Interactive Multiple Model Algorithm
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This implementation provides a MATLAB program for the Interactive Multiple Model (IMM) algorithm designed for hybrid system estimation. During tracker design, the selection of target models directly impacts filter performance quality. The multi-model approach significantly improves filter accuracy and stability by dividing the system into multiple models and employing appropriate switching rules to select optimal models. The algorithm operates through three key phases: interaction/mixing of model-conditioned estimates, model-specific filtering (typically using Kalman filters), and probability update and combination. This implementation likely includes functions for model transition probability management, state mixing calculations, and parallel filter banks. The method achieves superior estimation results across different system modes and finds extensive applications in practical domains such as target tracking, signal processing, and automatic control systems.
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