Huaiyan Wang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Huaiyan Wang has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Jiyan Yu
College of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
1 shared publication - Miao Liu
College of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
1 shared publication - Tongxing Peng
College of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
1 shared publication - Zhengpeng Yang
College of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
1 shared publication
Latest Publications
- Open Access
A Method for Predicting Trajectories of Concealed Targets via a Hybrid Decomposition and State Prediction Framework
Accurate trajectory prediction of concealed targets in complex, interference-laden environments present a formidable challenge for millimeter-wave sensor tracking systems. To address this, we propose a state-of-the-art autonomous prediction framework that integrates an Improved Sequential Variational Mode Decomposition (ISVMD) algorithm with an Extreme Learning Machine (ELM), synergistically optimized by the novel Red-billed Blue Magpie Optimizer (RBMO). The ISVMD enhances signal reconstruction by transforming noisy target echo signals into robust feature sequences, effectively mitigating the impacts of environmental disturbances and intentional concealment. Subsequently, the RBMO-optimized ELM leverages these feature sequences to predict the future trajectories of concealed targets with high precision. The RBMO further refines critical parameters within the ISVMD-ELM pipeline, ensuring adaptability and computational efficiency across diverse scenarios. Experimental validation using real-world data demonstrates that the RBMO-ISVMD-ELM approach surpasses state-of-the-art algorithms in both accuracy and robustness when predicting the trajectories of concealed ground targets, achieving optimal performance metrics under demanding conditions.
Open AccessA Method for Predicting Trajectories of Concealed Targets via a Hybrid Decomposition and State Prediction Framework
Accurate trajectory prediction of concealed targets in complex, interference-laden environments present a formidable challenge for millimeter-wave sensor tracking systems. To address this, we propose a state-of-the-art autonomous prediction framework that integrates an Improved Sequential Variational Mode Decomposition (ISVMD) algorithm with an Extreme Learning Machine (ELM), synergistically optimized by the novel Red-billed Blue Magpie Optimizer (RBMO). The ISVMD enhances signal reconstruction by transforming noisy target echo signals into robust feature sequences, effectively mitigating the impacts of environmental disturbances and intentional concealment. Subsequently, the RBMO-optimized ELM leverages these feature sequences to predict the future trajectories of concealed targets with high precision. The RBMO further refines critical parameters within the ISVMD-ELM pipeline, ensuring adaptability and computational efficiency across diverse scenarios. Experimental validation using real-world data demonstrates that the RBMO-ISVMD-ELM approach surpasses state-of-the-art algorithms in both accuracy and robustness when predicting the trajectories of concealed ground targets, achieving optimal performance metrics under demanding conditions.