GA-SMOTE-RF Enhanced Kalman Filter with Adaptive Noise Reduction

Low-noise free-space laser communication has widespread applications in military and rescue fields, but atmospheric turbulence severely affects communication quality. This paper proposes an intelligent classification and adaptive noise reduction system that integrates genetic algorithms (GA), synthetic minority oversampling technique (SMOTE), random forest (RF), and Kalman filtering, significantly improving turbulence channel interference classification accuracy and communication quality. Simulation results show that the system achieves a classification accuracy of 98.27%, with corresponding F1-score of 0.9732 and MCC of 0.9653, far exceeding algorithms such as SVM and KNN. After noise reduction, the average RMSE for 400 signal groups is 0.6983, with zero estimated delay, and the mean and standard deviation of the innovative sequence are -0.0049 and 0.6960, respectively, demonstrating excellent signal quality and efficient real-time processing capabilities. Beyond synthetic simulations, we conducted real-world FSO data studies to validate practical applicability. A 24-h field experiment collected 283 real FSO measurement windows, on which the proposed GA-SMOTE-RF method achieves 0.308 RMSE and 0.75% Average Regret in Kalman filter parameter selection, outperforming KNN and SVM, confirming practical applicability for real-world FSO systems.