Aiming at the shortcomings of the research on individual identification technology of emitters, which is primarily based on theoretical simulation and lack of verification equipment to conduct external field measurements, an emitter individual identification system based on Automatic Dependent Surveillance–Broadcast is designed. On one hand, the system completes the individual feature extraction of the signal preamble. On the other hand, it realizes decoding of the transmitter’s individual identity information and generates an individual recognition training data set, on which we can train the recognition network to achieve individual signal recognition. For the collected signals, six parameters were extracted as individual features. To reduce the feature dimensions, a Bessel curve fitting method is used for four of the features. The spatial distribution of the Bezier curve control points after fitting is taken as an individual feature. The processed features are classified with multiple classifiers, and the classification results are fused using the improved Dempster–Shafer evidence theory. Field measurements show that the average individual recognition accuracy of the system reaches 88.3%, which essentially meets the requirements.

针对发射器个体识别技术研究的不足之处，该研究主要基于理论模拟，缺乏用于进行外场测量的验证设备，设计了基于自动相关监视-广播的发射器个体识别系统。一方面，系统完成信号前同步码的单个特征提取。另一方面，它实现了对发射机个人身份信息的解码，并生成了个人识别训练数据集，我们可以在该训练数据集上训练识别网络以实现个人信号识别。对于收集的信号，提取了六个参数作为单个特征。为了减小特征尺寸，对四个特征使用贝塞尔曲线拟合方法。拟合后的贝塞尔曲线控制点的空间分布被视为一个单独的特征。使用多个分类器对处理后的特征进行分类，并使用改进的Dempster-Shafer证据理论对分类结果进行融合。现场测量表明，该系统的平均个体识别准确率达到88.3％，基本可以满足要求。