| 摘要 |
A location history is a collection of locations over time for an object. By applying a recurring time period to a location history, it can be converted into a stochastic model of the location history. For example, a location history can be reorganized based on intervals that subside a recurring cycle. In a described implementation, training a location history model involves traversing each interval of multiple cycles of a target location history. After each object location at each interval is entered into a training matrix, the intervals can be normalized to determine relative probabilities per location for each interval of a designated cycle. The training and resulting location history model can be Markovian or non-Markovian. Applications include probabilistic location estimation, fusion of location estimates, location-history simulation, optimal scheduling, transition analysis, clique analysis, and so forth.
|
