| This dissertation study focuses on the development of a technique for model dynamic system detection and state estimation of maneuverable targets in radar tracking applications. The new technique incorporates the Expectation and Maximization algorithm with that of the Interacting Multiple Model (IMM) estimator. The main feature of this technique is its ability to estimate the state of a dynamic system with several behavior modes, which can change from one to another. The IMM-EM estimator general structure consists of a bank of Kalman filters for the state cooperating with a filter for the parameters. The IMM-EM estimator is a suboptimal hybrid filter that has an excellent compromise between performance and complexity. Its complexity is nearly linear in the number of models. The IMM-EM algorithm has three major properties: it is recursive, modular, and has fixed computational requirements per cycle. In each cycle, it consists of three major steps: filtering, combination and model detection.; Adaptive radar systems can use IMM-EM technique in order to adjust their scan rate. Lowing the scan rate for non-maneuvering targets and increasing the scan rate for maneuvering targets. As the result, adaptive radar systems can increase their fidelity throughput by adjusting their scan rate. As an extended application of the IMM-EM technique, a novel methodology is developed for the computation of the radar scan rate based on the target model dynamics. |
