforms to image extended targets. Extended targets make the target scene denser, causing random transmit waveforms to be suboptimal for recovery. We propose to incorporate extended targets by considering them to be sparsely representable in redundant dictionaries. We demonstrate that a low complexity algorithm to optimize the transmit waveform leads to improved performance.