毫米波主被动复合近程探测目标识别方法研究
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摘要
毫米波探测技术以其良好的综合性能,成为当前精确探测技术发展的主要方向之一。随着探测技术的发展及探测目标背景的日趋复杂,对目标要了解的信息也越来越多,不仅要获得目标的距离、速度和角度等信息,而且还要对目标进行准确的跟踪和定位。因此,复合探测技术成为发展的必然趋势。本论文的研究基于毫米波主被动复合近程探测技术展开。主动探测采用毫米波近程高分辨力雷达,探测距离较远,可以获得目标的速度、角度、距离等信息；被动探测使用毫米波辐射计,不发射电磁波,因而没有电磁污染和目标闪烁效应,工作比较隐蔽,不易被发现。两种体制相结合构成的主被动复合探测系统,可以使主动和被动探测优势互补,探测目标更为详细的信息,进而提高目标识别率。
信号处理是精确探测系统一个不可或缺的部分,先进的信号处理技术是提高毫米波探测系统精确性的一个主要因素。论文重点对毫米波主被动复合探测的信号处理进行了深入的分析与研究。主动探测通过不同的波形设计方法,研究了各种高分辨力信号实现高分辨距离像的方法及其优缺点和应用。采用匹配追踪的时频分析方法,选择合适的原子库,对回波信号进行稀疏分解与重构,提取出表示回波信号特征信息的本征量,并利用相关向量机及模糊相关向量机的方法,实现了对毫米波近程高分辨力雷达距离像的目标识别。被动探测在稀疏分解对信号进行去噪处理的基础上,根据波形的时域特征以及频域、时频域特征研究了粗糙集理论和人工神经网络在目标识别中的应用,两者有机结合的粗神经元网络,可接受数据集合的上下边界数据,提高神经网络的性能,在信息处理中发挥了极大的优越性。
接着,对主被动复合探测信息融合的目标识别进行了研究。基于信息融合理论的体系结构和融合层次,结合毫米波主被动探测系统的特点,提出了一种目标特征的空域-时域融合结构：先利用基于模糊聚类的D-S证据理论对主、被动探测器的信号进行空域融合；然后将系统在不同高度下的空域融合结果作为可测函数,利用模糊积分的方法对每个高度的空域融合结果再进行时域融合,得到更可靠的对目标的一致性描述,进一步提高融合目标识别结果。最后,论文对毫米波主被动复合近程探测系统的目标识别技术实现进行了设计,由于复合探测系统工作模式复杂、信息处理量大,对信号处理的实时性要求高,因此利用高速DSP和FPGA芯片构建了一个信号处理系统。结合二者优点,可兼顾速度和灵活性,给出了信号处理系统的软硬件设计,作了相关测试实验,并根据实际电路,对高速数字设计中信号的完整性作了分析。
As its well synthetical performance, millimeter wave detecting technology has become one of the most current developing directions of precise detection. Along with the development of detecting technology and the complexity of spotting background, more and more information of the target is needed to be required, not only the distance, speed and angle of target, but also the accurate tracking and localizing to the target. Therefore, the compounding detection is an inevitable trend of development. Research of this dissertation is just based on millimeter wave active/passive compounding short-range detecting technology. Active detection adopts MMW short-range high-resolution radar, which has far detecting distance and can access the speed, angle, distance and other information of target. Passive detection uses MMW radiometer, which does not emit electromagnetic wave, has no glitter effect and works snugly. The compounded detecting system, working together by these two systems, can make the advantage to complement each other, so to obtain the more detailed information of target and the higher rate of target identification.
Signal processing is an indispensable part of a precise detection system. Advanced signal processing technology is a primary factor to improve the accuracy of detecting system. So the dissertation is focus on the thorough analysis and research of target identification system of MMW active/passive compounding detection. For active detection, through the different methods of waveform design, ways to synthesize high-resolution range profiles by different high-resolution signals is researched. And then, based on matching pursuit time-frequency analyzing method and appropriate atomic library, the echo signal is sparse decomposed and restructured to extract the features of the signal. At last, by the way of relevance vector machine and fuzzy relevance vector machine, target identification of the MMW short-range high-resolution radar range profile is realized. In passive detection, the radiation signal of target is denoised by the method of sparse decomposition at first. Then, based on the features of waveform in time domain, frequency domain, and time-frequency domain, target identification by application of rough set theory and artificial neural networks is detailed. The rough neural networks, organically combined of the two methods, may accept the data set with up-and-low boundary, which improves the performance of network and displays enormous superiority in information processing.
Following above, target identification of active/passive compounded detecting data fusion is discussed. Based on the architecture and the level of information fusion theory, a spatial-temporal fusion structure of target feature is designed, according to the characteristic of MMW active/passive detecting systems. Method of D-S evidence theory based on fuzzy clustering is adopted in space domain fusion for active and passive detecting signal. And then, the space domain fusing results of different heights are taken as the measurable functions, using fuzzy integral approach to secondary time domain fusing. So that, a more reliable consistent description of the target can be achieved, and the fusion results of target identification is improved greatly.
