基于时序融合网络的短波测向误差预测模型OA

Aiming at the problem that shortwave direction-finding errors exhibit intertwined character-istics of multi-periodicity,non-stationarity and long-range dependencies,which in turn limits the pre-diction accuracy,a shortwave direction-finding error prediction model based on time-series fusion net-work(TSF-Net)is proposed.Firstly,in view of the coexistence of random and consecutive missing val-ues in the raw data,a hierarchical preprocessing strategy is designed,combining linear interpolation and periodic-similarity-based imputation to construct well-aligned and regular high-quality input se-quences.Secondly,a parallel dual-stream feature extraction architecture is adopted,where the Ti-mesNet branch targetedly extracts the multi-periodic evolution patterns of direction-finding errors,and the Transformer branch targeted captures the global long-range dependencies and non-stationary time-varying characteristics.Finally,an adaptive weighted fusion module is constructed to realize comple-mentary fusion of dual-branch features through a static and dynamic dual-path weighting mechanism.Simulation results show that the proposed model achieves a 6.2%to 8.7%relative improvement in pre-diction accuracy compared with the second-best TimesNet baseline model.