Gaussian Mixture Filter-Incorporated Self-Attention Residual Neural Network for UAV Joint Error Identification and Target Localization

Target localization is a key technology for unmanned aerial vehicle (UAV) applications in various fields, such as target tracking, and task planning. However, the accuracy of UAV localization is significantly affected by systematic and random errors in attitude data, and the nonlinearity of measurement model together with the unknown distribution of measurement noise. To achieve robust and precise localization in long-distance oblique scenarios based on dynamic platforms, this paper proposes a Gaussian Mixture Filter-incorporated Self-Attention Residual Neural Network (GMAR) algorithm for target localization. Firstly, an end-to-end self-attention residual neural network (SA-ResNN) model is built to accurately model both systematic and random errors in attitude angle. The self-attention mechanism is innovatively introduced to enhance the global feature representation capability of the residual module. Then, the Gaussian mixture (GM) filter utilizes a Gaussian mixture model to model the prior and posterior probability density functions, which can effectively capture the uncertainty in the state probability density function under nonlinear measurement models and enhance the robustness of the localization system. Finally, simulations and flight experiments demonstrate that the proposed GMAR algorithm can significantly improve the localization accuracy and robustness of ground targets in long-distance oblique scenarios.