基于神经网络的地形等高线辅助导航

Addressing the issues of low accuracy in terrain elevation matching and poor real-time performance in iterative search methods, we propose a neural network-based method for terrain contour-aided navigation. This study focuses on two-dimensional contour feature matching to enhance the robustness of matching algorithms under elevation noise. Considering the rotational and translational invariance characteristics of wavelet transforms, we extract contour edge features using wavelet transform sub-bands. Furthermore, we present a contour edge feature matching algorithm based on neural networks that replaces the conventional iterative search matching process by using multiple sub-networks for classification recognition, greatly enhancing the algorithm's matching accuracy and real-time performance. In comparison to terrain elevation matching, simulation results show that the suggested approach improves the matching success rate by more than 30% and reduces the matching time by more than 97% when compared to iterative search-based terrain contour matching techniques.