Share Email Print
cover

Proceedings Paper

Predicting improved human auditory discrimination for land mine detection using EMI sensors
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

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.

Paper Details

Date Published: 22 August 2000
PDF: 8 pages
Proc. SPIE 4038, Detection and Remediation Technologies for Mines and Minelike Targets V, (22 August 2000); doi: 10.1117/12.396238
Show Author Affiliations
Yingyi Tan, Duke Univ. (United States)
Lisa G. Huettel, Duke Univ. (United States)
Leslie M. Collins, Duke Univ. (United States)


Published in SPIE Proceedings Vol. 4038:
Detection and Remediation Technologies for Mines and Minelike Targets V
Abinash C. Dubey; James F. Harvey; J. Thomas Broach; Regina E. Dugan, Editor(s)

© SPIE. Terms of Use
Back to Top