Abstract
Using the Bayesian framework of measurement likelihood, sensor data can be combined in a rigorous manner to produce a concise summary of knowledge of a target’s location in the state-space. This framework allows sensor data to be fused across time, space and sensor modality. When target motion and sensor measurements are modeled correctly, these “likelihood maps” contain all the relevant information for making inferences about the underlying target state. By combining all data without thresholding for detections, targets with low signal to noise ratio (SNR) can be detected where standard detection algorithms may fail. As the calculation cost of computing likelihood maps over the entire state space is prohibitively high for most practical applications, we propose an approximation which is computed in a distributed fashion, locally at each sensor node. We analyze this approximation, and give cases where it agrees with the centrally calculated likelihood map. Detection and tracking examples using measured data from multi-modal sensors (Radar, EO, Seismic) are presented.
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Gallagher, J., Moses, R., Ertin, E. (2009). Distributed Computation of Likelihood Maps for Target Tracking. In: Krishnamachari, B., Suri, S., Heinzelman, W., Mitra, U. (eds) Distributed Computing in Sensor Systems. DCOSS 2009. Lecture Notes in Computer Science, vol 5516. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02085-8_21
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DOI: https://doi.org/10.1007/978-3-642-02085-8_21
Publisher Name: Springer, Berlin, Heidelberg
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