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For track maintenance, there are primarily three generic sensor data fusion algorithm architectures, namely, central fusion, track fusion, and what will be referred to as composite measurement fusion. In central fusion, the sensor measurements are distributed by each sensor and the measurements from multiple sensors are then used to update the global tracks. In contrast, in track fusion a sequence of measurements is processed at the sensor or platform level to form tracks that are then distributed and this track data is then used to update the global tracks. Track fusion is also sometimes called hierarchical fusion, federated fusion or distributed fusion. Finally, in composite measurement fusion the measurements from multiple sensors for each apparent target are first combined to form a composite measurement and then the composite measurements are then used to update the global tracks. In addition there is also hybrid fusion, which is a combination of at least two of the above fusion approaches. The tracks typically include features or other target classification information. Each of these algorithm architectures has their own advantages and disadvantages. For example, track fusion may lead to substantially reduced communications load and that can be very important for physically distributed platforms. Track fusion also tends to be less sensitive to residual sensor bias errors. Central fusion typically provides more timely information. Also, for certain types of target scenarios and sensor suites, central fusion provides better accuracy in both estimation and target classification. Recent developments in track fusion make a particular hybrid fusion algorithm architecture not only appealing but practical. In this hybrid fusion, either measurement or track data in the form of a tracklet is distributed from a sensor for a target. This approach offers many of the advantages of both the central and track fusion algorithm architectures. Recently a number of methods have been identified for track fusion that take into account the cross- correlation between tracks from different sensors for a target. A tracklet, for example, is track data for a target that is not cross-correlated with any other track data for that target. Hence a special case of track fusion is tracklet fusion. Many of these track fusion methods do not, however, address the cross-correlation caused by process noise. Also, in track fusion, the selection of the mathematical model used for process noise is more critical than is commonly thought. This paper addresses these two issues.
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