Fuzzy c-means clustering-based smart tracking model for three-dimensional manoeuvring target including unknown acceleration input

This study proposes a smart tracking model with multiple structures. To achieve optimal performance, an ideal tracking system should maintain contact with the target and constantly update its tracking data. One of the major problems to consider arises from the target motion uncertainty. Although the variance of the overall noise is time-varying and may reduce tracking efficiency, it is difficult to adaptively approximate this uncertainty. To solve these computational problems, a variety of techniques have been studied, and the adaptive interacting multiple model has shown the best result. However, predefined sub-models with different conditions for multiple models are required. Moreover, the acceleration intervals for different acceleration levels should be determined in advance. To alleviate these problems, the authors propose a multiple-structured smart tracking algorithm which properly separates the acceleration from the overall noise by fuzzy c-means clustering. The filter in the algorithm recognises the manoeuvring target as if it does not include the acceleration input, and precisely tracks the target with multiple structures. This study focuses on the acceleration causing the uncertainty and three-dimensional (3D) motion of the real target motion in the tracking problem. Finally, a 3D cruise missile example is provided to show the effectiveness of the proposed algorithm.