Leslie M. Collins

Sensing of unexploded ordnance with magnetometer and induction data: theory and signal processing

We consider the detection of subsurface unexploded ordnance via magnetometer and electromagnetic-induction (EMI) sensors. Detection performance is presented, using model-based signal processing algorithms. We first develop and validate the parametric models, using both numerical and measured data. These models are then applied in the context of feature extraction, and the features are processed via two signal-processing algorithms. The detection algorithms are discussed in detail, with comparisons made based on performance with measured magnetometer and EMI data.

Classification of landmine-like metal targets using wideband electromagnetic induction

In their previous work, the authors have shown that the detectability of landmines can be improved dramatically by the careful application of signal detection theory to time-domain electromagnetic induction (EMI) data using a purely statistical approach. In this paper, classification of various metallic land-mine-like targets via signal detection theory is investigated using a prototype wideband frequency-domain EMI sensor. An algorithm that incorporates both a theoretical model of the response of such a sensor and the uncertainties regarding the target/sensor orientation is developed. This allows the algorithms to be trained without an extensive data collection. The performance of this approach is evaluated using both simulated and experimental data. The results show that this approach affords substantial classification performance gains over a standard approach, which utilizes the signature obtained when the sensor is centered over the target and located at the mean expected target/sensor distance, and thus ignores the uncertainties inherent in the problem. On the average, a 60% improvement is obtained.

Performance of an adaptive feature-based processor for a wideband ground penetrating radar system

A two-stage algorithm for landmine detection with a ground penetrating radar (GPR) system is described. First, 3-D data sets are processed using a computationally inexpensive pre-screening algorithm which flags potential locations of interest. These flagged locations are then passed to a feature-based processor which further discriminates target-like anomalies from naturally occurring clutter. Current field trial (over 6500 square meters) and blind test results (over 39000 square meters) are presented and these show at least an order of magnitude improvement over other radar system-based detection algorithms on the same test lanes.

Comparison of electrode discrimination, pitch ranking, and pitch scaling data in postlingually deafened adult cochlear implant subjects

The goal of this study was to investigate the relationship between variation in electrode site of stimulation and the perceptual dimensions along which such stimuli vary. This information may allow more effective use of electrode place when encoding speech information. To achieve this goal, two procedures which measure pitch in subjects implanted with the Nucleus/Cochlear Corporation multichannel device were performed. Estimates of electrode discriminability that can be obtained from these procedures were compared to a more direct measure of electrode discriminability that was obtained in a previous study [Collins et al., Assoc. Res. Otolaryng. Abstracts, No. 642 (1994)]. In the first task, subjects performed a pitch ranking procedure similar to that used in previous studies [Townshend et al., J. Acoust. Soc. Am. 82, 106-115 (1987); Nelson et al., J. Acoust. Soc. Am. 98, 1987-1999 (1995)]. Estimates of the pitch percept elicited by stimulation of each electrode as well as the discriminability of the electrodes were generated from the data using two different statistical analyses. In the second task, subjects performed a pitch scaling procedure similar to one used in a previous study [Busby et al., J. Acoust. Soc. Am. 95, 2658-2669 (1994)]. Again, two different statistical analyses were performed to generate estimates of the pitch percept corresponding to stimulation of each electrode and to generate estimates of electrode discriminability. In general, the estimates of the relationships between the pitch percepts obtained from the two procedures were not identical. In addition, the estimates of electrode discriminability were not equivalent to the electrode discrimination measures obtained from the same subjects during the previous study. Signal detection theory has been used to model the decision processes required by each of the procedures described above [e.g., Jesteadt and Bilger, J. Acoust. Soc. Am. 55, 1266-1276 (1974)]. However, these models do not predict the differences that were observed between the data sets obtained during this study. An alternate model is proposed which may explain the data obtained from these subjects. This model is based on the assumption that the percept that is elicited by electrical stimulation of an electrode is multidimensional, as opposed to unidimensional in nature. Therefore, the perceived signal is more appropriately modeled using a multidimensional random vector, where each element of the vector represents the perceived value of one of the dimensions of the signal.

