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Browsing by Author "Lu, Suihua, author"

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    Network multiple frame assignment architectures
    (Colorado State University. Libraries, 2001) Lu, Suihua, author; Poore, Aubrey, advisor; Kirby, Michael J., committee member; Zachmann, David W., committee member; Miranda, Rick, 1953-, committee member
    Multiple target tracking methods divide into two broad classes, namely single frame and multiple frame methods. The most successful of the multiple frame methods are multiple hypothesis tracking (MHT) and multiple frame assignments (MFA). In dense tracking environments the performance improvements of multiple frame methods over single frame methods is very significant, making it the preferred solution for many tracking problems. Thus, in addition to the availability single frame processing, multiple frame data association methods are an essential class of methods for almost all tracking needs. The application of multiple frame tracking methods must consider an architecture in which the sensors are distributed across multiple platforms. Such geometric and sensor diversity has the potential to significantly enhance tracking and discrimination accuracy. A centralized architecture in which all measurements are sent to one location and processed with tracks being transmitted back to the different platforms is a simple one that is probably optimal in that it is capable of producing the best track quality (e.g., purity and accuracy) and a consistent air picture. The centralized tracker is, however, unacceptable for several reasons, notably the communication overloads and single-point-failure. Thus, one must turn to a distributed architecture for both estimation/fusion and data association. One of the simplest network-centric architectures is that of placing a centralized tracker on each platform. The architecture is called Network MFA Centralized, which removes the problem of single-point-failure. However, due to communication delays in the network, the order the measurements arrive at different platforms varies. Each composite tracker is making its own tracking decisions based on the data it receives, regardless of decisions of other platforms. Therefore, a consistent air picture may not be achieved across the network. Thus, the objective of this thesis is the development of two near-optimal Network-Centric MFA architectures, namely Network MFA on Local Data and Network Tracks and Network MFA on All data and Network Tracks, that preserve the quality of a centralized tracker across a network of platforms while managing communication loading and achieving a consistent air picture. One technique that has proved useful for achieving SlAP is to require that each platform be in charge of assigning its own measurements to the network tracks. In the architecture of Network MFA on Local Data and Network Tracks, only local data are used in the sliding windows and track initiations are based on local data only. In the architecture of Network MFA on All Data and Network Tracks, all data (remote and local) are used in the sliding window. Communication loading is only addressed by the architectures in that track states and their error covariances are not required to be transmitted back to the various platforms. The results of extensive computations are presented to validate the differences in four tracking architectures.