UWB-SAR图像中的目标检测与鉴别
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摘要
针对超宽带合成孔径雷达(Ultra Wide Band Synthetic Aperture Radar,即UWB-SAR)探测叶簇隐蔽目标的应用需求,对UWB-SAR图像中的目标检测与鉴别问题进行了系统深入的研究。
首先综述了UWB-SAR目标检测与鉴别问题的研究现状、方法和意义,指出了UWB-SAR目标检测与鉴别在UWB-SAR自动目标识别系统中的位置和现存问题。
第二章研究了UWB-SAR图像中的恒虚警率(Constant False Alarm Rate,即CFAR)目标检测问题。采用试验数据分析了UWB-SAR图像中各种不同植被覆盖类型杂波的统计特性,并基于得到的结论提出了一种CFAR目标检测方法,该方法在一个通用形式下,能够在已知类型的不同植被覆盖杂波中实现CFAR处理;提出了一种快速统计量计算方法,该方法能大大提高CFAR算法的计算效率。
第三章针对目标检测与鉴别的应用需求研究了UWB-SAR图像中的目标特征提取方法。基于UWB-SAR回波模型和成像原理,研究了用频域滤波的方法在UWB-SAR图像频域支撑中提取目标的方向域、频率域和分辨率域特征的方法,给出了相应的滤波器设计;建立了UWB-SAR目标模型,改进了隐马尔可夫模型和统计分析方法用于特征整合;研究了UWB-SAR点目标检测及树干杂波抑制问题;实验结果表明,所研究的结果能改善UWB-SAR目标检测与鉴别的性能。
第四章研究了UWB-SAR图像中的非均匀背景目标检测问题。针对UWB-SAR图像中杂波统计模型随植被覆盖类型漂移的问题,在改进了α截集雷达杂波识别方法的基础上,提出了一种对杂波统计模型自适应的CFAR检测方法,有效地实现了在不同植被覆盖类型下的CFAR目标检测;针对杂波边沿的非均匀性,基于变量索引CFAR方法研究了UWB-SAR图像中强弱杂波边沿处的CFAR检测问题,实现了在杂波边沿处的CFAR目标检测;实验结果验证了方法的有效性。
第五章研究了UWB-SAR图像中目标检测与鉴别的定量性能评估方法。基于接收机工作特性曲线,提出了两种对目标检测算法性能进行定量评估的方法;以目标与杂波的可分离性为度量,引入了两个用于评估特征提取方法的性能指标;针对UWB-SAR图像中杂波与目标的特点,改进了UWB-SAR图像中目标检测与鉴别性能评估中若干参量的计算方法。
最后系统地总结了全文的工作,并给出了进一步研究的建议。
To detect concealed targets in foliage with Ultra Wide Band Synthetic Aperture Radar (UWB-SAR), the problem of target detection and discrimination in UWB-SAR image is systematically studied in this thesis.At first, the present research state, methods and significance of UWB-SAR target detection and discrimination are summarized, and the situation of UWB-SAR target detection and discrimination in UWB-SAR automatic target recognition system and its current existing problem is also pointed out.The problem of Constant False Alarm Rate (CFAR) target detection in UWB-SAR image is studied in chapter 2. The clutter statistical property of different vegetation bestrow is analysed, and a CFAR target detection method is proposed based on the corresponding conclusions. Under a common form, the method can perform CFAR processing in different vegetation bestrow clutter. A fast statistics computation method is proposed for CFAR processing, which can save the computation expense a lot.In Chapter 3, the target feature extraction method is studied for target detection and discrimination in UWB-SAR image. Based on the UWB-SAR echo model and image principal, the target feature extraction method from aspect domain, frequency domain and resolution domain in UWB-SAR image by filtering in frequency domain is presented, and the corresponding filter design are offered. With the constructed UWB-SAR target model, some statistical analysis methods and Hidden Markov Model are modified and combined for feature fusion. The point target detection method in UWB-SAR image is studied, and the result is used for trunk clutter suppression. The effectiveness of these methods is confirmed by the experiment results.The problem of nonhomogeneous environment target detection in UWB-SAR imagery is studied in chapter 4. To conquer the clutter model excursion with the change of vegetation bestrow, based on the modification of a truncation clutter recognition method, a CFAR target detection method that can adapt to the change of clutter statistical model is presented. To deal with the clutter edge at the boundary between a block of intensive clutter and a block of weak clutter, based on the variance index, a CFAR target detection method is proposed. The experiment results show that the methods are effective.In chapter 5, the quantificational performance evaluation method of UWB-SAR target detection and discrimination is studied. Based on the receiver operating characteristic curve, two methods are presented for quantificational performance evaluation of target detection. Using the separability of the target and clutter as the measure, two indexes for evaluating the performance of target feature extraction are introduced. A calculation method of several parameters for UWB-SAR target detection and discrimination is modified to cope with the speciality of UWB-SAR target and clutter.
