Multi-model algorithm is the state-of-the-art approach to maneuvering target tracking. Due to the increasing types of targets and complication of motion environment, it is more and more difficult to meet the target tracking requirements by only using the position measurement. In addition to the position measurement, the knowledge about targets and their environment can be adopted to adaptively adjust the three key factors, i.e., the model set, transition probability matrix and model probability in the multi-model algorithm to achieve better performance. This paper analyzes the principles and methods of knowledge-aided multiple-model maneuvering target tracking algorithm. According to the subjects (model set, transition-probability matrix and model probability) that the knowledge being used to adjust, the adjustment manner (intelligent methods and non-intelligent methods), the principles and characteristics of adjustments are introduced, respectively. Finally, future research of knowledge-aided multi-model maneuvering target tracking algorithm is given.