Predictive Filtering of Situation Awareness Position Information

The proposed research project is directed at the problem of reducing communications requirements for Situation Awareness Target location by using an optimal Kalman filter to predict target motion, and only communicating data when the target position differs sufficiently from the position predicted by the filter. Our Phase I approach is to quantify the bandwidth limitations of the communications system, then identify the optimal Kalman filter motion and observation models to use, based on the bandwidth limitations. We will develop a test version of the Kalman filter software, and demonstrate by tests conducted in the Wagner Data Fusion Testbed and by technical documentation that the resulting filter achieves a significant reduction in the communications requirements of the Situational Awareness system, without loss of information.