Signal Sorting Using Teaching-Learning-Based Optimization and Random Forest

In this paper, based on the teaching-learning-based optimization (TLBO) algorithm, a new TCTLBO algorithm is formed by introducing twice chaotic searches, and then the TCTLBO algorithm is used to optimize the weighted random forest (WRF) algorithm, and the TCTLBO-WRF sorting model is established to improve the accuracy of radar signal sorting under low signal-to-noise ratio (SNR) conditions. In order to verify the validity of the algorithm, it is compared with commonly used sorting algorithms. Simulation experiments show that the proposed model can effectively improve the radar signal sorting accuracy under low SNR conditions.