Research on adaptive Kalman filtering based on interacting multiple model - art. no. 683324

Some limits of standard Kalman filtering are simply analyzed. Such as the indefiniteness of motion resulted from targets maneuvering and the lower predictive precision brought about by non or little adaptive capabilities make standard Kalman filtering lower tracking precision and stabilization. Interacting multiple model algorithm is adopted to combine with Kalman filter, and a new adaptive Kalman filtering algorithm for improving tracking capabilities is proposed. Multiple models are designed to represent system possible running patterns, and "current" statistical model is designated as one of them. Each model has an independent Kalman filter, and the general state estimation is a kind of mixing data output produced by interacting among these models' state estimations through certain mixing probabilities. Each model state estimation is produced by one Kalman filter corresponding this system model. In simulation tests, three system models are designed to work, CV model, CA model and "current" statistic model. Tests show that the indefiniteness resulted from the target motional model approximately describing the target motional pattern, the lower adaptive tracking capabilities, and the lower tracking precision and stabilization of in targets tracking are improved efficiently. Moreover, the strong nonlinear problem is solved effectively.