Radar emitter individual identification plays an increasingly important role in electronic support measures (ESM) system. To cope with the problems of low accuracy and poor stability of radar emitter individual identification, a novel method, named MGWEPSO-BXGBoost (Mutation Grey Wolf Elite Particle Swarm Optimization Balanced eXtreme Gradient Boosting), is proposed. In consideration of the fact that the number of radar signals measured from the real environment is usually imbalanced, a novel balance mechanism is designed for XGBoost. MGWEPSO is further proposed to simultaneously optimize the prime parameters, whose values and combinations have a great influence on the identification results, of BXGBoost to improve the identification accuracy. To overcome the local optimal solution problem, leadership mechanism in wolves, elite rule and the idea of mutation are adopted, which is also conducive to improving the ability to find the global optimal solution. Furthermore, experiments based on signals measured in the real environment are carried out to demonstrate the effectiveness of the proposed method. The results verify that MGWEPSO-BXGBoost has high accuracy and strong stability even when the sample size of each individual is limited and imbalanced.

雷达辐射源个人识别在电子支持措施（ESM）系统中扮演着越来越重要的角色。针对雷达发射机个体识别精度低，稳定性差的问题，提出了一种新的方法，称为MGWEPSO-BXGBoost（突变灰狼精英粒子群优化均衡极端梯度提升）。考虑到从实际环境中测得的雷达信号数量通常不平衡这一事实，为XGBoost设计了一种新颖的平衡机制。进一步提出了MGWEPSO同时优化BXGBoost的主要参数，这些参数的值和组合对识别结果有很大的影响，以提高识别精度。为了克服局部最优解问题，狼的领导机制，采用精英规则和突变思想，也有利于提高寻找全局最优解的能力。此外，基于真实环境中测得的信号进行了实验，以证明该方法的有效性。结果证明，即使每个个体的样本量有限且不平衡，MGWEPSO-BXGBoost仍具有较高的准确性和较强的稳定性。