Quadrupole resonance (QR) technology for explosives detection is of crucial importance in an increasing number of applications. For landmine detection, where the detection system cannot be adequately shielded, QR has proven to be highly effective if the QR sensor is not exposed to radio frequency interference (RFI). However, strong non-Gaussian RFI in the field is unavoidable, making RFI mitigation a critical part of the signal processing. In this paper, a statistical model of the non-Gaussian RFI is presented. The QR model is used within the context of an adaptive filtering methodology to
mitigate RFI, and this approach is compared to other RFI mitigation techniques. Results obtained using both simulated and measured QR data are presented.
KEYWORDS: Sensors, Land mines, Electromagnetic coupling, Signal processing, Metals, Mining, Signal detection, Error analysis, Data centers, Target detection
Although the ability of EMI sensors to detect landmines has improved significantly, false alarm rate reduction remains a challenging problem. However, experienced operators can often discriminate mines from metallic clutter with the aid of an audio transducer. The goal of this work is to optimize the presentation of information to the operator and to determine whether information as to the presence of metal can be co-presented with information regarding mine/non-mine belief. Traditionally, an energy calculation is provided to the sensor operator via a signal whose loudness and/or frequency is proportional to the energy of the received signal. This information codes information as to the amount of metal present. However, there is information in the unprocessed sensor signal that the operator could use to effect discrimination. We have experimentally investigated the perceptual dimensions that most effectively convey the information in a sensor response to a listener using simulated data. Results indicated that, consistent with the auditory warning literature, pulsed audio signals with a distinct harmonic pattern which rise in fundamental frequency can be used to provide information which provides better performance than simple single-frequency tones. Additionally, the data indicated that the amount of metal could be coded in the rising pitch of the complex, and that the mine/no-mine probabilities could be coded in a separate dimension - the pulse rate. In this paper, we describe these results in detail.
KEYWORDS: Sensors, Signal processing, Signal detection, Land mines, Mining, Data modeling, Detection theory, Electromagnetic coupling, Metals, Detection and tracking algorithms
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.
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.
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