Ahmed E. Fouda

Imaging and tracking of targets in clutter using differential time-reversal techniques

This paper discusses the performance of two radar imaging techniques for detecting and tracking multiple moving targets in cluttered environments based on differential time-reversal (TR) techniques. The first technique classifies existing targets into stationary versus moving ones based on information provided by multistatic data matrices (MDMs) corresponding to successive pulse radar acquisitions (snapshots). Under certain conditions, the singular value decomposition (SVD) of the (time-averaged MDM provides information (number and location) on stationary targets, whereas the SVD of the differential MDM provides similar information on moving targets. The second imaging technique is aimed at providing real-time tracking of the moving targets. This technique exhibits superior clutter rejection and requires minimal processing costs. We show that the tracking technique benefits from statistical stability (self-averaging) provided by ultra-wideband (UWB) TR operation.

Statistical Stability of Ultrawideband Time-Reversal Imaging in Random Media

We carry out a study on the statistical stability of ultrawideband (UWB) time-reversal (TR) imaging in random media under different combinations of random medium parameters and interrogating signal properties. We examine conditions under which frequency decorrelation in random media provides a more effective “self-averaging” and, therefore, better statistical stability. We also present a new frequency-synthesized technique for UWB TR-based imaging. This technique is employed to construct TR-operator-decomposition and multiple-signal-classification images using either linear or full-aspect transceiver array configurations. The proposed technique automatically provides the best images of desired target(s) in terms of focusing resolution without the need for (synthetic) propagation of TR signals and ad hoc determination of the optimal focusing time instant. In addition, information about the imaging domain (background) can be stored and reused to reconstruct different targets.

Ultra-wideband microwave imaging of breast cancer tumors via Bayesian inverse scattering

We develop a new algorithm for ultra-wideband (UWB) microwave imaging of breast cancer tumors using Bayesian inverse scattering. A key feature of the proposed algorithm is that constitutive properties of breast tissues are reconstructed from scattered UWB microwave signals together with the confidence level of the reconstruction. Having such confidence level enables minimization of both false alarms and missed detections. Results from the application of the proposed algorithm demonstrate the accuracy in estimating both location and permittivity of breast tumors without the need for a priori knowledge of pointwise properties of the background breast tissue.

GPR Signal Enhancement Using Sliding-Window Space-Frequency Matrices

Ground penetrating radar (GPR) has shown to provide useful results for detection of buried objects. However, its performance sufiers from strong re∞ection from ground surface especially for shallowly buried targets. In such cases, the detection problem depends on the separation of the target signal from the ground backscatter such as landmines and unexploded ordnances. In this paper, we discuss and analyze the use of space-frequency time-reversal matrices for the enhancement of ground penetrating radar signals and potential clutter reduction. Through the use of sliding windows, submatrices from a given B-scan (radargram) are utilized to extract localized scattering information of a given detection scenario. Each sub-B-scan is decomposed to its singular vectors and later used to render synthetic aperture time-domain singular vector distributions corresponding to difierent scattering mechanisms. Later, they are weighted by the singular values and subtracted from the full B-scan to achieve reduced clutter and enhanced target response. The method shows satisfactory results for shallowly buried dielectric targets even in the presence of rough surface proflles.

Bayesian compressive sensing for ultrawideband inverse scattering in random media

We develop an ultrawideband (UWB) inverse scattering technique for reconstructing continuous random media based on Bayesian compressive sensing. In addition to providing maximum a posteriori estimates of the unknown weights, Bayesian inversion provides estimate of the confidence level of the solution, as well as a systematic approach for optimizing subsequent measurement(s) to maximize information gain. We impose sparsity priors directly on spatial harmonics to exploit the spatial correlation exhibited by continuous media, and solve for their posterior probability density functions efficiently using a fast relevance vector machine. We linearize the problem using the first-order Born approximation which enables us to combine, in a single inversion, measurements from multiple transmitters and ultrawideband frequencies. We extend the method to high-contrast media using the distorted-Born iterative method. We apply time-reversal strategies to adaptively focus the inversion effort onto subdomains of interest, and hence reduce the overall inversion cost. The proposed techniques are illustrated in a number of canonical scenarios including crosshole and borehole sensing.

Time-reversal techniques applied to ultrawideband indoor wireless communication systems: A comparative study

Time-reversal (TR) signal processing techniques have been applied to ultrawideband (UWB) wireless communication systems, providing myriad of advantages including: reduced receiver complexity and power consumption, increased system capacity, and enhanced immunity to eavesdropping. In this paper, we introduce three new TR-based techniques for UWB wireless: (i) equalized TR beamforming, (ii) TR beamforming with multiple-signal-classification (MUSIC), and (iii) differential TR. We apply them to multiple-input single-output (MISO) UWB wireless communication systems operating in indoor scenarios. We compare the bit error rate (BER) performances of the proposed techniques with those of conventional beamforming.

Experimental Demonstration of Statistical Stability in Ultrawideband Time-Reversal Imaging

We experimentally verify the statistical stability of ultrawideband time-reversal-based imaging of targets in discrete random media using electromagnetic waves. The measurements are carried out using a time-domain radar system that provides useful bandwidth of up to 40 GHz. We study the effect of the excitation bandwidth, discrete scatterers permittivity and fractional volume, and radar aperture size on the image stability. We also address the problem of imaging nonstationary targets in random media, where we verify the statistical stability of a differential time-reversal technique suited for tracking.

Numerical and experimental study on statistical stability of ultrawideband time-reversal imaging

We present a numerical study on the statistical stability of ultrawideband (UWB) time-reversal (TR) imaging in random media. We show that stable images can be obtained by using sufficiently wide bandwidth even in the absence of deterministic knowledge of the actual forward propagation media. We investigate the effect of array aperture size and spatial deployment (limited aspect versus full aspect) on the image stability. We verify our results by carrying out UWB measurements in random media composed of discrete dielectric scatterers.

Performance of differential time-reversal for imaging and tracking of targets in clutter

We evaluate the performance of two differential time-reversal techniques for detecting, tracking and imaging moving targets in clutter. The proposed techniques provide real-time tracking capabilities with outstanding clutter rejection performance. They also exploit the distinctive features of time-reversal such as superresolution and statistical stability.