2003/10/01 by T. Kirubarajan, Y. Bar-Shalom, Yaakov Bar‐Shalom · 139 citations
Computer Science · Engineering · Mathematics · #Alpha beta filter #Artificial intelligence #Computer science #Control (management) #Control theory (sociology) #Ensemble Kalman filter #Estimator #Extended Kalman filter #GNSS positioning and interference #Inertial Sensor and Navigation #Invariant extended Kalman filter #Kalman filter #Mathematics #Moving horizon estimation #Statistics #Target Tracking and Data Fusion in Sensor Networks #Tracking (education)
paper · doi:10.1109/taes.2003.1261143
published in IEEE Transactions on Aerospace and Electronic Systems 39(4), 1452-1457 (Institute of Electrical and Electronics Engineers)
openalex publication_date 2003/10/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/26
In this paper, a performance comparison between a Kalman filter and the interacting multiple model (IMM) estimator is carried out for single-target tracking. In a number of target tracking problems of various sizes, ranging from single-target tracking to tracking of about a thousand aircraft for air traffic control, it has been shown that the IMM estimator performs significantly better than a Kalman filter. In spite of these studies and many others, the condition under which an IMM estimator is desirable over a single model Kalman filter versus an IMM estimator are quantified here in terms of the target maneuvering index, which is a function of target motion uncertainty, measurement uncertainty, and sensor revisit interval. Using simulation studies, it is shown that above a certain maneuvering index an IMM estimator is preferred over a Kalman filter to track the target motion. These limits should serve as a guideline in choosing the more versatile, but somewhat costlier, IMM estimator over a simpler Kalman filter.