基于电磁环境感知的MIMO雷达信号处理的理论与方法研究
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摘要
多输入多输出（Multiple-input Multiple-Output MIMO）雷达，作为一种新兴雷达技术，正吸引着越来越多人的关注。MIMO雷达采用多个发射天线发射相互正交或独立的信号覆盖了整个空域，在接收端，通过匹配滤波处理来恢复各发射信号分量，并通过数字波束形成实现同时多波束，由于信号分集所激发出的远多于实际天线数的虚拟阵元，扩大了阵列孔径。与传统雷达系统相比，MIMO雷达自由度明显增大，参数估计的精度和空间分辨率提高，可估计的最大信源数增加。因此，MIMO雷达具有广阔的应用前景。本文针对MIMO雷达在抗干扰方面所面临的主要问题，提出了一种基于电磁环境感知（ElectromagneticEnvironmental Sensory—EES）的MIMO雷达。并对EES-MIMO雷达的工作原理、基本结构及其关键技术进行了深入的研究。本文的主要研究工作可分为五个部分：基于EES-MIMO雷达的工作原理及系统构成、EES-MIMO雷达的电磁环境感知方法、发射波形设计、基于单基地线阵EES-MIMO雷达MUSIC谱峰梯度搜索方法及基于单基地L阵EES-MIMO雷达MUSIC谱峰遗传算法和粒子群算法。
第一部分首先对单基地收发分集MIMO雷达的工作原理及系统构成、单基地收发分集MIMO雷达的虚拟阵元及特点进行了详细的阐述，为了提高MIMO雷达的抗干扰能力，提出了基于电磁环境感知的MIMO雷达，分析了该EES-MIMO雷达的工作原理及基本构成，为后续的研究工作奠定了理论基础。
复杂的阵列结构致使现在很多常用的噪声功率谱估计的算法难以应用于EES-MIMO雷达的电磁环境感知，这就需要探寻合适的噪声功率谱估计的算法。第二部分工作首先分析了雷达干扰的类型:有源干扰和无源杂波干扰，并详细地阐述了有源干扰和无源杂波干扰的表达形式及实现方式。针对窄带瞄准式干扰，将现代谱估计中AR模型参数互谱估计的SVD算法推广应用于EES-MIMO雷达的电磁环境感知的噪声功率谱估计，导出了EES-MIMO雷达标准噪声功率谱估计的互谱SVD算法。该算法具有不受EES-MIMO雷达阵列形式限制的优势。而后，基于点频式干扰，提出了有色噪声背景下正弦参数估计的互谱奇异值分解方法，该算法可以通过扩大互相关函数维数的方法来提高正弦频率估计的精度；基于扫频干扰，采用了基于二次型变换的互相关估计方法，该算法不仅有效地抑制了观测噪声的影响，而且将原本非平稳随机过程转化成为一个平稳的随机过程的参数估计问题，从而大大减少了参数估计的计算量，有效地提高了扫频信号参数估计的精度。最后，基于上述三种噪声功率谱估计的算法给出了幅度谱成型的方法。并利用仿真结果验证了有效性。
第三部分讨论了EES-MIMO雷达发射波形设计问题，首先介绍了正交波形设计的概念和优化的目标函数，讨论了用遗传算法进行波形设计的基本方法。并在此基础上讨论了正交离散频率编码（DFCW），正交频分复用（OFDM）相位编码和多载频相位编码（MCPC）,研究了三种编码方式的模糊函数的性能，给出了在一定幅度谱下用遗传算法对三种编码进行波形设计的方法。
第四部分工作则围绕单基地线阵EES-MIMO雷达MUSIC谱峰搜索方法展开研究。针对MIMO-MUSIC算法进行DOA估计时的谱峰搜索计算量大的问题，提出了基于一维迭代的谱峰搜索方法。详细分析了迭代的目标函数，推导出一阶和二阶导数的计算公式，提出了分三个阶段搜索的思想，并分别制定了三个阶段的搜索策略。
第五部分围绕单基地L阵列EES-MIMO雷达的MUSIC谱峰搜索问题。依据MUSIC谱函数，构造了谱峰搜索的目标函数，并对目标函数的二维寻优问题提出了两种有效的方法：遗传算法和粒子群算法。与原MUSIC谱峰搜索方法相比，其计算量和计算复杂度明显减小。
Multiple-Input Multiple-Output (MIMO) radar has attracted attention fromscholars. MIMO radar covers broad space by beaming orthogonal or independentsignals with different antennas. After the receiving end uses the matched filter toseparate the signal from each transmitter unit, it is equivalent to the synthesis of avirtual element, thus will expand the array aperture. The precision of parameterestimation and the space covered by MIMO radar are much larger than traditionalradar system and it also expands the max number of sources. Therefore, MIMO radarhas very high practical value.
