基于混沌预测的雷达微弱目标检测研究综述
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摘要
传统的雷达目标检测是将海杂波建模为随机过程,而最新的研究成果表明海杂波具有混沌特性,从而可将海杂波建模为非线性混沌模型。基于混沌预测检测微弱雷达目标信号是根据杂波信号和目标信号的动力学差异。通过海杂波训练预测器,假如待测信号当中含有雷达目标信号,则预测误差会突然增大,从而检测出目标信号。文章主要介绍了基于混沌预测检测雷达微弱目标在检测原理、国内外的研究现状以及未来的发展趋势。
The sea clutter is modeled as a random process in traditional radar target detection. The latest research results show that sea clutter is with characteristics, so the sea clutter can be modeled as a nonlinear chaotic model. Detection of weak radar target signal using chaotic prediction is based on the dynamic difference between clutter and target signal. The predictor is trained with sea clutter. If there is radar target signal in the signal to be measured, the prediction error will increase abruptly, and then the target signal is detected. This paper mainly introduces the detection principle of radar target based on chaotic prediction,the research status at home and abroad, and the development trend in the future.
The sea clutter is modeled as a random process in traditional radar target detection. The latest research results show that sea clutter is with characteristics, so the sea clutter can be modeled as a nonlinear chaotic model. Detection of weak radar target signal using chaotic prediction is based on the dynamic difference between clutter and target signal. The predictor is trained with sea clutter. If there is radar target signal in the signal to be measured, the prediction error will increase abruptly, and then the target signal is detected. This paper mainly introduces the detection principle of radar target based on chaotic prediction,the research status at home and abroad, and the development trend in the future.
引文
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[2]Leung H.Applying chaos to radar detection in an ocean environment:an experimental study[J].Oceanic Engineering IEEE Journal of,1995,20(1):56-64.
[3]S Haykin,X B Li.Detection of signals in chaos[J].Proceedings of the IEEE,1995,83(1):95-122.
[4]董华春,刘梅,权太范.高频雷达海杂波混沌特性的实验研究[J].高技术通讯,2000,10(3):41-44.
[5]Leung H,Young A.Small target detection in clutter using recursive nonlinear prediction[J].Aerospace&Electronic Systems IEEE Transactions on,2000,36(2):713-718.
[6]Hennessey G,Leung H,Drosopoulos A,et al.Sea-clutter modeling using a radial-basis-function neural network[J].Oceanic Engineering IEEE Journal of,2001,26(3):358-372.
[7]Leung H,Dubash N,Xie N.Detection of small objects in clutter using a GA-RBF neural network[J].Aerospace&Electronic Systems IEEE Transactions on,2002,38(1):98-118.
[8]温晓君.海杂波背景下基于神经网络的目标检测[J].系统仿真学报,2007,19(7):1639-1641.
[9]王福友,卢志忠,袁赣南,等.基于时空混沌的海杂波背景下小目标检测[J].仪器仪表学报,2009,30(6):1180-1185.
[10]卢宁,于才,唐伟.海杂波背景下的GRNN小目标检测[J].火控雷达技术,2012,(2):4-7.
[11]行鸿彦,祁峥东,徐伟.基于选择性支持向量机集成的海杂波背景中的微弱信号检测[J].物理学报,2012,61(24):240504-240504.
[12]毕井章,刘溶,周希辰,等.基于GRNN和时间窗方差滤波的海杂波抑制[J].电讯技术,2014,(7):932-936.
[2]Leung H.Applying chaos to radar detection in an ocean environment:an experimental study[J].Oceanic Engineering IEEE Journal of,1995,20(1):56-64.
[3]S Haykin,X B Li.Detection of signals in chaos[J].Proceedings of the IEEE,1995,83(1):95-122.
[4]董华春,刘梅,权太范.高频雷达海杂波混沌特性的实验研究[J].高技术通讯,2000,10(3):41-44.
[5]Leung H,Young A.Small target detection in clutter using recursive nonlinear prediction[J].Aerospace&Electronic Systems IEEE Transactions on,2000,36(2):713-718.
[6]Hennessey G,Leung H,Drosopoulos A,et al.Sea-clutter modeling using a radial-basis-function neural network[J].Oceanic Engineering IEEE Journal of,2001,26(3):358-372.
[7]Leung H,Dubash N,Xie N.Detection of small objects in clutter using a GA-RBF neural network[J].Aerospace&Electronic Systems IEEE Transactions on,2002,38(1):98-118.
[8]温晓君.海杂波背景下基于神经网络的目标检测[J].系统仿真学报,2007,19(7):1639-1641.
[9]王福友,卢志忠,袁赣南,等.基于时空混沌的海杂波背景下小目标检测[J].仪器仪表学报,2009,30(6):1180-1185.
[10]卢宁,于才,唐伟.海杂波背景下的GRNN小目标检测[J].火控雷达技术,2012,(2):4-7.
[11]行鸿彦,祁峥东,徐伟.基于选择性支持向量机集成的海杂波背景中的微弱信号检测[J].物理学报,2012,61(24):240504-240504.
[12]毕井章,刘溶,周希辰,等.基于GRNN和时间窗方差滤波的海杂波抑制[J].电讯技术,2014,(7):932-936.