The handheld F3 metal detector, developed by the MineLab Corporation, measures the responses of buried objects to electromagnetic pulses. These responses can be processed to determine whether a landmine is present. The simplest processor calculates the total energy in the response, thereby reducing the entire spatial and temporal response to a single value. This value, proportional to the amount of metal in the object, can then be compared to a predetermined threshold. The drawback of this common approach is that, although the threshold may be set so that few, if any, mines are missed, doing so may result in a high false alarm rate. Previous work has demonstrated that incorporating physics-based features into a Bayesian detection framework and performing simple, one-dimensional regional processing can significantly reduce the false alarm rate while maintaining the desired level of detection. Based on these promising results, this approach has been extended to incorporate two-dimensional regional processing. At the test site, data was collected both manually and robotically using nearly identical protocols. Thus, in theory, measured responses should be similar and algorithm performance equivalent whether the detector was operated by a robot or a human. The robustness of various algorithms was evaluated by comparing performance across manual and robotic data sets. Certain physics-based feature detectors were relatively unaffected by the response variability introduced unintentionally by the human operator. However, other algorithms that incorporate more sensitive, often regional, features were able to provide greater gains for the robotic data set than for the manual data set. These results imply that there may be a tradeoff between performance and practical issues that need to be addressed when selecting an algorithm for implementation in a field setting.
A hand-held mine detector has two modes of operation: search and localization. In search mode, the goal is to identify areas where a buried mine might be located. Since minimizing the number of misses is a top priority, many regions identified in this mode may contain clutter. To separate the clutter from the mines, the detector can be switched into the localization mode during which a more thorough interrogation of the region is performed. Because causality is not required in localization mode, the analyzed signal is not limited to a single data point, but instead can consist of the response across an entire spatial "region". Previous work has demonstrated that so called "region processing" can potentially improve the localization performance of the detector. We have used the Minelab F1A4 metal detector, an EMI-based system, to collect regional data for a variety of objects including buried mines, metallic and non-metallic clutter, and short-circuited copper loops in free space. Several physics-based processing algorithms were developed and used to predict discrimination performance. Analysis of the loops, whose physical properties were known, indicated that discrimination between objects might be possible using a feature extracted from the detector output. Subsequently, this feature was used as the basis of an algorithm which, when used to process the mine/clutter data, significantly decreased the false alarm rate. This algorithm and its performance were further enhanced by incorporating information about the entire regional response of each object.
It is widely accepted that single sensors cannot simultaneously achieve both high detection rates and low false alarm rates for the landmine detection problem. Thus, in this paper we consider the fusion of two types of sensors, electromagnetic induction (EMI) and ground penetrating radar (GPR). In its most common instantiation, EMI essentially provides metal detection and thus detects mines with high metal content as well as metal debris in the environment. More advanced EMI systems have begun to show potential for discriminating such debris from landmines. GPR is also used for landmine detection since it can detect and identify low-metallic subsurface anomalies. In our previous work, we have shown that a Bayesian detection approach can be applied to EMI and GPR data and provide improvements in false alarm rates. In this paper, we present results that indicate that statistical signal processing techniques can be applied simultaneously to GPR and EMI data and that reductions in false alarm rates can be achieved. We present results for two landmine detection systems, both handheld, and when possible compare the results to those obtained by a human operator who essentially fuses the outputs of the single sensor systems.
As in many areas, performance of landmine detection algorithms is judged in terms of detection and false alarm rates. For the landmine detection problem, it is often the case that detectors satisfy one requirement at the cost of poor performance with regard to the other. It is widely accepted that single sensors cannot simultaneously achieve both high detection rates and low false alarm rates, since every sensor has its advantages and disadvantages when dealing with a large variety of landmines, from large metal-cased mines to small plastic-cased mines. Thus, in this paper we consider two types of sensors, EMI and GPR. In its most common instantiation, time-domain EMI is essentially a metal detector and thus detects mines with high metal content as well as metal debris in the environment. More advanced EMI systems have begun to show potential for the discrimination of such debris from mines. GPR is also used for landmine detection since it can detect and identify low-metallic subsurface anomalies. In our previous work, we have shown that Bayesian detection approach can be applied to EMI data and provide promising results. In this paper, we present results that indicate that statistical signal processing technique applied to GPR data can also yield performance improvements. Theoretical results are verified by data collected with a developmental mine-detection system, which consists of co-located metal detectors and GPR sensors. Thus, in addition to discussing individual sensor data processing, we also present result of data fusion of both the EMI and the GPR data using the detection system.
Although the ability of EMI sensors 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 form clutter with the aid of an audio transducer. Assuming the basic information needed for discriminating landmines form clutter is largely available form existing sensors, the goal of this wok is to optimize the presentation of information to the operator and to be able to predict improved performance prior to extensive experimental testing. Traditionally, an energy 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 theoretical work indicated 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 suggest that the operator could make better sue 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. We then experimentally investigated the perceptual dimensions that most effectively convey the information in a sensor response to a listener using simulated data. Results indicated that by supplying the sensor response more appropriately to the listener, discrimination, as opposed to simple detection, could be achieved. In this paper we discuss an additional theoretical treatment of these experimental data in which we show that we can predict such improvements. These results are verified in a follow-on listening experiment with actual data measured from landmines.