Abstract
Incorporating obstacle information into maneuvering target-tracking algorithms may lead to a better performance when the target when the target maneuver is caused by avoiding collision with obstacles. In this paper, we propose a fuzzy-logic-based method incorporating new obstacle information into the interacting multiple-model (IMM) algorithm (FOIA-MM). We use convex polygons to describe the obstacles and then extract the distance from and the field angle of these obstacle convex polygons to the predicted target position as obstacle information. This information is fed to two fuzzy logic inference systems; one system outputs the model weights to their probabilities, the other yields the expected sojourn time of the models for the transition probability matrix assignment. Finally, simulation experiments and an Unmanned Aerial Vehicle experiment are carried out to demonstrate the efficiency and effectiveness of the proposed algorithm.
Highlights
The Multiple-Model (MM) algorithm is an effective approach to maneuvering target tracking in many real-world applications [1], that works by regarding the maneuvering as the transition to motion modes and describes them by a finite number of kinematic models
Its popularization and further development have been spearheaded by the interacting MM (IMM) algorithm, which was proposed by Bar-Shalom and
Simulation experiments were carried out to compare the performances of the fuzzy-logic-based obstacle information-aided multiple-model (FOIA-MM) algorithm and the state-dependent variations of the interacting multiple-model (SD-IMM) algorithm
Summary
The Multiple-Model (MM) algorithm is an effective approach to maneuvering target tracking in many real-world applications [1], that works by regarding the maneuvering as the transition to motion modes and describes them by a finite number of kinematic models. The performance of the IMM algorithm suffers when there are too many motion models that overlap and compete with each other [4,5,6]. Circular obstacles are presented in the state-dependent variations of the interacting multiple-model (SD-IMM). The SD-IMM algorithm gives the same adjustment value for the same distance between a target and obstacle circles with different radiuses. Fuzzy inference systems are applied to simplify the complex relationship between the obstacle information and the update of the MP and TPM; a fuzzy-logic-based, obstacle information-aided multiple-model (FOIA-MM) algorithm is presented.
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