Open Access

Pascal Ketterer, Patrick Hoher, Johannes Reuter

• Interacting multiple model filters are most commonly used in the context of maneuvering targets, as they can represent the different dynamics of a real system by combining the estimates of multiple models. However, the interacting multiple model approach generally requires more computational effort than a single Kalman filter. In this work, down-sampling is used to reduce the computational effort. We propose an adaptive scheme to maintain the accuracy of the estimator to a defined level. To this end, the trace of the innovation covariance matrix is evaluated, and if it lies above a certain threshold, out-of-sequence measurements are iteratively used to improve the estimate until the uncertainty threshold is met. The approach is evaluated by Monte Carlo analysis. The results show that with this approach, the number of measurements to be processed, and thus the computational effort can be dynamically reduced, while the accuracy remains at a desired level.