ID 原文 译文
17685 然后根据杂波多普勒频率与空间角度的耦合关系获得各杂波单元对应的空时导向矢量； Afterwards, the space time steering vectors of them are obtained based on the couplingrelationship between Doppler frequencies and space angles of clutter.
17686 最后利用获得新的导向矢量构成的滤波器再次对“目标”距离多普勒单元及其附近单元进行滤波处理，此时真实目标信杂噪比会大幅度降低，而离散旁瓣杂波信杂噪比变化不大，从而实现离散旁瓣杂波的判别。 Lastly, the above range-Doppler cells areprocessed again by the adaptive processing filters which are derived from the new space-time steering vectors.Obviously, the signal-clutter-noise ratio of real moving target will be reduced significantly, while it will notchange much for the discrete side-lobe clutter. Therefore, the discrete side-lobe clutter can be identified byusing the proposed method.
17687 理论分析及机载实测数据处理证明该方法具有良好的稳健性和可靠性。 Theoretical analyses and multi-channel airborne radar experiments demonstrate theeffectiveness and stability of this method.
17688 广播式自动相关监视(ADS-B)作为新一代空中交通管理(ATM)通信协议，是未来空管监视系统的关键技术。 As a new generation of Air Traffic Management(ATM) communication protocol, AutomaticDependent Surveillance-Broadcast(ADS-B) is the key technology of ATM monitoring system in the future.
17689 目前，由于ADS-B采用明文格式广播发送数据，其安全性问题受到挑战。 At present, the security of ADS-B is challenged because it broadcasts data in plaintext format.
17690 针对ADS-B易受到的欺骗干扰，该文将ADS-B位置数据和同步的二次雷达(SSR)数据作差，将两者的差值作为样本数据。 Because ADS-B issusceptible to spoofing, the difference between ADS-B position data and synchronous Secondary SurveillanceRadar(SSR) data is taken as sample data.
17691 利用多核支持向量数据描述(MKSVDD)训练样本，得到了超球体分类器，此超球体分类器能检测出ADS-B测试样本中的异常数据。 Using Multi-Kernel Support Vector Data Description(MKSVDD) totrain samples, a hypersphere classifier is obtained, which can detect anomalous data in ADS-B test samples.
17692 并且，通过粒子群算法(PSO)优化了GaussLapl和GaussTanh两种MKSVDD的惩罚因子、多核核函数系数以及核参数，提高了异常数据检测性能。 In addition, Particle Swarm Optimization (PSO) is used to optimize GaussLapl and GaussTanh MKSVDD penalty factors, coefficients of multi-kernel functions and kernel parameters.The performance of anomaly detection is improved.
17693 实验结果表明，对于随机位置偏移、固定位置偏移、拒绝服务(DOS)攻击和重放攻击，粒子群优化多核支持向量数据描述(PSO-MKSVDD)模型能检测出这4种攻击类型的异常数据。 Experimental results show that PSO-MKSVDD can detect anomalous data of randomposition deviation, fixed position deviation, Denial Of Service(DOS) attack and replay attack.
17694 且相较于其他机器学习和深度学习方法，该模型的适应性更好，异常检测的召回率和检测率更优。 In addition,compared with other machine learning and deep learning methods, this model has better adaptability andbetter recall rate and detection rate of anomaly detection.