17 April 2008 A distributed automatic target recognition system using multiple low resolution sensors
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Proceedings Volume 6968, Signal Processing, Sensor Fusion, and Target Recognition XVII; 69680K (2008); doi: 10.1117/12.778104
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States
Abstract
In this paper, we propose a multi-agent system which uses swarming techniques to perform high accuracy Automatic Target Recognition (ATR) in a distributed manner. The proposed system can co-operatively share the information from low-resolution images of different looks and use this information to perform high accuracy ATR. An advanced, multiple-agent Unmanned Aerial Vehicle (UAV) systems-based approach is proposed which integrates the processing capabilities, combines detection reporting with live video exchange, and swarm behavior modalities that dramatically surpass individual sensor system performance levels. We employ real-time block-based motion analysis and compensation scheme for efficient estimation and correction of camera jitter, global motion of the camera/scene and the effects of atmospheric turbulence. Our optimized Partition Weighted Sum (PWS) approach requires only bitshifts and additions, yet achieves a stunning 16X pixel resolution enhancement, which is moreover parallizable. We develop advanced, adaptive particle-filtering based algorithms to robustly track multiple mobile targets by adaptively changing the appearance model of the selected targets. The collaborative ATR system utilizes the homographies between the sensors induced by the ground plane to overlap the local observation with the received images from other UAVs. The motion of the UAVs distorts estimated homography frame to frame. A robust dynamic homography estimation algorithm is proposed to address this, by using the homography decomposition and the ground plane surface estimation.
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Zhanfeng Yue, Pramod Lakshmi Narasimha, Pankaj Topiwala, "A distributed automatic target recognition system using multiple low resolution sensors", Proc. SPIE 6968, Signal Processing, Sensor Fusion, and Target Recognition XVII, 69680K (17 April 2008); doi: 10.1117/12.778104; https://doi.org/10.1117/12.778104
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KEYWORDS
Unmanned aerial vehicles

Automatic target recognition

Sensors

Detection and tracking algorithms

Video

Cameras

Lawrencium

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