Uncertain Information Fusion for Robust Target Classification in Surveillance Battlefield Environments
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In this paper, we address fusion processing techniques of multi-sensor data perceived through the IR sensors of military surveillance robots, which are positioned within a limited range with a close distance between each of the robots. To combine the multi-sensor data from the distributed battlefield robots, we propose a set of fusion rules to formulate a combined prediction from the multi-source data. Through the use of these fusion rules, we make informed predictions of the type of target, which are represented by the degrees of reliability denoted in mathematical probabilities. We have implemented three fusion operators to compare the capabilities of their fusion processing, and have experimented them in simulated, uncertain battlefield environments. We have also applied fusion techniques to an unmanned battlefield simulator with multiple surveillance robots and various types of targets. We have obtained experimental results that the fusion of the data received from cooperative surveillance robots can significantly improve the accuracy of the predictions of the type of target present in a battlefield, compared to using a single surveillance robot to make predictions.
Abstract
In this paper, we address fusion processing techniques of multi-sensor data perceived through the IR sensors of military surveillance robots, which are positioned within a limited range with a close distance between each of the robots. To combine the multi-sensor data from the distributed battlefield robots, we propose a set of fusion rules to formulate a combined prediction from the multi-source data. Through the use of these fusion rules, we make informed predictions of the type of target, which are represented by the degrees of reliability denoted in mathematical probabilities. We have implemented three fusion operators to compare the capabilities of their fusion processing, and have experimented them in simulated, uncertain battlefield environments. We have also applied fusion techniques to an unmanned battlefield simulator with multiple surveillance robots and various types of targets. We have obtained experimental results that the fusion of the data received from cooperative surveillance robots can significantly improve the accuracy of the predictions of the type of target present in a battlefield, compared to using a single surveillance robot to make predictions.
참고문헌 (15)
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