Robust Track Association and Fusion with Extended Feature Matching

In this work, we propose a new data processing architecture as well as track association and fusion algorithms to improve target classification and tracking accuracy using distributed and, possibly, legacysensor platforms. We present a robust data fusion algorithm that can incorporate target classes/types at the fusion center when receiving sensor reports and/or local tracks. We aim to tackle the following technical challenges in feature aided tracking. – Unknown number of targets: When the fusion center does not have any prior knowledge on the number of targets in the surveillance area, track fusion becomes extremely difficult especially when targets are closely spaced. – Measurement origin uncertainty: The local tracker does not know which measurement comes from which target and each local tracker may provide false tracks or incorrect target types. Consequently, the fusion center does not know which local tracks are from the same target and fusion has to be made based on imperfect data association. – Tracks from legacy sensor systems: Existing trackers often have very different filter designs. Some may be based on the state-of-theart multiple model algorithm while some on the fixed gain Kalman filter. Thus some trackers can report both target state estimate and the associated covariance to the fusion center, but others may only provide target state estimate without the covariance information. Those legacy sensor systems require special treatment in the development of fusion algorithms. Our track association framework can also incorporate tracks with extended feature points and kinematic constraints, which improves both data association and tracking accuracy.