Benjamin Barrowes

Through-Wall Bio-Radiolocation With UWB Impulse Radar: Observation, Simulation and Signal Extraction

In this paper the cardio-respiratory signatures of human beings were studied using both an ultra-wide band (UWB) impulse radar system in a laboratory through-wall experiment and a numerical simulation using the finite difference time domain (FDTD) method. Signals from both the physical experiment and numerical simulation are processed with the Hilbert-Huang Transform (HHT), a novel signal processing approach for nonlinear and non-stationary data analysis. The results show that by using the HHT, human respiration characteristics can be successfully identified and differentiated for different subjects and a variety of respiratory statuses. However, reliable detection of cardiologic signatures requires a radar system with higher central frequency. Our results demonstrate that this combination of UWB impulse radar and HHT data processing has potential for through-wall life detection and possibly other applications.

Numerical simulation of UWB impulse radar vital sign detection at an earthquake disaster site

Using the finite difference time domain (FDTD) numerical simulation approach and synthetic computational experiments we investigated the use of the ultra-wide band (UWB) radar technique for human vital sign detection under collapsed building debris caused by catastrophic earthquakes. The model of the collapsed building was developed based on a real situation from an earthquake disaster site. The model consists of two human beings with different characteristics of vital signs, i.e., with different cardio-respiration features, posed in different positions, and buried at different depths in the debris. Analysis of the synthetic data indicates that the UWB impulse radar can identify and separate the human subjects vital sign for a radar record as short as 20s. The simulation approach was verified with a physical experiment using impulse UWB radar with two human subjects positioned behind a concrete wall. Advanced signal processing of source separation and signal processing using empirical mode decomposition were conducted to identify and locate the human subjects. The results show that this approach is a promising technique for search and rescue of living victims at disaster sites.

Realistic subsurface anomaly discrimination using electromagnetic induction and an SVM classifier

The environmental research program of the United States military has set up blind tests for detection and discrimination of unexploded ordnance. One such test consists of measurements taken with the EM-63 sensor at Camp Sibert, AL. We review the performance on the test of a procedure that combines a field-potential (HAP) method to locate targets, the normalized surface magnetic source (NSMS) model to characterize them, and a support vector machine (SVM) to classify them. The HAP method infers location from the scattered magnetic field and its associated scalar potential, the latter reconstructed using equivalent sources. NSMS replaces the target with an enclosing spheroid of equivalent radial magnetization whose integral it uses as a discriminator. SVM generalizes from empirical evidence and can be adapted for multiclass discrimination using a voting system. Our method identifies all potentially dangerous targets correctly and has a false-alarm rate of about 5%.

The Application of the Hilbert-Huang Transform in Through-wall Life Detection with UWB Impulse Radar

Hilbert-Huang Transformation (HHT) is a powerful tool for nonlinear and non- stationary data analysis. In this paper, a dataset using an ultra-wide (UWB) impulse radar system with central frequency of 1 GHz was collected for life motion detection behind a cinder block wall. To extract the information of life motions such as breathing and heartbeats from the raw data, we flrst applied the empirical mode decomposition (EMD), the flrst step of HHT to decompose the signal (background signal included) into a family of the intrinsic mode func- tions (IMFs). We then apply Hilbert spectral analysis (HSA) to get the frequency spectra of difierent IMFs. After dividing by the spectrum of the background radar record (equivalent to de-convolving the background record in the time domain), we found that breathing appear as a spectral peak at 0.2{0.4Hz and heart beating appears as 1.0{1.2Hz. This is coinciding with real condition. Our preliminary results show that the HHT technique provides signiflcant assistance in signal processing for the detection of human targets behind opaque obstacles.

High frequency electromagnetic induction sensing for non-metallic ordnances detection

High frequency (>100 kHz) electromagnetic induction (HFEMI) sensing phenomena are investigated for nonmetallic ordnances detection and discrimination. HFEMI responses are studied using numerical and experimental data. The numerical modeling is done via the method of auxiliary sources, and data are collected using a new HEMI system, that has been developed at our lab. The comparisons between modeled and actual data are illustrated for a non-metallic 105 mm projectile.

SLO blind data set inversion and classification using physically complete models

Discrimination studies carried out on TEMTADS and Metal Mapper blind data sets collected at the San Luis Obispo UXO site are presented. The data sets included four types of targets of interest: 2.36 rockets, 60-mm mortar shells, 81-mm projectiles, and 4.2 mortar items. The total parameterized normalized magnetic source (NSMS) amplitudes were used to discriminate TOI from metallic clutter and among the different hazardous UXO. First, in objects frame coordinate, the total NSMS were determined for each TOI along three orthogonal axes from the training data provided by the Strategic Environmental Research and Development Program (SERDP) along with the referred blind data sets. Then the inverted total NSMS were used to extract the time-decay classification features. Once our inversion and classification algorithms were tested on the calibration data sets then we applied the same procedure to all blind data sets. The combined NSMS and differential evolution algorithm is utilized for determine the NSMS strengths for each cell. The obtained total NSMS time-decay curves were used to extract the discrimination features and perform classification using the training data as reference. In addition, for cross validation, the inverted locations and orientations from NSMS-DE algorithm were compared against the inverted data that obtained via the magnetic field, vector and scalar potentials (HAP) method and the combined dipole and Gauss-Newton approach technique. We examined the entire time decay history of the total NSMS case-by-case for classification purposes. Also, we use different multi-class statistical classification algorithms for separating the dangerous objects from non hazardous items. The inverted targets were ranked by target ID and submitted to SERDP for independent scoring. The independent scoring results are presented.