At last, the research of the thesis is summarized and some problems and interesting area for future research are pointed out.
首先综述了UWB-SAR目标检测与鉴别问题的研究现状、方法和意义,指出了UWB-SAR目标检测与鉴别在UWB-SAR自动目标识别系统中的位置和现存问题。
第二章研究了UWB-SAR图像中的恒虚警率(Constant False Alarm Rate,即CFAR)目标检测问题。采用试验数据分析了UWB-SAR图像中各种不同植被覆盖类型杂波的统计特性,并基于得到的结论提出了一种CFAR目标检测方法,该方法在一个通用形式下,能够在已知类型的不同植被覆盖杂波中实现CFAR处理;提出了一种快速统计量计算方法,该方法能大大提高CFAR算法的计算效率。
第三章针对目标检测与鉴别的应用需求研究了UWB-SAR图像中的目标特征提取方法。基于UWB-SAR回波模型和成像原理,研究了用频域滤波的方法在UWB-SAR图像频域支撑中提取目标的方向域、频率域和分辨率域特征的方法,给出了相应的滤波器设计;建立了UWB-SAR目标模型,改进了隐马尔可夫模型和统计分析方法用于特征整合;研究了UWB-SAR点目标检测及树干杂波抑制问题;实验结果表明,所研究的结果能改善UWB-SAR目标检测与鉴别的性能。
第四章研究了UWB-SAR图像中的非均匀背景目标检测问题。针对UWB-SAR图像中杂波统计模型随植被覆盖类型漂移的问题,在改进了α截集雷达杂波识别方法的基础上,提出了一种对杂波统计模型自适应的CFAR检测方法,有效地实现了在不同植被覆盖类型下的CFAR目标检测;针对杂波边沿的非均匀性,基于变量索引CFAR方法研究了UWB-SAR图像中强弱杂波边沿处的CFAR检测问题,实现了在杂波边沿处的CFAR目标检测;实验结果验证了方法的有效性。
第五章研究了UWB-SAR图像中目标检测与鉴别的定量性能评估方法。基于接收机工作特性曲线,提出了两种对目标检测算法性能进行定量评估的方法;以目标与杂波的可分离性为度量,引入了两个用于评估特征提取方法的性能指标;针对UWB-SAR图像中杂波与目标的特点,改进了UWB-SAR图像中目标检测与鉴别性能评估中若干参量的计算方法。
最后系统地总结了全文的工作,并给出了进一步研究的建议。
To detect concealed targets in foliage with Ultra Wide Band Synthetic Aperture Radar (UWB-SAR), the problem of target detection and discrimination in UWB-SAR image is systematically studied in this thesis.At first, the present research state, methods and significance of UWB-SAR target detection and discrimination are summarized, and the situation of UWB-SAR target detection and discrimination in UWB-SAR automatic target recognition system and its current existing problem is also pointed out.The problem of Constant False Alarm Rate (CFAR) target detection in UWB-SAR image is studied in chapter 2. The clutter statistical property of different vegetation bestrow is analysed, and a CFAR target detection method is proposed based on the corresponding conclusions. Under a common form, the method can perform CFAR processing in different vegetation bestrow clutter. A fast statistics computation method is proposed for CFAR processing, which can save the computation expense a lot.In Chapter 3, the target feature extraction method is studied for target detection and discrimination in UWB-SAR image. Based on the UWB-SAR echo model and image principal, the target feature extraction method from aspect domain, frequency domain and resolution domain in UWB-SAR image by filtering in frequency domain is presented, and the corresponding filter design are offered. With the constructed UWB-SAR target model, some statistical analysis methods and Hidden Markov Model are modified and combined for feature fusion. The point target detection method in UWB-SAR image is studied, and the result is used for trunk clutter suppression. The effectiveness of these methods is confirmed by the experiment results.The problem of nonhomogeneous environment target detection in UWB-SAR imagery is studied in chapter 4. To conquer the clutter model excursion with the change of vegetation bestrow, based on the modification of a truncation clutter recognition method, a CFAR target detection method that can adapt to the change of clutter statistical model is presented. To deal with the clutter edge at the boundary between a block of intensive clutter and a block of weak clutter, based on the variance index, a CFAR target detection method is proposed. The experiment results show that the methods are effective.In chapter 5, the quantificational performance evaluation method of UWB-SAR target detection and discrimination is studied. Based on the receiver operating characteristic curve, two methods are presented for quantificational performance evaluation of target detection. Using the separability of the target and clutter as the measure, two indexes for evaluating the performance of target feature extraction are introduced. A calculation method of several parameters for UWB-SAR target detection and discrimination is modified to cope with the speciality of UWB-SAR target and clutter.
At last, the research of the thesis is summarized and some problems and interesting area for future research are pointed out.
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