The application discloses a cross-
modal solving method and
system for low-altitude target identification based on a
hybrid expert network, relates to the technical field of low-altitude target identification, and solves the technical problem that the existing technology causes loss of spatial geometric features and reduces the accuracy of target identification results due to forced 2D dimension reduction alignment in cross-
modal solving. The method inputs image data, audio data and
radar detection data into a solving model to obtain a solving result. The solving model comprises a visual expert network, an audio expert network, a
radar expert network, a cross-
modal adaptive calibration module, a
Transformer space-
time correlation decoder and a multi-task solving head. The cross-modal adaptive calibration module performs depth back-projection of visual features to a three-dimensional
voxel grid to obtain visual probability voxels, projects audio latent feature probabilities to the three-dimensional
voxel grid to obtain audio probability voxels, and fuses visual probability voxels and audio probability voxels that meet
geometric consistency conditions in the neighborhood of each
radar anchor
voxel with the radar anchor voxel to generate a three-dimensional fusion voxel grid.