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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)
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)
This paper is concerned with analytical and semi-analytical methods for predicting performance of track-to-track association, in terms of probability of each track being correctly associated with the track that shares the same origin, when association is performed by an optimal assignment algorithm. The focus of this paper is to quantify how much feature or(More)
– This paper describes a generalization of Murty's algorithm generating ranked solutions for classical assignment problems. The generalization extends the domain to a general class of zero-one integer linear programming problems that can be used to solve multi-frame data association problems for track-oriented multiple hypothesis tracking (MHT). The(More)
This paper presents numerical performance evaluation of various algorithms that have been developed for track-to-track fusion and association problems, through a long history of the distributed multiple target tracking algorithm development. We will use a general linear-Gaussian standard model both for the target state and the sensor observation models.(More)
In track fusion, the measurements of individual sensors for each target are processed to generate local state estimates, which are then fused to obtain the global state estimate for the target. When there is no process noise or the fusion rate equals the sensor observation rate, the standard tracklet fusion or equivalent measurement fusion algorithm(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)