To overcome the interference problem faced by MIMO radar, this paperdiscussed MIMO radar based on Electromagnetic Environmental Sensory (EES) andanalyzed the working principles of EES-MIMO.This research can be divided into fivesections: the principle and structure of the EES-MIMO radar, the electromagneticenvironment sensing method of EES-MIMO radar, the transmitted waveform design,MUSIC peaks gradient search method based on single-base EES-MIMO radar andMUSIC peaks genetic algorithm and particle swarm search method for single-base Larray EES-MIMO radar.
The first section analyzed the functions of single base transceiver MIMO radar,its structure and the virtual array elements and characteristics. In order to improve theanti-interference ability of MIMO radar, this article introduces the EES-MIMO radar,and gave detailed descriptions of its structure and functionalities, thus provided atheoretical support for following researches.
Complex array structures in commonly used noise spectrum estimationalgorithm are difficult to apply to MIMO radar’s electromagnetic environment andwill require an appropriate noise spectrum algorithm. The second section first analyzed the two radar interferences: active jamming and passive clutter. To solvenarrowband rifle-type interference, author used SVD method from AR model togenerate the EES-MIMO radar standard noise power spectrum estimation andanalyzed the order determination of cross-spectrum AR model. For point-frequencyinterference, author introduced cross spectrum decomposition method under thecolored noise background. This method improves estimation accuracy by expandingthe cross-correlation dimension and it is not only effective in suppressing theinfluence of the observation noise, but also reduces the amount of computation. Lastly,author proposed amplitude spectrum method to solve the interferences and thesimulation confirm the effectiveness.
The third section discusses the design of EES-MIMO radar transmitterwaveform. Author first discussed the concept of orthogonal waveform design,optimizing the objective function and the basic method of waveform design usinggenetic algorithms. These discussions focus on the orthogonal Discrete FrequenciesCoding Waveform (DFCW), Orthogonal Frequency Division Multiplexing (OFDM)and Multi-Carrier frequency Phase Coding (MCPC). Lastly, author used geneticalgorithms to generate three coding waveform designs for certain range spectrum.
The fourth section focused on the single-base line array of EES-MIMO radar andMUSIC spectrum peak search method. To avoid the intensive computation from DOAestimation, this article proposed a method based on one-dimensional iteration. Authoranalyzed objective function of the iteration and used first and second order derivativeto propose a three phases search method and developed a three-stage search strategy.
The fifth section focused on the search method for MUSIC peak spectrum onsingle-base L array EES-MIMO radar. This article suggested two methods: geneticalgorithms and particle swarm optimization based on two-dimensional optimizationfor MUSIC spectral function. Compared to traditional MUSIC peak spectrum searchmethod, these methods require less computation and are also less complex.
第一部分首先对单基地收发分集MIMO雷达的工作原理及系统构成、单基地收发分集MIMO雷达的虚拟阵元及特点进行了详细的阐述，为了提高MIMO雷达的抗干扰能力，提出了基于电磁环境感知的MIMO雷达，分析了该EES-MIMO雷达的工作原理及基本构成，为后续的研究工作奠定了理论基础。
复杂的阵列结构致使现在很多常用的噪声功率谱估计的算法难以应用于EES-MIMO雷达的电磁环境感知，这就需要探寻合适的噪声功率谱估计的算法。第二部分工作首先分析了雷达干扰的类型:有源干扰和无源杂波干扰，并详细地阐述了有源干扰和无源杂波干扰的表达形式及实现方式。针对窄带瞄准式干扰，将现代谱估计中AR模型参数互谱估计的SVD算法推广应用于EES-MIMO雷达的电磁环境感知的噪声功率谱估计，导出了EES-MIMO雷达标准噪声功率谱估计的互谱SVD算法。该算法具有不受EES-MIMO雷达阵列形式限制的优势。而后，基于点频式干扰，提出了有色噪声背景下正弦参数估计的互谱奇异值分解方法，该算法可以通过扩大互相关函数维数的方法来提高正弦频率估计的精度；基于扫频干扰，采用了基于二次型变换的互相关估计方法，该算法不仅有效地抑制了观测噪声的影响，而且将原本非平稳随机过程转化成为一个平稳的随机过程的参数估计问题，从而大大减少了参数估计的计算量，有效地提高了扫频信号参数估计的精度。最后，基于上述三种噪声功率谱估计的算法给出了幅度谱成型的方法。并利用仿真结果验证了有效性。
第三部分讨论了EES-MIMO雷达发射波形设计问题，首先介绍了正交波形设计的概念和优化的目标函数，讨论了用遗传算法进行波形设计的基本方法。并在此基础上讨论了正交离散频率编码（DFCW），正交频分复用（OFDM）相位编码和多载频相位编码（MCPC）,研究了三种编码方式的模糊函数的性能，给出了在一定幅度谱下用遗传算法对三种编码进行波形设计的方法。
