Airborne minefield detection

Bishop, Paul K.; Perry, K.M.; Poulter, M.A.

doi:10.1049/cp:19980722

Cited by 7 publications

(1 citation statement)

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“…These have resulted into programs such as Airborne Standoff Minefield Detection System (ASTAMIDS) [Maksymonko and Breiter 1997], Remote Minefield Detection System (REMIDS) [Bishop et al 1998], and Costal Battlefield Reconnaissance and Analysis Program (COBRA) [Witherspoon et al 1995, Suzanne et al 1997]. More recently an emphasis on a highly maneuverable defense unit has resulted in the specification of the Lightweight Airborne multispectral Minefield Detection (LAMD) program.…”

Section: Introductionmentioning

confidence: 99%

<title>Algorithms and architecture for airborne minefield detection</title>

et al. 2002

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The current minefield detection approach is based on a sequential processing employing mine detection followed by minefield detection. In case of patterned minefield, minefield detection algorithms seek to exploit the minefield pattern (such as linearity) while in case of scattered minefield they utilize the spatial distribution of the mine targets. However, significant challenges remain in adequate modeling and detection of the minefield process especially in the presence of false alarms due to cultured as well as natural clutter. A short review of the literature on spatial point processes is included especially for the case of scattered minefields. It is further noted that, minefields are characterized by as a pattern (or spatial distribution) of "similar looking" mine-like objects. The sequential mine-detection followed by mine-field detection paradigm fails to exploit this critical aspect of similarity of targets for minefield detection. In this paper we propose a minefield detection scheme that incorporates similarity based clustering of targets in order to improve the performance of minefield detection. This approach can be interpreted as statistics of a marked point process. Some preliminary comparative ROC curves are evaluated for simulated minefield data in order to show the effectiveness of the minefield detection based on the marked point process. An autonomous self-organizing scheme for on-line clustering of mine-targets is also presented.

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Section: Introductionmentioning

confidence: 99%