Fauzia Ahmad

Synthetic aperture beamformer for imaging through a dielectric wall

A coarray-based aperture synthesis scheme using subarrays and postdata acquisition beamforming is presented for through-the-wall wideband microwave imaging applications. The wall causes wave refraction and a change in the propagation speed, both effects alter the travel time between the transmitter, the target, and the receiver. Coherent combining of the pulse waveforms emitted by the different transmitters and incident at the receivers through reflections from targets and clutter requires incorporation of wall effects into the beamformer design. Simulation results verifying the proposed synthetic aperture technique for a through-the-wall imaging (TWI) system are presented. The impact of the wall ambiguities or incorrect estimates of the wall parameters, such as thickness and dielectric constant, on performance is considered.

Three-Dimensional Wideband Beamforming for Imaging Through a Single Wall

Through-the-wall imaging and urban sensing is an emerging area of research and development. The incorporation of the effects of signal propagation through wall material in producing an indoor image is important for reliable through-the-wall mission operations. We have previously analyzed wall effects, such as refraction and change in propagation speed, and designed a wideband beamformer for 2D imaging using line arrays. In this letter, we extend the analysis to 3D imaging via delay-and-sum beamforming in the presence of a single uniform wall. The third dimension provides valuable information on target heights that can be used for enhancing target discrimination/identification. Supporting simulation results are provided.

Autofocusing of Through-the-Wall Radar Imagery Under Unknown Wall Characteristics

The quality and reliability of through-the-wall radar imagery is governed, among other things, by the knowledge of the wall characteristics. Ambiguities in wall characteristics smear and blur the image, and also shift the imaged target positions. An autofocusing technique, based on higher order statistics, is presented which corrects for errors under unknown walls. Simulation results show that the proposed technique provides high-quality focused images with target locations in close proximity to true target positions.

Multi-location wideband synthetic aperture imaging for urban sensing applications☆

The presence of significant multipath propagation and heavy clutter in indoor environments imposes severe limitations on imaging through walls, rendering through-the-wall radar imaging a difficult and complex proposition. It is highly desirable to properly interpret the radar images and determine the contents of the indoor scene with a high level of confidence. Data collected from multiple vantage points around a structure can be used to improve imaging visibility into the indoor scene, which, in turn, enhances indoor target detection and localization. In this paper, we consider multi-location radar imaging. Image fusion techniques for combining synthetic aperture radar images acquired from multiple locations along two sides of an enclosed structure are presented. Supporting results, based on real-data collected in a semi-controlled laboratory environment, are provided which demonstrate the improved performance of the multiple location scheme compared to operation from a single vantage point.

Multipath Model and Exploitation in Through-the-Wall and Urban Radar Sensing

We derive a multipath model for sensing through walls using radars. The model considers propagation through a front wall and specular reflections at interior walls in an enclosed room under surveillance. The model is derived such that additional eigenrays can be easily accommodated. A synthetic aperture radar (SAR) system is considered, and stationary or slowly moving targets are assumed. The focused downrange and crossrange locations of multipath ghosts are established and validated using numerical, as well as experimental data. The multipath model permits an implementation of a multipath exploitation algorithm, which associates, as well as maps, each target ghost back to its corresponding true target location. In doing so, the proposed algorithm improves the radar system performance by aiding in ameliorating the false positives in the original SAR image, as well as increasing the signal-to-clutter ratio at the target locations, culminating in enhanced behind the wall target detection and localization.

Noncoherent approach to through-the-wall radar localization

A noncoherent through-the-wall radar system approach, based on stepped-frequency signal synthesis and trilateration technique, is presented. This approach involves multiple independent monostatic radar units and as such, provides flexibility in positioning the units with various standoff distances and inter-element spacing. The performance of the proposed noncoherent localization system was demonstrated using simulated and real data. The results show that the radar is able to detect and locate multiple targets behind walls

Through-the-Wall Human Motion Indication Using Sparsity-Driven Change Detection

We consider sparsity-driven change detection (CD) for human motion indication in through-the-wall radar imaging and urban sensing applications. Stationary targets and clutter are removed via CD, which converts a populated scene into a sparse scene of a few human targets moving inside enclosed structures and behind walls. We establish appropriate CD models for various possible human motions, ranging from translational motions to sudden short movements of the limbs, head, and/or torso. These models permit scene reconstruction within the compressive sensing framework. Results based on laboratory experiments show that a sizable reduction in the data volume is achieved using the proposed approach without a degradation in system performance.

Multipath exploitation in through-the-wall radar imaging using sparse reconstruction

Multipath exploitation and compressive sensing (CS) have both been applied independently to through-the-wall radar imaging (TWRI). Fast and efficient data acquisition is desired in scenarios where multipath effects cannot be neglected. Hence, we combine the two methods to achieve good image reconstruction in multipath environments from few spatial and frequency measurements. Ghost targets appear in the scene primarily due to specular reflections from interior walls and multiple reflections within the front wall. Assuming knowledge of the room geometry, we can invert the multipath model and eliminate ghosts by means of CS. We develop effective methods for the reconstruction of stationary scenes, which employ a group sparse CS approach. Additionally, we separate the target and wall contributions to the image by a sparse reconstruction approach joining wall and target models, which allows suppression of the ghosts and increased signal-to-clutter ratio (SCR) at the target locations. Effectiveness of the proposed approach is demonstrated using both simulated and real data.

Design and implementation of near-field, wideband synthetic aperture beamformers

A coarray-based near-field, wideband synthetic aperture beamformer using stepped-frequency signal synthesis and post-data acquisition processing is presented. While coarray techniques offer significant reduction in the number of array elements for a given angular resolution, the hybrid subarray-stepped frequency realization of wideband systems simplifies implementations and offers flexibility in beamforming. Proof of concept is provided using real data collected in an anechoic chamber for several pulse shapes and array weightings.

Joint Wall Mitigation and Compressive Sensing for Indoor Image Reconstruction

Compressive sensing (CS) for urban operations and through-the-wall radar imaging has been shown to be successful in fast data acquisition and moving target localizations. The research in this area thus far has assumed effective removal of wall electromagnetic backscatterings prior to CS application. Wall clutter mitigation can be achieved using full data volume which is, however, in contradiction with the underlying premise of CS. In this paper, we enable joint wall clutter mitigation and CS application using a reduced set of spatial-frequency observations in stepped frequency radar platforms. Specifically, we demonstrate that wall mitigation techniques, such as spatial filtering and subspace projection, can proceed using fewer measurements. We consider both cases of having the same reduced set of frequencies at each of the available antenna locations and also when different frequency measurements are employed at different antenna locations. The latter casts a more challenging problem, as it is not amenable to wall removal using direct implementation of filtering or projection techniques. In this case, we apply CS at each antenna individually to recover the corresponding range profile and estimate the scene response at all frequencies. In applying CS, we use prior knowledge of the wall standoff distance to speed up the convergence of the orthogonal matching pursuit for sparse data reconstruction. Real data are used for validation of the proposed approach.