NSMC for UXO discrimination in cases with overlapping signatures

In this paper the normalized surface magnetic charge model (NSMC) is employed for discriminating objects of interest, such as unexploded ordnances (UXO), from innocuous items, in cases when UXO electromagnetic induction (EMI) responses are contaminated by signals from other objects or magnetically susceptible ground. The model is designed for genuine discrimination and it is a physically complete, fast, and accurate forward model for analyzing EMI scattering. In the NSMC the overall EMI inverse problem can be summarized as follows: first, for any primary magnetic field the scattered magnetic field at selected points outside the object is recorded; and second, using the scattered field information an object buried object location, orientation and the amplitude of the NSMC are estimated. Finally, the total NSMC is used as a discriminant for distinguishing between UXO and non-UXO items. To illustrate the applicability of the NSMC algorithm, blind test data, which are collected at Cold Regions Research and Engineering Laboratory facility for actually buried objects under different type soil, are processed and analyzed.

Comparison of the physically complete model with a simple dipole model for UXO detection and discrimination

The physically complete Normalized Surface Magnetic Source (NSMS) model and a variant of the simple dipole model are applied to new-generation electromagnetic induction (EMI) data. The main objective is to assess the NSMS and dipole models capabilities to discriminate between UXO and clutter starting from scattered EMI signals. The discrimination contains two sets of parameters: (1) intrinsic parameters associated with the size, shape, and material composition of the target; and (2) extrinsic parameters related to the orientation and location of the anomaly. To discriminate UXO from clutter a mathematical model is fit to the geophysical data, after which both intrinsic and extrinsic parameters are extracted using an optimization technique. The inverted intrinsic parameters thus found are used to isolate objects of interest from non-hazardous items. The discrimination performance depends significantly on the mathematical model. In this work we present results of applying the single dipole, multi-dipole, and NSMS models to single- and multi-axis sensor data produced by new-generation EMI instruments such as MPV, TEMTADS, and MetalMapper, all of which are are time-domain systems. The MPV has a single transmitter and five tri-axial receivers, the TEMTADS array is a towed system featuring 25 transmitter/receiver pairs, and MetalMapper contains three rectangular transmitters and five tri-axial receivers distributed on a plane. The inversion and discrimination performance of the NSMS and single-dipole models are illustrated for the high-quality, well-located EMI data produced by these instruments. Specifically, we present comparisons between inverted intrinsic and extrinsic parameters, as determined from each model and compared with the ground truth.

Radar wave simulation using three-dimensional multi-region pseudospectral time domain algorithm with directional Kirchhoff integration surface

We present a three-dimensional (3D), multi-region (MR) pseudospectral time domain (PSTD) method with directional Kirchhoff integration (DKI) surface for simulating electromagnetic (EM) wave propagation in large domain heterogeneous media. The basic idea is to divide the whole computational domain into a number of sub-regions containing strong scatters embedded into a uniform background. The PSTD method is used to compute the EM field inside all the sub-regions, while Kirchhoff integration techniques are used to compute the EM field in the uniform background. As the result a 3D multi-region PSTD method with directional Kirchhoff integration surface concept (DKI/MR/PSTD) is presented. An application in through-wall detection is introduced in the source sub-region with heterogeneous scatters. Comparison of the results obtained in this approach with single-region PSTD demonstrates the validity of DKI/MR/PSTD in simulation of through-wall detection cases. Later time multiple refraction and reflection of EM wave trains can be clearly simulated besides the first wave phase in the heterogeneous source sub-region. The EM waveforms simulated from DKI/MR/PSTD coincide well with those corresponding phases from the classical single-region PSTD. Moreover, the signal to noise ratio in EM waveforms simulated by DKI/MR/PSTD is higher than those obtained by single-region PSTD. For the given test case the computation time of DKI/MR/PSTD is less than 1/3 of that used by the classical single-region PSTD. The memory requirement by DKI/MR/PSTD is about one half of the classical PSTD. The results show that the DKI/MR/PSTD method is valid for EM wave propagation simulation.

An Analytical Expression for Estimating a Buried Object's Location, Orientation and Magnetic Polarization to Support UXO Discrimination

A new physics based expression is presented for determining a buried objects location, orientation and magnetic polarizability. The approach assumes the target exhibits a dipolar response and uses only three global values: (1) a H magnetic field vector, (2) a vector potential A and (3) a scalar magnetic potential psi all at a single location in space. To illustrate the accuracy of the proposed algorithm, several numerical results are demonstrated using synthetic data for one and three dipoles.