Two-dimensional and three-dimensional NUFFT migration method for landmine detection using ground-penetrating Radar

Ground-penetrating radar (GPR) has been widely used for landmine detection due to its high signal-to-noise ratio (SNR) and superior ability to image nonmetallic landmines. Processing GPR data to obtain better target images and to assist further object detection has been an active research area. Phase-shift migration is a widely used method; however, its wavenumber space is nonuniformly sampled because of the nonlinear relationship between the uniform frequency samples and the wavenumbers. Conventional methods use linear interpolation to obtain uniform wavenumber samples and compute the fast Fourier transform (FFT). This paper develops two- and three-dimensional migration methods that process GPR data to obtain images close to the actual target geometries using a nonuniform fast Fourier transform (NUFFT) algorithm. The proposed method is first compared to the conventional migration approaches on simulated data and then applied to landmine field data sets. Results suggest that the NUFFT migration method is useful in focusing images, estimating landmine structure, and retaining relatively high signal-to-noise ratio in the migrated data. The processed data sets are then fed to the normalized energy and least-mean-square-based anomaly detectors. Receiver operating characteristic curves of data sets processed by different migration methods are compared. The NUFFT migration shows potential improvements on both classifiers with a reduced false alarm rate at most probabilities of detection.

An improved Bayesian decision theoretic approach for land mine detection

A rigorous signal detection theoretic analysis is used to improve detectability of land mines. The development is performed for sensors that integrate time-domain information to provide a single data point (standard metal detector), those that provide a sampled portion of the time-domain waveform, and those that operate at several discrete frequencies. This approach is compared to standard thresholding techniques, and it is shown to provide substantial improvements when evaluated on measured data.

Texture Features for Antitank Landmine Detection Using Ground Penetrating Radar

In this paper, we consider the application of texture features for antitank landmine detection in ground- penetrating-radar data in the difficult scenario of very high clutter environments. In particular, we develop a technique for 3-D texture feature extraction, and we compare the results for landmine/clutter discrimination using classifiers that are built on 3-D as well as on 2-D texture feature sets. Our results indicate performance improvements across several different challenging testing scenarios when using the relevance-vector-machine classifiers that are trained on our 3-D feature sets as compared to the performance using the 2-D texture feature sets.

Histograms of Oriented Gradients for Landmine Detection in Ground-Penetrating Radar Data

Ground-penetrating radar (GPR) is a powerful and rapidly maturing technology for subsurface threat identification. However, sophisticated processing of GPR data is necessary to reduce false alarms due to naturally occurring subsurface clutter and soil distortions. Most currently fielded GPR-based landmine detection algorithms utilize feature extraction and statistical learning to develop robust classifiers capable of discriminating buried threats from inert subsurface structures. Analysis of these techniques indicates strong underlying similarities between efficient landmine detection algorithms and modern techniques for feature extraction in the computer vision literature. This paper explores the relationship between and application of one modern computer vision feature extraction technique, namely histogram of oriented gradients (HOG), to landmine detection in GPR data. The results presented indicate that HOG features provide a robust tool for target identification for both classification and prescreening and suggest that other techniques from computer vision might also be successfully applied to target detection in GPR data.

Application of feature extraction methods for landmine detection using the Wichmann/Niitek ground-penetrating radar

Ground-penetrating radar (GPR) has been proposed as an alternative to classical electromagnetic induction techniques for the landmine detection problem. The Wichmann/Niitek system provides a good platform for novel GPR-based antitank mine detection and classification algorithm development due to its extremely high SNR. When the GPR sensor is mounted on a moving vehicle, the target signatures are hyperbolas in a time-domain data record. The goal of this work is to extract useful features that exploit this knowledge in order to improve target detection. The algorithms can be divided into two steps: feature extraction and classification. Preprocessing is also considered to remove both stationary effects and nonstationary drift of the data and to improve the contrast of the desired hyperbolas. The algorithm is evaluated using real data over primarily plastic antitank mines collected with a fielded GPR sensor at a government test site.

A comparison of the performance of statistical and fuzzy algorithms for unexploded ordnance detection

We focus on the development of signal processing algorithms that incorporate the underlying physics characteristic of the sensor and of the anticipated unexploded ordnance (UXO) target, in order to address the false alarm issue. In this paper, we describe several algorithms for discriminating targets from clutter that have been applied to data obtained with the multisensor towed array detection system (MTADS). This sensor suite includes both electromagnetic induction (EMI) and magnetometer sensors. We describe four signal processing techniques: a generalized likelihood ratio technique, a maximum likelihood estimation-based clustering algorithm, a probabilistic neural network, and a subtractive fuzzy clustering technique. These algorithms have been applied to the data measured by MTADS in a magnetically clean test pit and at a field demonstration. The results indicate that the application of advanced signal processing algorithms could provide up to a factor of two reduction in false alarm probability for the UXO detection problem.