Jon E. Mooney

Identification of metallic mine-like objects using low frequency magnetic fields

This paper addresses the issue of identifying conducting objects based on their response to low frequency magnetic fields; an area of research referred to as magnetic singularity identification (MSI). Real-time identification was carried out on several simple geometries. The low frequency transfer function of these objects was measured for both cardinal and arbitrary orientations of the magnetic field with respect to the planes of symmetry of the objects (i.e., different polarizations). Distinct negative real axis poles (singularities) associated with each object form the basis for their real-time identification algorithm. Recognizing this identification problem as one of inference from incomplete information, a generalized likelihood ratio test (GLRT) is presented as a solution to the M-ary hypothesis testing problem of interest. Best performance of their GLRT classification scheme, measured through Monte Carlo simulation and presented in terms of percent correct identification versus SNR, was obtained with a single pole per object orientation.

Robust target identification in white Gaussian noise for ultra wide-band radar systems

Radar target identification, as witnessed by the plethora of the literature on the topic, is an important problem of considerable interest to many civilian and military agencies. The number of signatures even for a small target library can become quite large since, in general, a unique return is produced for each new target aspect. Any robust target identification algorithm must adequately address this issue. The extinction pulse (E-pulse) and other related techniques, which are based on a singularity expansion method description of the radar return, indeed boast an aspect independent identification algorithm. However, as demonstrated in this paper, the performance of these techniques in white Gaussian noise is inferior to the method described here. In this paper, we develop a new method based on a generalized likelihood ratio test (GLRT) to perform target identification in the presence of white Gaussian noise. As with the E-pulse technique, our method takes advantage of the parsimonious singularity expansion representation of the radar return. In addition, sufficient statistics and simple practical implementations of a GLRT are presented. Simulation results using various thin wire targets are presented contrasting the performance of the GLRT to the E-pulse technique as a function of signal-to-noise (SNR) ratio.

Performance analysis of a GLRT automated target discrimination scheme

The use of a generalized likelihood ratio test (GLRT) based on the late-time scattered return for target discrimination was recently presented by J.E. Mooney et al. (see ibid., vol.46, p.1817-23, Dec. 1998). The performance of the GLRT was demonstrated by direct simulation with scattering data from a target library consisting of several thin-wire targets. In this paper, a numerical procedure for analytically evaluating the performance of the GLRT is presented. At the heart of this procedure is the computation of the probability density of the GLRT decision statistic. Unlike previous works that rely solely on some simulation examples to demonstrate performance, our accurate analytical results provide strong evidence of the effectiveness of the GLRT method. The resulting analysis yields a measure of the discrimination capability of the GLRT. This measure, which is referred to as the probability of correct identification, is computed as a function of signal-to-noise ratio (SNR) using the theoretical scattering data from several thin-wire targets. These results are compared to the direct simulation results presented by Mooney et al. to demonstrate the accuracy of the analysis.

Performance analysis of an automated E-pulse target discrimination scheme

An automated E-pulse scheme for target discrimination was initially presented by Ilavarasan et al. (1993) without an analytic performance evaluation. Assuming that target responses are contaminated with white Gaussian noise, an automated E-pulse scheme is rigorously analyzed to yield a reliable measure of performance. The discrimination performance of this automated E-pulse scheme is determined quantitatively through the use of energy discrimination numbers (EDNs). Statistics of the EDNs are evaluated analytically to derive the probability of correct identification. The probability of identification as a function of signal-to-noise ratio (SNR) is evaluated using the theoretical scattering data for all potential targets to predict the performance of the automated E-pulse scheme. These theoretical results are corroborated by direct simulation of the discrimination scheme. In addition, the probability density functions of the EDNs are presented providing new physical insights into E-pulse performance as a function of target geometries and SNR.

Simulants (decoys) for low-metallic-content mines: theory and experimental results

Two sets of metallic objects are created to provide a standard set of metallic test targets to facilitate an objective comparison and evaluation of metal detectors. The first set of metallic objects is chosen form combinations of small metal parts common to many low-metallic content landmines. The collections of small metal parts are chosen based on an average detection distance measured with five sensitive metal detectors. A second set of metal objects is created using short-circuited coils of wire, INSCOILS. A development of the theory describing the interactions of INSCOILS with a metal detectors transmit and receive coil shows that the coupling and response function of an INSCOIL can be independently controlled. By varying the wire gauge, wire material, and loop size, an INSCOIL can be made to approximate the response of an arbitrary metallic object. A pulse-induction measurement system is used to measure the response of different metallic objects. The pulse-induction measurement system is used to match the response of an INSCOIL to that of the collection of small metal parts. Surrogate landmines are also constructed by matching the response of a coil of wire to that of a specific landmine.

Robust Target Identification Using a Generalized Likelihood Ratio Test

The concept of deciding among a set of alternatives (or hypotheses) based upon the observation of a set of random variables has been a topic studied by statisticians for many years. This concept, known as hypothesis testing, provides a mathematically solid foundation to perform target identification. Target identification with known signatures can be easily formulated using Bayes hypothesis testing. However, a significant challenge lies in the need to accurately discriminate among known targets with only partial knowledge of target signatures. The lack of complete target signature knowledge results from the unknown orientation of the target and the dependency of the target signature on the target’s orientation. For practical purposes, it is important to derive efficient and reliable schemes to accurately identify the target without a priori knowledge concerning the target’s orientation.