Prediction, Tracking and Retrodiction for Path-Constrained Targets

Prediction, tracking, and retrodiction for targets whose motion is constrained by external conditions (e.g., shipping lanes, roads) present many challenges to tracking systems. The targets are moving along a path, defined by way-points and segments. Measurements are obtained by sensors at low revisit rates (e.g., spaceborne). Existing tracking algorithms assume that the targets follow the same motion model between successive measurements, but in a low revisit rate scenario targets may change the motion model between successive measurements. A prediction algorithm is proposed here, which addresses this issue by considering possible motion model whenever targets move to a different segment. Further, when a target approaches a junction, it has the possibility to travel into one of the multiple segments connected to that junction. To predict the probable locations, multiple hypotheses for segments are introduced and a probability is calculated for each segment hypothesis. When measurements become available, segment hypothesis probability is updated based on a combined mode likelihood and a sequential probability ratio test is carried out to reject the hypotheses with low probability. Retrodiction for path constrained targets is also considered, because in some scenarios it is desirable to find out the target’s exact location at some previous time (e.g., at the time of an oil leakage). A retrodiction algorithm is developed for path constrained targets so as to facilitate motion forensic iv analysis. Simulation results are presented to validate the proposed algorithms.