摘要
针对接收通道噪声影响和传感器引起的信号畸变,仅提高单传感器的识别性能远不能满足需求,提出了一种基于协作表示的雷达辐射源多传感器融合识别方法。首先,在训练阶段构成离线的完备字典,而多个传感器的接收信号在字典上求得协作表示系数及分类残差。接着通过设计合理的基本概率分配函数,将多传感器的分类残差与单元素事件的D-S理论相结合,根据最大信任决策规则得到融合识别结果。采用常见的6种雷达辐射源信号进行了仿真实验,仿真结果验证了提出方法的有效性,且较单传感器提高了识别性能,具有较好的噪声鲁棒性,适用于小样本的识别。
Aimed at the impact of noise in receiving channels and signal distortion caused by sensors, simply improving the recognition performance of a single sensor no long meeting the demands, a collaborative represen- tation based radar emitter fusion recognition of multi-sensor method is proposed. Firstly, a completed dictionary is constructed with off-line sample signals in the training phase, on which collaborative coefficients of multiple receiving signals and classification residuals are obtained. Then, multi-sensor classification residuals and the D-S theory are combined by designing the basic probability assignment function reasonably, and consequently the fu- sion recognition result is acquired according to the maximum belief rule. Simulation experiments are performed by adopting 6 types of conventional radar emitters, the results validate the effectiveness of the proposed method and show that the method not only improves the performance in comparison with the single sensor, but is robust to noise and applicable to small-sample-size recognition.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2016年第12期2725-2730,共6页
Systems Engineering and Electronics
基金
国家高技术研究发展计划(863计划)(2014AA7014061)
国家自然科学基金(61501484)资助课题
关键词
雷达辐射源识别
协作表示
决策级融合
D-S证据理论
小样本问题
radar emitter recognitions collaborative representations decision-level fusion
D-S evidence the-ory
small-sample-size problem
作者简介
周志文（1989-），男，博士研究生，主要研究方向为辐射源识别、信息融合。Email：mini_paper@sina．com
黄高明（1972-），男，教授，博士研究生导师，主要研究方向为盲信号处理、无源探测。E-mail：hgaom@126．com
高俊（1957-），男，教授，博士研究生导师，主要研究方向为数字信号处理、数字通信技术。E-mail：gaojunnj@163．com
