Interactive Multiple Model Target Tracking with UKF and EKF Filtering

The Interactive Multiple Model (IMM) is a widely used technique in target tracking applications, primarily designed to handle scenarios where target motion patterns frequently change. This approach dynamically adjusts the weights of multiple motion models by maintaining a bank of parallel filters and calculating model probabilities through Markovian switching. In code implementation, this typically involves maintaining separate state estimators for each motion model and performing probability-weighted fusion at each time step.

Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF) represent two prominent nonlinear filtering methods commonly employed for state estimation in target tracking. EKF linearizes nonlinear systems using first-order Taylor series expansion, making it efficient for weakly nonlinear systems but potentially inaccurate for highly nonlinear scenarios. UKF utilizes the Unscented Transform to directly approximate nonlinear distributions through carefully selected sigma points, avoiding linearization errors and demonstrating superior performance in strongly nonlinear conditions. Implementation-wise, UKF typically requires 2n+1 sigma points (where n is the state dimension) for propagation through nonlinear functions.

Within the IMM framework, UKF and EKF can be strategically combined to accommodate different motion patterns. For instance, constant velocity models can employ EKF for computational efficiency, while highly maneuvering motion models may utilize UKF for enhanced accuracy. The system adaptively selects optimal filtering strategies through dynamic model probability updates, where model transition probabilities are typically predefined based on expected maneuvering characteristics. This probabilistic weighting mechanism significantly improves overall tracking performance.

In practical applications, IMM-UKF/EKF algorithms are extensively used in radar systems, UAV navigation, and autonomous driving applications. These implementations effectively handle sudden target maneuvers through sophisticated model management, thereby enhancing tracking stability and precision. Code implementation typically involves modular design with separate modules for model probability calculation, filter interaction, and state fusion.