Application of Bayesian Optimization and Ensemble Learning in Target Recognition of Missile-Borne Radar

Abstract

When air-to-air missile strikes low and ultra-low altitude targets, the performance of missile-borne radar to distinguish targets and clutter is reduced. In this paper, aiming at the problem of target recognition of missile-borne radar, multiple target recognition models are established by using a variety of ensemble learning algorithms and Baye-sian optimization algorithm, and their performance is tested and compared. Through feature extraction, data standardization and feature selection, a target clutter data set with optimized features is constructed. The target recognition models of XGBoost, LightGBM and CatBoost are constructed and tested by using Bayesian optimization algorithm to adjusting the parameters. The test results show that the target recognition efficiency of XGBoost, LightGBM and CatBoost is better than that of random forest, support vector machine and AdaBoost. XGBoost, LightGBM, CatBoost and random forest are selected as base classifiers, and the target recognition model of Stacking is constructed and tested. The test results show that the target recognition accuracy of the Stacking algorithm is 98.88%, which is better than each of four models that constitute the stacking algorithm, but its operating efficiency is greatly reduced. In summary, the target recognition accuracy of CatBoost algorithm can reach 98.03%. Although it is not optimal, its test time is 0.011 s, and its operation efficiency is more obvious.

Keywords

• |missile-borne radar|target recognition|bayesian optimization|ensemble learning|xgboost|lightgbm|catboost|stacking