MATLAB Implementation of Maneuvering Target Tracking Using Interactive Multiple Model Algorithm

In this article, we discuss maneuvering target tracking and the Interactive Multiple Model (IMM) algorithm. For maneuvering target tracking, we employ tracking algorithms to determine the position and velocity of moving targets, enabling effective monitoring and prediction. The implementation typically involves state estimation using Kalman filter variants, where the system model handles target dynamics while measurement updates incorporate sensor observations. The Interactive Multiple Model algorithm is a sophisticated approach for handling uncertainty in target motion models. It operates by running multiple Kalman filters in parallel, each with different motion models (such as constant velocity, constant acceleration, or coordinated turn models). The algorithm adaptively adjusts model probabilities based on measurement residuals, effectively blending filter outputs through a Markov chain transition probability matrix. This allows the system to dynamically switch between models to accommodate changing target behavior. The performance of these algorithms is significantly influenced by the statistical model employed. The "current" statistical model used in our implementation provides high accuracy and stability by adapting the process noise covariance based on the target's current maneuver characteristics. This model assumes that the target's acceleration is time-correlated and uses a modified Rayleigh distribution to represent maneuver acceleration. Key MATLAB functions involved in this implementation include: - immfilter for Interactive Multiple Model filtering - kalmanfilter for state estimation - maneuvering_jacobian for calculating model transition matrices - measurement_update for sensor data incorporation These algorithms and models find applications across multiple domains including autonomous vehicles (for pedestrian and vehicle tracking), robotics (for motion planning and obstacle avoidance), and surveillance systems (for air traffic control and security monitoring). Therefore, in-depth research and development of these techniques and models are crucial for advancing modern tracking systems.