摘要
针对目前反辐射技术及反辐射导弹技术的迅速发展,对空有源预警系统易被发现和攻击。设计了一种基于推扫式实时图像处理的无源预警系统,通过光学成像方法实现区域范围内的对空预警目标。提出了一种新的图像并行处理架构实现了空中目标的实时识别预警。同时提出了一种新的基于粗糙集和支持向量机迭代的识别算法,通过对训练样本基于类隶属度进行分块和排序,加速对支持向量的选取和最优分界面的构建,使样本集的训练时间大大减少,并且提高了识别函数的识别率,泛化性能和可实现性。实验结果显示该系统的目标识别反应时间约为15.1ms,识别率高达93%,达到了对空实时预警的要求。
When the current anti-radiation technology and anti-radiation missile technology developed rapidly, active antiaircraft warning system can be easily found and attacked. Passive warning system is designed based on real-time scan-image processing by the optical imaging method to achieve region-wide early warning. A new parallel image processing structure is proposed to achieve real-time recognition of early warning air targets. At the same time, a new iterative identification algorithm is proposed based on Rough Sets （RS） and Support Vector Machine （SVM）. The training data sets are separated into some blocks and their samples in sort-subjection are arranged to speed up the selection for support vector and the construction of the optimal interface. Therefore, training time of the sample set was greatly reduced and the recognition rate, generalization, realization performance of its recognition function was increased. Experimental results show that the system reached real-time early warning of air requirements while target recognition reaction time was about 15.1 ms and recognition rate was as high as 93%.
出处
《光电工程》
CAS
CSCD
北大核心
2011年第1期15-22,共8页
Opto-Electronic Engineering
基金
国家863高技术研究发展计划资助项目(NO.2007AA12Z113)
