Dr. Biao Wu Profile

Dr. Biao Wu

Postdoctoral Fellow at Univ of Alberta

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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.

Proceedings Article | 17 April 2008 Paper

Handling target obscuration through Markov chain observations

Michael Kouritzin, Biao Wu

Proceedings Volume 6968, 69680S (2008) https://doi.org/10.1117/12.779837

KEYWORDS: Radar, Doppler effect, Electronic filtering, Target detection, Raster graphics, Particles, Particle filters, Target recognition, Stochastic processes, Electromagnetic radiation

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Target Obscuration, including foliage or building obscuration of ground targets and landscape or horizon obscuration of airborne targets, plagues many real world filtering problems. In particular, ground moving target identification Doppler radar, mounted on a surveillance aircraft or unattended airborne vehicle, is used to detect motion consistent with targets of interest. However, these targets try to obscure themselves (at least partially) by, for example, traveling along the edge of a forest or around buildings. This has the effect of creating random blockages in the Doppler radar image that move dynamically and somewhat randomly through this image. Herein, we address tracking problems with target obscuration by building memory into the observations, eschewing the usual corrupted, distorted partial measurement assumptions of filtering in favor of dynamic Markov chain assumptions. In particular, we assume the observations are a Markov chain whose transition probabilities depend upon the signal. The state of the observation Markov chain attempts to depict the current obscuration and the Markov chain dynamics are used to handle the evolution of the partially obscured radar image. Modifications of the classical filtering equations that allow observation memory (in the form of a Markov chain) are given. We use particle filters to estimate the position of the moving targets. Moreover, positive proof-of-concept simulations are included.

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