Dehong Liu

Electromagnetic time-reversal imaging of a target in a cluttered environment

Electromagnetic time-reversal imaging is addressed for a target situated in a cluttered background. We first investigate the theory of electromagnetic time-reversal imaging, followed by an experimental demonstration. A transmitter-receiver antenna array is connected to a network analyzer and applied to transmit wideband waveforms for detecting a target within a cluttered environment. We assume the cluttered background is fixed, thus the target signature is extracted by observing changes manifested by the introduction of a target. A numerical algorithm is required for computation of the Greens function employed within the time-reversal imager, with this implemented here via ray tracing. Example time-reversal images of different cluttered backgrounds and different targets are presented using measured data, with comparisons to a traditional radar imaging technique. Results show that the time-reversal imagery yields good focusing at the target, significantly better than when the background is not accounted for.

Electromagnetic Time-Reversal Source Localization in Changing Media: Experiment and Analysis

An experimental study is performed on electromagnetic time reversal in highly scattering environments, with a particular focus on performance when environmental conditions change. In particular, we consider the case for which there is a mismatch between the Greens function used on the forward measurement and that used for time-reversal inversion. We examine the degradation in the time-reversal image with increasing media mismatch, and consider techniques that mitigate such degradation. The experimental results are also compared with theoretical predictions for time reversal in changing media, with good agreement observed

Electromagnetic Target Detection in Uncertain Media: Time-Reversal and Minimum-Variance Algorithms

An experimental study is performed on imaging targets that are situated in a highly scattering environment, employing electromagnetic time-reversal methods. A particular focus is placed on performance when the electrical properties of the background environment (medium) are uncertain. It is assumed that the (unknown) medium characteristic of the scattered fields represents one sample from an underlying random process, with this random process representing our uncertainty in the media properties associated with the scattering measurement. While the specific Greens function associated with the scattered fields is unknown, we assume access to an ensemble of Greens functions sampled from the aforementioned distribution. This ensemble of Greens functions may be used in several ways to mitigate uncertainty in the true Greens function. Specifically, when performing time-reversal imaging, we consider a Greens function as a representative of the average of the ensemble, as well as Greens functions based on a principal components analysis of the ensemble. We also develop a wideband minimum-variance beamformer with environment perturbation constraints, in which the unknown Greens function is constrained to reside in a subspace spanned by the Greens function ensemble. These algorithms are examined using electromagnetic scattering data measured in a canonical set of laboratory experiments. The qualitative performance of the different techniques is presented in the form of images, with quantitative results presented in the form of receiver operating characteristic performance

Coherence, Compressive Sensing, and Random Sensor Arrays

Random sensor arrays are examined from a compressive-sensing (CS) perspective, particularly in terms of the coherence of compressive-sensing matrices. It is demonstrated that the maximum sidelobe level of an array corresponds to the coherence of interest for compressive sensing. This understanding is employed to explicitly quantify the accuracy of array source localization as a function of the number of sources and the noise level. The analysis demonstrates that the compressive-sensing theory is applicable to arrays in vacuum, as well as in the presence of a surrounding linear medium. Furthermore, the presence of a surrounding media with known properties may be used to improve array performance, with this related to phase conjugation and time reversal. Several numerical results are presented to demonstrate the theory.

Airport detection in large aerial optical imagery

A method to detect airports in large aerial optical imagery is considered. Combining texture segmentation and shape detection, this method shows advantages in analyzing large aerial imagery. First, large aerial images are segmented and interpreted according to textural features using a fast kernel matching pursuits (KMP) algorithm. As a result, attention is then paid to small regions of interest, extracted from the large images. Second, for each region of interest, a corresponding binary image is generated via the Canny edge operator, yielding a modified Hough transform image with which we search for elongated rectangles with desired dimensions (characteristic of runways). Those detected rectangles are declared as runways and the corresponding region of interest as an airport. Application on a dozen aerial images from southern California, demonstrates the effectiveness of the algorithm.

In situ compressive sensing

Compressive sensing (CS) is a framework that exploits the compressible character of most natural signals, allowing the accurate measurement of an m-dimensional signal u in terms of n m measurements v. The CS measurements may be represented in terms of an n × m matrix that defines the linear relationship between v and u. In this paper, we demonstrate that similar linear mappings of the form u → v are manifested naturally by wave propagation in general media, and therefore in situ CS measurements may be performed simply by exploiting the propagation and scattering properties of natural environments. The connection between the propagation medium and the basis in which u is sparsely rendered is quantified in terms of a mutual-coherence factor, which plays an important role in defining the number of required in situ CS measurements. In addition to presenting the basic in situ CS framework, a simple but practical example problem is considered in detail from multiple perspectives.

Compressive sensing for multi-static scattering analysis

Compressive sensing (CS) is a framework in which one attempts to measure a signal in a compressive mode, implying that fewer total measurements are required vis a vis direct sampling methods. Compressive sensing exploits the fact that the signal of interest is compressible in some basis, and the CS measurements correspond to projections (typically random projections) performed on the basis function coefficients. In this paper, we demonstrate that ideas from compressive sensing may be exploited in the context of electromagnetic modeling, here multi-static scattering from an arbitrary target. In this context, the computational analysis may be viewed as a numerical experiment, and ideas from compressive sensing may be used to reduce the number of computations required for target characterization. It is demonstrated that the compressive sensing framework may be applied with relatively minor modifications to many existing numerical models, with examples presented here for a fast-multipole computational engine.

Experimental validation of a transport-based imaging method in highly scattering environments

We demonstrate the effectiveness of a transport-based reconstruction method for imaging in highly scattering environments. Experimentally measured wave energy data in the micro-wave regime are used to reconstruct extended inclusions buried in scattering media or hidden behind non-penetrable obstacles. The performance of the imaging method is illustrated under various circumstances, via a set of electromagnetic experiments.

On enhancing classification performance by exploiting multiple scattering

Using concepts developed in the fields of compressive sensing and random-projection-based embeddings, we consider classification of an object situated within a complex propagation environment. We demonstrate that propagation through such an environment may be exploited to enhance classification performance, analogous to the enhanced resolution in time-reversal techniques. The theory is demonstrated using rf measurements.

Wideband Array Imaging of a Target Situated in an Unknown Random Media

We propose two new methods for wideband array signal imaging for targets situated in unknown random media. First, a normalized coherent interferometric (N-CINT) imaging algorithm is developed based on coherent interferometric (CINT) imaging theory, yielding improved imaging performance with experimental data. Second, a phase-difference analysis (PDA) method is proposed to significantly reduce computation time and to improve imaging quality. The parameters in the two methods are determined adaptively by optimizing an objective function. Experiments are carried out for electromagnetic scattering using a linear antenna array, providing a demonstration of these methods.