Ms. Dandan Luo Profile

Dandan Luo

at Univ at Buffalo

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Proceedings Article | 11 May 2009 Paper

Detecting targets hidden in random forests

Michael Kouritzin, Dandan Luo, Fraser Newton, Biao Wu

Proceedings Volume 7336, 73360N (2009) https://doi.org/10.1117/12.817502

KEYWORDS: Rockets, Target detection, Particles, Camouflage, Neptunium, Particle filters, Matrices, Electro optics, Cameras, Detection and tracking algorithms

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Military tanks, cargo or troop carriers, missile carriers or rocket launchers often hide themselves from detection in the forests. This plagues the detection problem of locating these hidden targets. An electro-optic camera mounted on a surveillance aircraft or unmanned aerial vehicle is used to capture the images of the forests with possible hidden targets, e.g., rocket launchers. We consider random forests of longitudinal and latitudinal correlations. Specifically, foliage coverage is encoded with a binary representation (i.e., foliage or no foliage), and is correlated in adjacent regions. We address the detection problem of camouflaged targets hidden in random forests by building memory into the observations. In particular, we propose an efficient algorithm to generate random forests, ground, and camouflage of hidden targets with two dimensional correlations. The observations are a sequence of snapshots consisting of foliage-obscured ground or target. Theoretically, detection is possible because there are subtle differences in the correlations of the ground and camouflage of the rocket launcher. However, these differences are well beyond human perception. To detect the presence of hidden targets automatically, we develop a Markov representation for these sequences and modify the classical filtering equations to allow the Markov chain observation. Particle filters are used to estimate the position of the targets in combination with a novel random weighting technique. Furthermore, we give positive proof-of-concept simulations.

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