IMM_MSPDA Association for Multi-Sensor Multi-Target Data Fusion

This paper introduces an IMM-MSPDA (Interacting Multiple Model with Markov Switching Probability Data Association) algorithm for multi-sensor multi-target data fusion, which significantly improves the accuracy and reliability of maneuvering target tracking. The IMM-MSPDA algorithm integrates multiple motion models to track targets under different maneuvering conditions, while employing a Markov switching probability data association mechanism to validate tracking results. The algorithm implementation typically involves model probability calculation, state interaction, and Markov-based association validation, where each model's likelihood is weighted according to switching probabilities. Performance validation confirms the superior capability of multi-model algorithms for tracking maneuvering targets. Furthermore, this algorithm can be applied to other domains such as autonomous driving, robotics, and intelligent surveillance systems, where robust multi-target tracking is essential.