KEYWORDS: Sensors, Target detection, Land mines, Ground penetrating radar, Radar, General packet radio service, Signal detection, Detection and tracking algorithms, Target recognition, Data modeling
In this work we present an application of matched subspace detectors to the problem of target detection and identification using ground penetrating radar data. In particular we apply sets of matched subspace detector filter banks to data containing both anti-personnel and anti-tank targets as well as metallic and non-metallic clutter objects. Current results indicate the potential for robust target detection and identification but further improvements via subspace modeling and signal extraction/enhancement may also improve performance.
In this paper we present a multi-stage algorithm for target/clutter discrimination and target identification using the Niitek/Wichmann ground penetrating radar (GPR). To identify small subsets of GPR data for feature-processing, a pre-screening algorithm based on the 2-D lattice least mean squares (LMS) algorithm is used to flag locations of interest. Features of the measured GPR data at these flagged locations are then generated and pattern recognition techniques are used to identify targets using these feature sets. It has been observed that trained human subjects are often quite successful at discriminating targets from clutter. Some features are designed to take advantage of the visual aberrations that a human observer might use. Other features based on a variety of image and signal processing techniques are also considered. Results presented indicate improvements for feature-based processors over pre-screener algorithms.
Recently, blind tests of several automated detection algorithms operating on the NIITEK ground penetrating radar data (GPR) have resulted in quite promising performance results. Anecdotally, human observers have also shown notable skill in detecting landmines and rejecting false alarms in this same data; however, the basis of human performance has not been studied in depth. In this study, human observers are recruited from the undergraduate and graduate student population at Duke University and are trained to visually detect landmines in the NIITEK GPR data. Subjects are then presented with GPR responses associated with blanks, clutter items (including emplaced clutter), and landmines in a blind test scenario. Subjects are asked to make the decision as to whether they are viewing a landmine response or a false alarm, and their performance is scored. A variety of landmines, measured at several test sites, are presented to determine the relative difficulty in detecting each mine type. Subject performance is compared to the performance of two automated algorithms already under development for the NIITEK radar system: LMS and FROSAW. In addition, subjects are given a subset of features for each alarm from which they may indicate the reason behind their decision. These last data may provide a basis for the design of an automated algorithm that takes advantage of the most useful of the observed features.
Ground penetrating radar has been proposed as an alternative sensor to classical electromagnetic induction techniques for the landmine detection problem. The NIITEK-Wichmann antenna provides a high frequency radar signal with very low noise levels following the ground reflection. As a result, the signal from a buried object is not masked by the inherent noise in the system. It has been demonstrated that an operator can learn to interpret the NIITEK-Wichmann radar signal to detect and identify buried targets. The goal of this work is to develop signal processing algorithms to automatically process the radar signals and differentiate between targets and clutter. The algorithms that we are investigating have been tested on data collected at the JUXOCO test grid as well as on data collected in calibration lanes that are used for evaluating the performance of handheld and vehicular landmine detection systems. We have developed algorithms based on principle component analysis, independent component analysis, matched filters, and Bayesian processing of wavelet features. We have also considered several approaches to ground-bounce removal prior to processing. In this paper we discuss the relative performance of each of the techniques as well as the impact of ground bounce removal on processing of the data.
KEYWORDS: Sensors, Signal processing, Signal detection, Land mines, Electromagnetic coupling, Detection theory, Data modeling, Detection and tracking algorithms, Interference (communication), Mining
Although the ability of EMI sensor to detect landmines has improved significantly, false alarm rate reduction remains a challenging problem. Improvements have been achieved through development of optimal algorithms that exploit models of the underlying physics along with knowledge of clutter statistics. Moreover, experienced operators can often discriminate mines from clutter with the aid of an audio transducer, the method most commonly used to alert the sensor operator that a target is presented. Assuming the basic information needed for discriminating landmines from clutter is largely available from existing sensors, the goal of this work is to optimize the presentation of information to the operator. Traditionally, an energy calculation is provided to the sensor operator via a signal whose loudness or frequency is proportional to the energy of the calculation is provided to the sensor operator via a signal whose loudness or frequency is proportional to the energy of the received signal. Our preliminary work has shown that when the statistic used to make a decision is not simply the signal energy the performance of mine detection systems can be improved dramatically. This finding suggests that the operator could make better use of a signal that is a function of this more accurate test statistic, and that there may be information in the unprocessed sensor signal that the operator could use to effect discrimination. In this paper, we investigate and quantify, through listening experiments, the perceptual dimensions that most effectively convey the information in a sensor response more appropriately to the listener, discrimination, as opposed to simple detection, can be achieved.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.