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焦平面热成像系统离散采样性能评价方法
引用本文:崔建平,王吉晖,金伟其,白廷柱,高稚允. 焦平面热成像系统离散采样性能评价方法[J]. 红外与激光工程, 2013, 42(8): 1973-1978,2007
作者姓名:崔建平  王吉晖  金伟其  白廷柱  高稚允
作者单位:1.北京理工大学 光电成像技术与系统教育部重点实验室,北京 100081
基金项目:国家自然科学基金(61007057);武器预研基金
摘    要:在充分研究焦平面热成像系统欠采样成因的基础上,分析了ANN法和基于信息理论的光电成像系统欠采样混频评价方法,把欠采样混频看作是系统的噪声,并和其他系统噪声一起加到MRTD模型中,给出了包含欠采样噪声的MRTD模型。对比含欠采样噪声的MRTD模型仿真数据与文献中实测MRTD数据,该模型对MRTD仿真结果与测量数据走势基本一致,能够用于评价欠采样混频对热成像系统性能的影响,但模型的精确性还需要进一步修正。下一步将通过测量各种型号焦平面热像仪的MRTD,对含欠采样混频的MRTD模型进行修正。

关 键 词:焦平面热成像系统   欠采样修正MRTD模型   欠采样噪声   性能评价
收稿时间:2012-12-04

Performance evaluation method of discrete sampling on the focal plane thermal imaging systems
Cui Jianping , Wang Jihui , Jin Weiqi , Bai Tingzhu , Gao Zhiyun. Performance evaluation method of discrete sampling on the focal plane thermal imaging systems[J]. Infrared and Laser Engineering, 2013, 42(8): 1973-1978,2007
Authors:Cui Jianping    Wang Jihui    Jin Weiqi    Bai Tingzhu    Gao Zhiyun
Affiliation:1.Key Laboratory of Photoelectric Imaging Technology and System,Ministry of Education,Beijing Institute of Technology,Beijing 100081,China
Abstract:Based on the full study for the under-sampling aliased response of focal plane thermal imaging systems, the Aliasing As Noise(AAN) method and the optical imaging system under-sampling evaluation method on the basis of information theory were analyzed. The under-sampling aliased response was regarded as a system noise and put into MRTD model like other system noise. The new MRTD model containing the under-sampling noise was obtained. By contrasting the data which got from the simulation of the new MRTD model and references, the change between the simulation and data in references is basically same. So the new model can be used to evaluate the impact of under-sampling on the performance of thermal imaging systems, however it needs further amendment for more accurate. The next step, the new MRTD model with aliasing response will be amended by measured MRTD data of focal plane thermal imaging systems.
Keywords:focal plane array(FPA) thermal imaging system  MRTD model with aliasing  under-sampling noise  performance evaluation
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