21 May 2015 Efficient target tracking with an ad-hoc network of omni-directional sensors
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Abstract
Ad-hoc networks of omni-directional sensors provide an efficient means to obtain low-cost, easily deployed, reliable target tracking systems. To remove target position dependency on the target power, a transformation to another coordinate system is introduced. It can be shown that the problem of sensing target position with omni-directional sensors can be adapted to the conventional Kalman filter framework. To validate the proposed methodology, first an analysis is conducted to show that by converting to log-ratio space and at the same time reducing the number of parameters to track, no information about target position is lost. The analysis is done by deriving the CRLBs for the position estimation error in both original and transformed spaces and showing that they are the same. Second, to show how the traditional Kalman filter framework performs, a particle filter that works off the transformed coordinates is designed. The number of particles is selected to be sufficiently large and the result is used as ground truth to compare with the performance of the Kalman tracker. The comparisons are done for different target movement speeds and sensor density modes. The results provide an insight into Kalman tracker performance in different situations.
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Kalin Atanassov, Kalin Atanassov, } "Efficient target tracking with an ad-hoc network of omni-directional sensors", Proc. SPIE 9497, Mobile Multimedia/Image Processing, Security, and Applications 2015, 94970J (21 May 2015); doi: 10.1117/12.2177632; https://doi.org/10.1117/12.2177632
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