Session D20: Invited Session: Advanced Electromagnetic Imaging and Remote Sensing: From DC to Daylight

Advanced EM Modeling in Support of Buried Target Detection and Identification

The detection and discrimination of buried unexploded ordnance is essential for restoration of millions of acres of munitions testing grounds to public use. Public safety is paramount so high detection probability is essential, and accurate discrimination is key for economically feasible remediation. Time domain electromagnetic (TDEM) induction methods in principle have the capability to address these criteria. The transmitter loop current pulse generates a magnetic field in the target region. This changing applied field, especially as the pulse terminates, induces currents in the target, generating a scattered magnetic field. The decaying scattered field, following pulse termination, induces the measured voltage in the receiver loop. There are three different regimes that one may identify in the voltage time traces: early, intermediate, and late time. At very early time, immediately following pulse termination, the currents are confined to the immediate surface of the target. The initial diffusion of these currents into the target interior leads to a 1/2 power law decay for nonferrous targets, (3/2 for ferrous targets). At intermediate time, as the currents penetrate the deeper target interior, the power law crosses over to a multi-exponential decay, representing the simultaneous presence of a finite set of exponentially decaying modes. Finally, at late time only the single, slowest decaying mode survives. At intermediate- to late-time our mean field algorithm models the dynamics by computing as large a number as possible of the modes, and determining the excitation level of each. At early time, the power law arises from a superposition of an essentially infinite number of exponentials, and a complementary theory, based on the detailed dynamics of the initial very thin surface current sheet, has been developed instead. We pre-compute intrinsic properties of modes containing all discrimination information of targets, and use this for nearly real-time inversion of TDEM measurement for target properties and subsequent discrimination.   

Through-wall microwave imaging: some applications of physics to urban reconnaissance

I will review some recent physics-based modeling approaches in support of microwave through-wall building tomography. Building layout estimation is a nonlinear inverse problem with a large number of degrees of freedom (geometry, location, and scattering properties of major building elements, such as walls, floors, and ceilings, plus many other smaller elements such as windows, doorways, and stairways). The physics of microwave propagation in such environments is very complex, involving multiple reflection, transmission, and diffraction events. Careful control of measurement protocol, using well-focused and directed transmitter and receiver arrays, can mitigate this to some degree. However, even under the most optimistic scenarios, the number of interactions increases exponentially as the signal penetrates more deeply into the building. Multiple overlapping returns from different building elements quickly overwhelm one's ability to disambiguate their sources. To explore the fundamental limitations on solutions to the inverse problem, efforts to create physics-based models that capture the signal complexity as accurately as possible will be described. These models remain an approximate description of reality, but nevertheless enable one to understand the effects of the explosion of multiple scattering events on the inversion, and quantify the limits of the inversion quality under even the most optimistic scenarios for data diversity and precision.   

Advanced methods in synthetic aperture radar imaging

For over 50 years our world has been mapped and measured with synthetic aperture radar (SAR). A SAR system operates by transmitting a series of wideband radio-frequency pulses towards the ground and recording the resulting backscattered electromagnetic waves as the system travels along some one-dimensional trajectory. By coherently processing the recorded backscatter over this extended aperture, one can form a high-resolution 2D intensity map of the ground reflectivity, which we call a SAR image. The trajectory, or synthetic aperture, is achieved by mounting the radar on an aircraft, spacecraft, or even on the roof of a car traveling down the road, and allows for a diverse set of applications and measurement techniques for remote sensing applications. It is quite remarkable that the sub-centimeter positioning precision and sub-nanosecond timing precision required to make this work properly can in fact be achieved under such real-world, often turbulent, vibrationally intensive conditions. Although the basic principles behind SAR imaging and interferometry have been known for decades, in recent years an explosion of data exploitation techniques enabled by ever-faster computational horsepower have enabled some remarkable advances. Although SAR images are often viewed as simple intensity maps of ground reflectivity, SAR is also an exquisitely sensitive coherent imaging modality with a wealth of information buried within the phase information in the image. Some of the examples featured in this presentation will include: (1) Interferometric SAR, where by comparing the difference in phase between two SAR images one can measure subtle changes in ground topography at the wavelength scale. (2) Change detection, in which carefully geolocated images formed from two different passes are compared. (3) Multi-pass 3D SAR tomography, where multiple trajectories can be used to form 3D images. (4) Moving Target Indication (MTI), in which Doppler effects allow one to detect and geolocate moving targets within SAR images. (5) Real time video SAR, where one forms a continuously updated SAR image by ``staring'' at an area of interest.   

Multiple-input Multiple-output Ground Moving Target Indicator Radar: Theory and Practice

Multiple-input multiple-output (MIMO) extensions to radar systems enable a number of advantages compared to traditional approaches. These advantages include improved angle estimation and target detection. In this paper, an overview of MIMO radar is provided, and the concept of coherent MIMO radar is defined. The principle focus of the paper is the discussion of MIMO ground moving target indication (GMTI). For GMTI radar modes, the advantages of a coherent MIMO architecture include improved angle estimation and enhanced slow speed target detection. To illustrate this, the concept of coherent MIMO radar is introduced and performance comparisons made between MIMO GMTI and traditional radar GMTI. These comparisons are supported by theoretical bounds, simulations, and experimental results for GMTI angle estimation accuracy and minimum detectable target velocity. For some applications, these results indicate significant potential improvements in clutter-mitigation, signal-to-noise ratio (SNR) loss, and reduction in angle-estimation error for slow-moving targets. The important effects of waveform characteristics is addressed.   

Image Formation in Bio-optical Sensing

Over the past two decades a number of optical sensing methods have emerged with potential to provide complementary information to traditional medical imaging modalities in application areas ranging from basic science to disease diagnosis and treatment monitoring. Though still largely in the research and development stage, modalities including diffuse optical tomography (DOT), fluorescence molecular tomography (FMT), photo-acoustic tomography (PAT), and bio-luminescence tomography (BLT) have excited much interest due to their natural functional imaging capability, their relatively low cost, and the fact that none required the use of ionizing radiation. These advantages however are tempered by a number of challenges associated with the processing of these data. Specifically, these data types all rely in one way or another on the interaction of light with tissue. The diffusive nature of this interaction inherently limits the spatial resolution of these modalities. As a result the process of forming an image is a far more delicate task than is the case with more standard imaging modalities such as X-ray computed tomography (CT). Two basic methods have been explored to address the ill-posedness of these problems in order to improve the information content in the resulting images. The optical data may be augmented either through the use of spectral diversity or by attempting to integrate optical data types with information from other modalities such as CT or MRI. Alternatively, a mathematical technique known as regularization can be used to impose physically-based constraints on the reconstruction. In this talk, I shall provide an overview of the work in my group in optical image formation within the contexts of DOT for breast cancer imaging and FMT for small animal imaging. The focus of the talk will be on methods that integrate data augmentation and mathematical regularization. In the case of FMT, we shall discuss our work in combining the optical data with information provided by CT concerning the structural distribution of tissue classes within the region of interest. Here, we have developed a number of spatially-varying regularization methods which use the CT data to help constrain the FMT reconstruction and obtain imaging results that are substantially improved over classical regularization techniques. For DOT, we have recently been considering the use of hyperspectral data sets in which information from over 100 near infrared wavelengths is made available to the processing. When combined with a regularization scheme based on parameterizing the images in a geometric manner, we believe that it will be possible to produce a standalone DOT system with spatial resolution that is today only achieved by combining DOT with e.g., CT.