Position estimation technology is a critical foundation for spatial cognition in intelligent systems. Conventional position estimation methods remain vulnerable to measurement errors and noise interference. At the same time, existing deep learning-based approaches demonstrate notable limitations in multi-source sensor data fusion, dynamic environment adaptation, and the optimization of computational complexity versus real-time performance. To address these challenges, this paper presents a novel position estimation framework based on a hybrid Informer-ESN architecture. The proposed model effectively integrates ESN's nonlinear mapping capabilities and short-term memory characteristics with the Informer network's superior temporal modeling and feature extraction capacities, thereby leveraging their complementary strengths for enhanced sequential data processing. This integrated approach demonstrates robust performance in handling complex nonlinear systems under specialized noise conditions. Experimental results indicate that our model achieves significant improvements in both estimation accuracy and robustness compared to conventional methods. Specifically, it enables more precise tracking of unmanned surface vehicles while maintaining consistent performance across varying operational sequences.