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Target tracking using multiple sensors can provide better performance than using a single sensor. One approach to multiple target tracking with multiple sensors is to first perform single sensor tracking and then fuse the tracks from the different sensors. Two processing architectures for track fusion are presented: sensor to sensor track fusion, and sensor(More)
In a distributed estimation or tracking system, local estimates are first generated from individual sensors. The state estimates of associated objects are then fused to generate the global estimates. The fusion algorithm has to deal with correlated estimation errors due to common past information or common process noise. Most approaches to estimation fusion(More)
Track fusion over a network of sensors requires association of the tracks before the state estimates can be combined. Track association generally involves two steps: evaluating an association metric to score each track-to-track association hypothesis, and selecting the best assignment between two sets of tracks. In many applications feature-aided track(More)
Target tracking is an important application for putation and thus the processing has to be distributed, with Abrelsstract ho T etsor t ngtworks. i eas e an i a th a nfrgy nodes communicating processed data instead of sensor data. wireless ad hoc sensor networks. Because of the energy Distributed tracking for sensor networks was first invesand communication(More)
Centralized fusion provides, by definition, the best (optimal) estimation performance by directly using measurements of all sensors. When bandwidth is limited, sensors can only communicate their local processing results or “state estimates” instead of measurements to the fusion node. The goal of optimal fusion is to reconstruct the optimal(More)
Multiple hypothesis tracking (MHT) addresses difficult tracking problems by maintaining alternative association hypotheses until enough good data, e.g., features, are collected to select the correct hypotheses. Traditional MHT's cannot track targets over long durations because they frequently generate too many hypotheses to maintain the correct ones with(More)
The theoretic fundamentals of distributed information fusion are well developed. However, practical applications of these theoretical results to dynamic sensor networks have remained a challenge. There has been a great deal of work in developing distributed fusion algorithms applicable to a network centric architecture. In general, in a distributed system(More)
Evan Fortunato, William Kreamer, Shozo Mori, Chee-Yee Chong, Gregory Castanon BAE Systems, Advanced Information Technologies, Burlington, MA, U.S.A. {evan.fortunato,bill.kreamer,shozo.mori,chee.chong,greg.castanon}@baesystems.com This work supported by DARPA/IXO and AFRL/IFKE under Contract No. FA8750-05-C-0115 Approved for public release; distribution is(More)