Multi-Modal Signal Prediction Method Based on Trajectory Time Series

As technology advances, target positioning technology is widely applied in automated systems. However, the multi-modal nature of trajectory time series makes accurately predicting dynamic target behaviors challenging. Modal decomposition can be used to process and analyze multi-modal signals, but the decomposed signals still face challenges in classification accuracy and modeling prediction. This paper innovatively proposes a multi-modal signal analysis and fusion prediction algorithm (MSAFPA) based on trajectory time series to address these issues. First, the algorithm performs a detailed analysis of the decomposed signals to accurately reveal their intrinsic characteristics and patterns. Based on these characteristics, the signals are classified accordingly. The algorithm then skillfully integrates the unique advantages of polynomial models, autore-gressive moving average (ARMA) models, and support vector regression (SVR) models in time series prediction. It conducts targeted and detailed predictions for each type of modality, achieving deep fusion of multi-modal prediction results. This ensures the comprehensiveness and accuracy of the prediction outcomes. This innovative solution provides an effective approach to the challenges of predicting and fusing multi-modal signals. Comparative analyses with existing methods demonstrate the superior performance of MSAFPA in trajectory prediction.