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雷达波模糊度函数控制飞控智能距离估计算法 被引量:1

Algorithm of Flight Control Range Estimation Based on Radar Signal Ambiguity Function Control
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摘要 提出一种改进的飞控雷达系统雷达波模糊度函数控制距离估计算法,并将算法有效进行移植进行软件设计,应用于飞机飞行控制智能距离估计系统中。采用对数调频信号的调频形式构建飞控距离估计信号模型,分析了该信号的模糊度函数及对距离的分辨力。提取攻击和地面目标检测性能曲线,设计了飞控智能距离估计软件系统并进行安装调试。算法仿真实验和软件系统测试性能表明,相比传统的线性调频波信号雷达距离估计,改进的飞控距离估计算法能在信噪比极低环境下,随着飞机姿态速度的快速变换,距离估计结果精确。软件设计平台具有较好友好性和人际交互性,能有效移植到飞机控制平台中。 An improved radar range estimation algorithm was proposed based on broadband logarithmic frequency modula-tion in flight control system. And the application software was designed for the aircraft flight control intelligent measure-ment system. The control ranging signal model was constructed with logarithmic FM signals. The ambiguity function of the signal and the distance resolution was analyzed. The target detection performance curve was extracted. And the flight con-trol location intelligent software system was designed with installation. Compared with the linear frequency modulated sig-nal radar ranging method, simulation result shows that the ranging precision is perfect in the low SNR condition, software design platform has good friendly and interpersonal performance, and it can transplanted to the aircraft control platform ef-fectively for application.
作者 陈婷
出处 《科技通报》 北大核心 2014年第6期231-233,共3页 Bulletin of Science and Technology
关键词 雷达距离估计 飞行控制 宽带模糊度函数 软件设计 radar range estimation flight control wideband ambiguity function software design
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  • 1于德介,杨宇,程军圣.一种基于SVM和EMD的齿轮故障诊断方法[J].机械工程学报,2005,41(1):140-144. 被引量:56
  • 2DONG H L, SUNG H L, MAN G N. Smart soft-sensing for the feedwater flowrate at PWRs using a GMDH algorithm[J]. IEEE Transactions on Nuclear Science, 2010, 57(1):340-347.
  • 3LEE J M, YOO C K, CHOI S W. Nonlinear process monitoring using kernel principal component analysis[J]. Chemical Engineering Science, 2004, 59:223-234.
  • 4ACHMAD W, YANG B S, TIAN H. Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors[J]. Expert Systems with Applications, 2007, 32:299-312.
  • 5ACHMAD W, YANG B S. Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors[J]. Expert Systems with Applications, 2007, 33:241-250.
  • 6ACHMAD W, ERIC Y K, SON J D, et al. Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine[J]. Expert Systems with Applications, 2009, 36:7252-7261.
  • 7LI Junhong, CUI Peiling. Kernel scatter-difference-based discriminant analysis for nonlinear fault diagnosis[J]. Chemometrics and Intelligent Laboratory Systems, 2008, 94:80-86.
  • 8CHO H W. Nonlinear feature extraction and classification of multivariate process data in kernel feature space[J]. Expert Systems with Applications, 2007, 32:534-542.
  • 9CAO L J, CHUA K S, CHONG W K, et al. A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine[J]. Neurocomputing, 2003, 55:321-336.
  • 10XU Yong, ZHANG D, SONG Fengxi, et al. A method for speeding up Feature extraction based on KPCA[J]. Neurocomputing, 2007, 70(4-6):1056-1061.

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