Sample unbalanced HRRP ground target recognition based on improved Lightgbm

Abstract

Radar-based ground target recognition faces significant challenges, including complex terrain, diverse target types, high recognition difficulty, and low accuracy. Moreover, the non-cooperative nature of military targets limits access to comprehensive target data, leading to sample imbalances that further degrade recognition performance. Addressing these issues, this paper proposes a target recognition method based on LightGBM, which balances model complexity and recognition accuracy. This method integrates a weighted focal loss function with dual-stage ground clutter suppression and enhancement techniques. Initially, during the data preprocessing phase, spherical hypothesis clustering, coupled with the local outlier factor algorithm, is utilized to mitigate ground target clutter. Subsequently, in the training phase for target recognition, the weights of imbalanced samples are dynamically adjusted to augment the model's learning capacity and heighten its focus on challenging targets. This approach dynamically adjusts the weights of imbalanced samples, thereby enhancing the model's learning ability and increasing its attention to difficult-to-classify instances. Additionally, to better accommodate complex backgrounds and bolster the model's robustness, an adaptive weighting coefficient adjustment mechanism is incorporated. Ultimately, ground targets are identified using a LightGBM multi-classifier. Simulations based on actual radar seeker data have validated the effectiveness of this method, and the recognition performance for six distinct target types has been evaluated. Comparative analyses with other classifiers demonstrate that this method exhibits superior performance in ground target recognition under conditions of imbalanced samples.