Combination of multisource information to ground target tracking

Ground targets can be detected by multiple classes of sources in military surveillance. There are two main challenges for the acquisition of ground situation picture from data collected by multiple sources. First, different sources provide different information that describes military entities at different granularities and accuracies. This makes processing of data in one unified tracker difficult. Secondly, the data update rates of these sources vary, some update rates could be very low (such as hours), leading to greater difficulty for data association. This paper presents our attempt in multi-source ground target tracking, taking the above two issues into consideration. Targets are tracked in groups, and multiple trackers are designed so that data of different granularities are processed by the respective trackers. Tracks from these trackers are then correlated to form the common picture. Two strategies are proposed to handle the problem of varying data update rate. The first strategy is to exploit different approaches to calculate the beliefs of data association according to update rates. When update rate is high, the belief is calculated by a distance function based on estimated kinematical states. When update rate is low, the belief of data association is computed using Bayesian network. Bayesian network infers the beliefs based on observed information and domain knowledge. The second strategy is to exploit the complementary information in different trackers to improve data association. The first step is to find the correlation among tracks from different trackers. This track-track correlation information is fed back to modify the beliefs of data associations in the tracks. Experiments demonstrated that such combination of multi-source information not only produces more complete ground picture, but also helps to improve the data association accuracy in the respective trackers.