Finally, the realization of target identification technology in MMW active/passive short-range detection system has been designed in the dissertation. Due to the complexity of working mode, large amount of information processing, and high requirement of real-time processing, high-speed chip DSP and FPGA have been chosen to construct a signal processing system. The design may satisfy the requirement of speed and flexibility synchronously by combining the advantages of DSP and FPGA. The software and hardware design of signal processing system is work out and correlative experiments have been down. Furthermore, based on the actual circuit, signal integrity in high-speed digital circuit design is analyzed to achieve better circuit design.
信号处理是精确探测系统一个不可或缺的部分,先进的信号处理技术是提高毫米波探测系统精确性的一个主要因素。论文重点对毫米波主被动复合探测的信号处理进行了深入的分析与研究。主动探测通过不同的波形设计方法,研究了各种高分辨力信号实现高分辨距离像的方法及其优缺点和应用。采用匹配追踪的时频分析方法,选择合适的原子库,对回波信号进行稀疏分解与重构,提取出表示回波信号特征信息的本征量,并利用相关向量机及模糊相关向量机的方法,实现了对毫米波近程高分辨力雷达距离像的目标识别。被动探测在稀疏分解对信号进行去噪处理的基础上,根据波形的时域特征以及频域、时频域特征研究了粗糙集理论和人工神经网络在目标识别中的应用,两者有机结合的粗神经元网络,可接受数据集合的上下边界数据,提高神经网络的性能,在信息处理中发挥了极大的优越性。
接着,对主被动复合探测信息融合的目标识别进行了研究。基于信息融合理论的体系结构和融合层次,结合毫米波主被动探测系统的特点,提出了一种目标特征的空域-时域融合结构：先利用基于模糊聚类的D-S证据理论对主、被动探测器的信号进行空域融合；然后将系统在不同高度下的空域融合结果作为可测函数,利用模糊积分的方法对每个高度的空域融合结果再进行时域融合,得到更可靠的对目标的一致性描述,进一步提高融合目标识别结果。最后,论文对毫米波主被动复合近程探测系统的目标识别技术实现进行了设计,由于复合探测系统工作模式复杂、信息处理量大,对信号处理的实时性要求高,因此利用高速DSP和FPGA芯片构建了一个信号处理系统。结合二者优点,可兼顾速度和灵活性,给出了信号处理系统的软硬件设计,作了相关测试实验,并根据实际电路,对高速数字设计中信号的完整性作了分析。
As its well synthetical performance, millimeter wave detecting technology has become one of the most current developing directions of precise detection. Along with the development of detecting technology and the complexity of spotting background, more and more information of the target is needed to be required, not only the distance, speed and angle of target, but also the accurate tracking and localizing to the target. Therefore, the compounding detection is an inevitable trend of development. Research of this dissertation is just based on millimeter wave active/passive compounding short-range detecting technology. Active detection adopts MMW short-range high-resolution radar, which has far detecting distance and can access the speed, angle, distance and other information of target. Passive detection uses MMW radiometer, which does not emit electromagnetic wave, has no glitter effect and works snugly. The compounded detecting system, working together by these two systems, can make the advantage to complement each other, so to obtain the more detailed information of target and the higher rate of target identification.
Signal processing is an indispensable part of a precise detection system. Advanced signal processing technology is a primary factor to improve the accuracy of detecting system. So the dissertation is focus on the thorough analysis and research of target identification system of MMW active/passive compounding detection. For active detection, through the different methods of waveform design, ways to synthesize high-resolution range profiles by different high-resolution signals is researched. And then, based on matching pursuit time-frequency analyzing method and appropriate atomic library, the echo signal is sparse decomposed and restructured to extract the features of the signal. At last, by the way of relevance vector machine and fuzzy relevance vector machine, target identification of the MMW short-range high-resolution radar range profile is realized. In passive detection, the radiation signal of target is denoised by the method of sparse decomposition at first. Then, based on the features of waveform in time domain, frequency domain, and time-frequency domain, target identification by application of rough set theory and artificial neural networks is detailed. The rough neural networks, organically combined of the two methods, may accept the data set with up-and-low boundary, which improves the performance of network and displays enormous superiority in information processing.
Following above, target identification of active/passive compounded detecting data fusion is discussed. Based on the architecture and the level of information fusion theory, a spatial-temporal fusion structure of target feature is designed, according to the characteristic of MMW active/passive detecting systems. Method of D-S evidence theory based on fuzzy clustering is adopted in space domain fusion for active and passive detecting signal. And then, the space domain fusing results of different heights are taken as the measurable functions, using fuzzy integral approach to secondary time domain fusing. So that, a more reliable consistent description of the target can be achieved, and the fusion results of target identification is improved greatly.
Finally, the realization of target identification technology in MMW active/passive short-range detection system has been designed in the dissertation. Due to the complexity of working mode, large amount of information processing, and high requirement of real-time processing, high-speed chip DSP and FPGA have been chosen to construct a signal processing system. The design may satisfy the requirement of speed and flexibility synchronously by combining the advantages of DSP and FPGA. The software and hardware design of signal processing system is work out and correlative experiments have been down. Furthermore, based on the actual circuit, signal integrity in high-speed digital circuit design is analyzed to achieve better circuit design.
引文
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