Generalized positioning system based on use of a statistical filter

Abstract

A generalized positioning system, corresponding method, and filter for use in the generalized positioning system, based on Kalman filtering or any other kind of filtering that produces an estimate of position, or other aspect of the motion of an object, using statistical measures involving current and previous measurements of position or any other aspect of the motion of the object, but with greater responsiveness than a positioning system based on the usual statistical filtering, such as Kalman filtering. The generalized positioning system uses a calculated association probability for each measurement in a set of measurements of position (or other aspect of the motion) at a particular instant of time, the association probabilities being used in the calculation of a combined measurement innovation (residual), which is in turn used in calculating the next estimate of position (or other motion state information). In one aspect of the invention, a pre-validation is performed, based on a usually pre-determined gate width of a validation region. By using a combined measurement innovation, the generalized positioning system is able to accept simultaneous measurements from different sources of information, including different kinds of sources of information, so that it can provide position estimates (or estimates for other components of the state of motion, i.e. of other aspects of the motion) based on, e.g., information received both from satellites, used in a global positioning system, and also from cellular base station in a cellular telephone network.