28 July 1997 Decentralized sensor fusion and support using multiple models
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Abstract
In an aircraft application there are a number of limitations (limited computational capability, limited data bus bandwidth...) on a sensor system as well as there are a number of requirements to fulfill to make the sensor system robust against jamming and other kinds of disturbances. We are presenting a new approach to decentralized sensor fusion and tracking, where `decentralized' means that there are a considerable amount of pre-computation performed in the sensors. The novelty of the approach lies in the way the information is fed back from the central fusion unit that is handling the information delivered by the sensors. The information is fed back to the sensors to enable an improved tracking performance and at the same time not contaminating the sensor in the case the central fusion information is distorted due to jamming. The algorithm is realized by using an extend version of the multiple models algorithm AFMM (same class of algorithms as the IMM), in combination with a decentralized Kalman filtering scheme. The extension of AFMM consists of a possibility to include externally hypotheses in the regular split and prune mechanism. This way the external information is treated the same way as a prior in a Kalman filtering scheme and thus the global track estimate is confronted with the sensor's view before accepted by the sensor. In the paper the performance of the tracking algorithm is evaluated in a system consisting of a Radar and an Infrared search and track sensor.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Predrag Pucar, Predrag Pucar, Paer Norberg, Paer Norberg, } "Decentralized sensor fusion and support using multiple models", Proc. SPIE 3068, Signal Processing, Sensor Fusion, and Target Recognition VI, (28 July 1997); doi: 10.1117/12.280813; https://doi.org/10.1117/12.280813
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