Federated Information Mode-Matched Filter in an IMM Algorithm
Authors: | Kim Yong-Shik, Pusan National University, Korea, Republic of Hong Keum-Shik, Pusan National University, Korea, Republic of |
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Topic: | 7.5 Intelligent Autonomous Vehicles |
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Session: | Intelligent Autonomous Vehicles |
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Keywords: | Automated guided vehicle, extended Kalman filter, information filter, interacting multiple model, navigation, sensor fusion, target tracking filter |
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Abstract
In this paper, a tracking algorithm for autonomous navigation of automated guided vehicles (AGVs) operating in container terminals is presented. The developed tracking algorithm is an interacting multiple model algorithm used to detect other AGVs using fused information from multiple sensors. In order to detect other AGVs, two kinematic models were derived: A constant velocity model for linear motion, and a constant-speed turn model for curvilinear motion. In the constant-speed turn model, a nonlinear information filter is used in place of the extended Kalman filter in nonlinear systems. The model-matched filter used in multi-sensor environments takes the form of a federated nonlinear information filter. In this paper, the structural features and information sharing principle of the federated information filter are discussed.