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一种精确估计恒虚警检测器标度因子的通用方法 被引量：4

A Scheme of Accurate Estimation on Scaling Factors for Radar CFAR Detectors
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摘要 利用径向基函数(RBF)网络具有良好的逼近任意非线性映射的能力和快速收敛的特点,提出了一种精确估计雷达恒虚警检测器标度因子的通用方法。由于限制条件较少,该方法既可用于估计单部雷达CFAR处理的标度因子,也可用于估计雷达组网CFAR处理(集中式和分布式)的标度因子。数值分析表明,该方法可快速精确估计多种CFAR检测器的标度因子。 Using the perfect properties of Radial Basis Function (RBF) neural networks, such as approximation any non-linear mapping and quick convergence, a new scheme is proposed to estimate scaling factors for radar CFAR detectors. Owing to few constraints, it can estimate scaling factor for single radar as well as radar netting system. The numerical results indicate that the proposed scheme can quickly reach high estimation accuracy for most CFAR detectors.
作者 杨军 马晓岩 万山虎 秦江敏
机构地区 空军雷达学院信息工程系
出处 《电子与信息学报》 EI CSCD 北大核心 2004年第7期1131-1136,共6页 Journal of Electronics & Information Technology
基金 国家"863"高技术资助项目(2002AA135320)
关键词 恒虚警率 标度因子 径向基函数 神经网络 CFAR, Scaling factor, RBF, Neural networks
分类号 TN957.5 [电子电信—信号与信息处理]
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参考文献12

  • 1Srinivasan R. Fast simulation of smallest-of and geometric-mean CFAR detectors. IEE Proc.-F,2001, 148(3): 186-191.
  • 2Schilling R J, Carroll J J, A1-Ajlouni A F. Approximation of nonlinear systems with radial basis function neural networks. IEEE Trans. on Neural Networks, 2001, 12(1): 1-15.
  • 3Leshno M, Lin V Y, Pinkus A, Schocken S. Multilayer feedforward networks with a nonpolynomial activation function can approximate any function. IEEE Trans. on Neural Networks, 1993, 6(1):861-867.
  • 4Moody J, Darken C. Fast learning in networks of locally-tuned processing units. Neural Computation, 1989, 2(1): 281-294.
  • 5Orr M J L. Regularization in the radial basis function centers. Neural Computation, 1995, 7(3):606-620.
  • 6Chen S, Cowan C F N, Grant P M. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans. on Neural Networks, 1991,2(2): 302-309.
  • 7Chen S, Grant P M, Cowan C F N. Orthogonal least squares learning algorithm for trainning multioutput radial basis function networks. IEE Proc.-F, 1992, 139(6): 378-384.
  • 8Finn H M, Jonhson R S. Adaptive detection mode with threshold control as a function of spatially sampled clutter-level estimates. RCA Review, 1968, 29(2): 414-464.
  • 9Rohling H. Radar CFAR threshold in clutter and multiple target situations. IEEE Trans. on AES, 1983, 19 (l): 608-621.
  • 10Richard J T, Dilard G M. Adaptive detection algorithms for multi-targets situations. IEEE Trans. on AES, 1986, 22(4), 443-454.

同被引文献15

  • 1郭启俊,刘劲,刘宏伟.基于时频融合的分布式目标的恒虚警率检测[J].雷达科学与技术,2007,5(1):65-68. 被引量：4
  • 2Anna L Dzvonokovskaya, Hermann Rohling. Ship Detection with Adaptive Power Regression Thresholding for HF Radar. Hamburg University of Technology, Germany, 2007.
  • 3Goldman H, Bar - David D I. Analysis and Application of the Excision CFAR Detector. IEEE Pro. -F,1998,135(6) :563 - 575.
  • 4Hamid A, Viswanathan R. A New Distribueted Constant False Alarm Rate Detector[J]. IEEE Transactions on AES, 1997,33(1) :85 - 97.
  • 5[1]ROHLING H.Radar CFAR Thresholding in Clutter and Multiple Target Ssituation[J].IEEE Transactions on AES,1983,19(2):608-621.
  • 6[2]GANDHI P,KASSAM S A.Analysis of CFAR Processors in Nonhomogeneous Background[J].IEEE Transactions on AES,1988,24(2):427-445.
  • 7[3]VISWANATHAN R,EFTEKHARI A.A Selection and Estimation Test for Multiple Targets in Clutter Detection[J].IEEE Transactions on AES,1992,28(1):505-519.
  • 8[4]BARKAT M,VARSHNEY P K.Adaptive Cell averaging CFAR Detection in Distributed Sensor Networks[J].IEEE Transactions on AES,1991,27(2):424-429.
  • 9[5]RITCEY J A.Analysis of the censored Mean-Level detector[J].IEEE Transactions on AES,1986,22(4):443-454.
  • 10[6]UNER M K,VARSHNEY P K.Decentralized CFAR Detection Based on Order Ststistics[C]//In Proceeding of 36th Midwest Symposium on Circuits and systems,Detroit,MI,1993:146-149.

引证文献4

二级引证文献8

电子与信息学报
2004年 第7期

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