第四部分工作则围绕单基地线阵EES-MIMO雷达MUSIC谱峰搜索方法展开研究。针对MIMO-MUSIC算法进行DOA估计时的谱峰搜索计算量大的问题，提出了基于一维迭代的谱峰搜索方法。详细分析了迭代的目标函数，推导出一阶和二阶导数的计算公式，提出了分三个阶段搜索的思想，并分别制定了三个阶段的搜索策略。
第五部分围绕单基地L阵列EES-MIMO雷达的MUSIC谱峰搜索问题。依据MUSIC谱函数，构造了谱峰搜索的目标函数，并对目标函数的二维寻优问题提出了两种有效的方法：遗传算法和粒子群算法。与原MUSIC谱峰搜索方法相比，其计算量和计算复杂度明显减小。
Multiple-Input Multiple-Output (MIMO) radar has attracted attention fromscholars. MIMO radar covers broad space by beaming orthogonal or independentsignals with different antennas. After the receiving end uses the matched filter toseparate the signal from each transmitter unit, it is equivalent to the synthesis of avirtual element, thus will expand the array aperture. The precision of parameterestimation and the space covered by MIMO radar are much larger than traditionalradar system and it also expands the max number of sources. Therefore, MIMO radarhas very high practical value.
To overcome the interference problem faced by MIMO radar, this paperdiscussed MIMO radar based on Electromagnetic Environmental Sensory (EES) andanalyzed the working principles of EES-MIMO.This research can be divided into fivesections: the principle and structure of the EES-MIMO radar, the electromagneticenvironment sensing method of EES-MIMO radar, the transmitted waveform design,MUSIC peaks gradient search method based on single-base EES-MIMO radar andMUSIC peaks genetic algorithm and particle swarm search method for single-base Larray EES-MIMO radar.
The first section analyzed the functions of single base transceiver MIMO radar,its structure and the virtual array elements and characteristics. In order to improve theanti-interference ability of MIMO radar, this article introduces the EES-MIMO radar,and gave detailed descriptions of its structure and functionalities, thus provided atheoretical support for following researches.
Complex array structures in commonly used noise spectrum estimationalgorithm are difficult to apply to MIMO radar’s electromagnetic environment andwill require an appropriate noise spectrum algorithm. The second section first analyzed the two radar interferences: active jamming and passive clutter. To solvenarrowband rifle-type interference, author used SVD method from AR model togenerate the EES-MIMO radar standard noise power spectrum estimation andanalyzed the order determination of cross-spectrum AR model. For point-frequencyinterference, author introduced cross spectrum decomposition method under thecolored noise background. This method improves estimation accuracy by expandingthe cross-correlation dimension and it is not only effective in suppressing theinfluence of the observation noise, but also reduces the amount of computation. Lastly,author proposed amplitude spectrum method to solve the interferences and thesimulation confirm the effectiveness.
The third section discusses the design of EES-MIMO radar transmitterwaveform. Author first discussed the concept of orthogonal waveform design,optimizing the objective function and the basic method of waveform design usinggenetic algorithms. These discussions focus on the orthogonal Discrete FrequenciesCoding Waveform (DFCW), Orthogonal Frequency Division Multiplexing (OFDM)and Multi-Carrier frequency Phase Coding (MCPC). Lastly, author used geneticalgorithms to generate three coding waveform designs for certain range spectrum.
The fourth section focused on the single-base line array of EES-MIMO radar andMUSIC spectrum peak search method. To avoid the intensive computation from DOAestimation, this article proposed a method based on one-dimensional iteration. Authoranalyzed objective function of the iteration and used first and second order derivativeto propose a three phases search method and developed a three-stage search strategy.
The fifth section focused on the search method for MUSIC peak spectrum onsingle-base L array EES-MIMO radar. This article suggested two methods: geneticalgorithms and particle swarm optimization based on two-dimensional optimizationfor MUSIC spectral function. Compared to traditional MUSIC peak spectrum searchmethod, these methods require less computation and are also less complex.
引文
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