Imaging of targets embedded in multilayered dielectric media has attracted growing interest in microwave remote sensing, nondestructive testing, ground penetrating radar, and urban sensing. Compressive sensing has been successfully applied in the aforementioned applications for efficient target imaging, leading to prompt actionable intelligence. Recently, a total variation minimization (TVM) based approach was proposed, which offers superior performance over standard L1- minimization based sparse reconstruction in terms of target shape reconstruction and distinguishing closely-spaced point targets from an extended target. Alternatively, group sparse reconstruction (GSR) schemes can also be employed to account for target extent. In this paper, we provide a performance comparison between TVM and GSR schemes for extended target imaging in multi-layered media using numerical electromagnetic data.
In this paper, we present a sparse image reconstruction approach for radar imaging through multilayered media with total variation minimization (TVM). The approach is well suited for high-resolution imaging for both ground penetrating radar (GPR) and through-the-wall radar imaging (TWRI) applications. The multilayered media Green’s function is incorporated in the imaging algorithm to efficiently model the wave propagation in the multilayered environment. For GPR imaging, the multilayered subsurface Green’s function is derived in closed form with saddle point method, which is significantly less time consuming than numerical methods. For through-the-wall radar imaging, where the first and last layers are freespace, a far field approximation of the Green’s function in analytical form is used to model the wave propagation through single or multilayered building walls. The TVM minimizes the gradient of the image resulting in excellent edge preservation and shape reconstruction of the image. Representative examples are presented to show high quality imaging results with limited data under various subsurface and through-the-wall imaging scenarios.
For through-the-wall radar imaging (TWRI), an accurate characterization of the wall is important for the enhancement of
imaging of the target behind the wall. In this paper we cast the two-dimensional (2D) wall interior structure imaging as a
subsurface imaging problem. The region between the front and back walls is imaged using a novel linear inverse
scattering algorithm for 2D subsurface imaging. The imaging algorithm is based on first order Born approximation and
exploiting halfspace Green's function. The exploding reflection model is employed and then the Green's function is
expanded in the spectral domain to formulate a novel real time intra-wall imaging algorithm. The linearization of the
inversion scheme and the employment of FFT/IFFT in the imaging formula make the imaging algorithm suitable in
several applications concerning the diagnostics of large probed domain and allow real time processing. A numerical
result is presented to show the effectiveness and efficiency of the proposed algorithm for real time intra-wall
characterization.
With recent advances in both algorithm and component technologies, through-the-wall sensing and imaging is emerging
as an affordable sensor technology in civilian and military settings. One of the primary objectives of through-the-wall
sensing systems is to detect and identify targets of interest, such as humans and cache of weapons, enclosed in building
structures. Effective approaches that achieve proper target radar cross section (RCS) registration behind walls must, in
general, exploit a detailed understanding of the radar phenomenology and more specifically, knowledge of the expected
strength of the radar return from targets of interest. In this paper, we investigate the effects of various wall types on the
received power of the target return through the use of a combined measurement and electromagnetic modeling approach.
The RCS of material-exact rifle and human models are investigated in free-space using numerical electromagnetic
modeling tools. A modified radar range equation, which analytically accounts for the wall effects, including multiple
reflections within a given homogeneous or layered wall, is then employed in conjunction with wideband measured
parameters of various common wall types, to estimate the received power versus frequency from the aforementioned
targets. The proposed technique is, in principle, applicable to both bistatic and mono-static operations.
Target detection and classification are considered the primary tasks in through-the-wall radar imaging. Indoor targets can
be stationary or in motion. In this paper, we apply the matched illumination concept to the scattered electromagnetic
field of two stationary targets that are commonly found in an indoor environment, namely, a wooden chair and a wooden
table. The optimal waveform was obtained by choosing the eigenvector corresponding to the largest eigenvalue of the
target's autocorrelation matrix. The scattered field over the frequency band of 1-3 GHz was obtained by full wave
numerical simulations using a commercially available
Finite-Difference Time Domain solver (XFDTD from
REMCOM). The detection performance of the optimum waveform against the commonly used linear frequency
modulated (LFM) signal of the same bandwidth was compared.
Recently, there has been considerable interest in the area of Radio Frequency Identification (RFID) and Radio
Frequency Tagging (RFTAG). This emerging area of interest can be applied for inventory control (commercial) as well
as friend/foe identification (military) to name but a few. The current technology can be broken down into two main
groups, namely passive and active RFID tags. Utilization of Space-Filling Curve (SFC) geometries, such as the Peano
and Hilbert curves, has been recently investigated for use in completely passive RFID applications [1, 2]. In this work,
we give an overview of our work on the space-filling curves and the potential for utilizing the electrically small,
resonant characteristics of these curves for use in RFID technologies with an emphasis on the challenging issues
involved when attempting to tag conductive objects. In particular, we investigate the possible use of these tags in
conjunction with high impedance ground-planes made of Hilbert or Peano curve inclusions [3, 4] to develop electrically
small RFID tags that may also radiate efficiently, within close proximity of large conductive objects [5].
Future sensing technologies are needed to provide higher accuracy, lower power consumption and occupy small real estate within munitions. The novel ideas being supported at the Army Research Development Engineering Center (ARDEC) at Dover, New Jersey, uses principles of electromagnetic propagation and the properties of waveguide cavities with various geometries to develop a new class of sensors for onboard direct measurement of the angular orientation and position of objects in flight and applications such as mobile robotic platforms. Currently available sensors for munitions are based on inertia, optics or heat. Inertia based sensing generally suffers from drift, noise and the currently available sensors cannot survive high firing accelerations while maintaining the required measurement sensitivity. Optical technologies generally have short range and require line-of-site. The sensing technologies presented in this paper employ radio frequency, make direct measurement of position and orientation, and do not require added information for their operation. The presented sensors employ waveguide cavities that are embedded into the structure of munitions. It is shown that the geometry of the waveguide cavity can be designed to achieve high angular orientation sensitivity with respect to a reference, polarized electromagnetic field. In this paper, the theoretical fundamentals describing the operation of the developed sensors are described. Studies of the interaction of the polarized signals with various waveguides and cavity geometries are presented. Simulations results as well as experimental results validating the theoretical and the simulation results are provided. The simulation and experimental results clearly demonstrate the potentials of the developed position and angular orientation sensors in general, and to munitions in particular.
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