基于MIMO雷达的目标恒虚警检测研究
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摘要
受MIMO通信的启发,Fishler于2004年提出了MIMO体制雷达。目前对MIMO雷达研究主要集中在两种不同形式的MIMO系统:一是基于相控阵体制的MIMO雷达,雷达单元之间的间距与相控阵雷达相同,采用波形分集来实现MIMO特性;二是基于多基地的MIMO系统,雷达单元间距足够大,以此获得空间分集增益以提高雷达的目标检测和参数估计性能。结合航空基金等课题,本文研究MIMO雷达关于目标检测方面的问题,主要包括以下内容:
在单元平均恒虚警检测器的基础上,提出了一种MIMO雷达双门限恒虚警检测器,分析了MIMO雷达双门限恒虚警检测器的性能,并和单门限恒虚警检测器进行了对比,同时给出了MIMO雷达双门限检测器在多目标环境下的检测性能。仿真结果表明在MIMO雷达中应用双门限检测具有结构简单,数据传输量低等特点,在单目标和多目标环境中,双门限恒虚警检测器的检测性能优于单门限恒虚警检测器。
在球不变随机矢量非高斯杂波背景下,研究了MIMO雷达目标的自适应检测问题。将广义似然比检验扩展到MIMO雷达的目标检测问题中,并在球不变随机矢量杂波模型中推导了检测器的形式,将其与在高斯杂波背景中推得的检测器进行比较。随后,研究了非高斯杂波中MIMO雷达的目标CFAR检测问题,分析了在K分布杂波中进行目标检测的性能。最后应用蒙特卡洛方法进行仿真并进行分析,仿真结果表明在高斯和非高斯杂波环境中,MIMO雷达广义似然比检测器能够保持较好的检测性能,而在K分布杂波环境中,MIMO雷达广义似然比检测器明显优于高斯杂波环境中的最优检测器。
基于极值理论和柯尔莫诺夫-斯米尔诺夫检验,对MIMO雷达的检测性能进行了分析。首先对极值理论和柯尔莫诺夫-斯米尔诺夫检验进行介绍,然后将极值理论用到MIMO雷达的目标检测中。本文采用的方法能够在背景杂波分布未知和多目标环境下根据回波数据自适应地确定检测门限,能够准确估计目标的个数,从而提高了MIMO雷达在多目标条件下的检测性能。
针对MIMO雷达发射正交信号形成波形分集的特点,研究了基于空时波形自适应处理的MIMO雷达的目标检测问题,将广义似然比检验的检测算法应用到MIMO雷达的空时波形自适应处理后的目标检测问题中。对回波数据进行空时波形自适应处理后,其概率统计分布的参数会发生变化,广义似然比检验可以在参数未知的高斯杂波环境中对目标进行检测,具有一定的通用性。因此本文采用广义似然比检验的检测算法并进行了相应的理论分析,并用蒙特卡洛方法来检验提出的检测器的目标检测性能。
Inspired by multiple-input multiple-output (MIMO) communications, fishler proposed the concept of MIMO radar. Currently, the researches on MIMO radar are focused on two main configurations: the first one is base on the phased-array radar with the distance between its arrays is half wavelength. It realizes the MIMO characteristic by utilizing waveform diversity; the second one is base on the multi-static radar. The space between the arrays is far enough in this type of MIMO radar, so it can get space diversity, thus can improve the performance of target detection and parameter estimate. This paper mainly researches the target detection of MIMO radar, including the following issues:
Base on the cell-average constant false alarm rate detector, we proposal a double threshold Constant False Alarm Rate (CFAR) detector. Fist, we analyze the detection performance of the double threshold CFAR detector, and then compare it to the single threshold detector, and analyze this detector’s performance in multiple targets environment last. The simulation results show that: applying double threshold detection in MIMO radar, we can improve the constant false alarm rate detection performance, especially in multiple targets environments. Besides it has the advantage of simplicity, low data transmission rate and so on.
In the non-Gaussian clutter environments, which are modeled as Spherically Invariant Random Vector (SIRV), we analyze the adaptive target detection problem on MIMO radar. After extending the generalized likelihood rate test (GLRT) to the MIMO radar detection, we derive the detector in SIRV clutter environments. In order to prove this new detector’s robustness, we compare it with the detector derived in the Gaussian clutter environments. And then researches the CFAR detection problem of MIMO radar in non-Gaussian clutter environment and analyses the target detection performance in K-distribute clutter environment. At last we make some simulations with Monte-Carlo method. The simulation results show that: the MIMO-GLTR detector maintains good detection performance in Gaussian and non-Gaussian clutter environment and performances obviously better than the optimization Gaussian detector.
Based on Extreme Value Theory and Kolmogorov-Smirnov statistical test, this paper analyses the detection performance of MIMO radar. After introducing the extreme value theory and kolmogorov-smirnov statistical test, we use this theory to detect target in MIMO radar. The method proposed by this paper can estimate the detection threshold accurately under the environments that the distribution of the backgrounds is unknown and multiple targets are presented. Besides, it can accurately estimate the targets’number, thus improves the detection performance under multiple targets environment.
In allusion to the waveform diversity in MIMO radar, we research the MIMO radar target detection problem base on space time waveform adaptive process, and then apply the generalized likelihood rate test to this problem. The parameters of the probability statistics distribute change after space time waveform adaptive process, and the generalized likelihood rate test can detect targets in the unknown Gaussian clutter environment. This paper adopts the generalized likelihood rate test detection algorithm and makes some theoretic analysis, then we checkout the detector’performance in the way of Monte-Carlo simulation.
在单元平均恒虚警检测器的基础上,提出了一种MIMO雷达双门限恒虚警检测器,分析了MIMO雷达双门限恒虚警检测器的性能,并和单门限恒虚警检测器进行了对比,同时给出了MIMO雷达双门限检测器在多目标环境下的检测性能。仿真结果表明在MIMO雷达中应用双门限检测具有结构简单,数据传输量低等特点,在单目标和多目标环境中,双门限恒虚警检测器的检测性能优于单门限恒虚警检测器。
在球不变随机矢量非高斯杂波背景下,研究了MIMO雷达目标的自适应检测问题。将广义似然比检验扩展到MIMO雷达的目标检测问题中,并在球不变随机矢量杂波模型中推导了检测器的形式,将其与在高斯杂波背景中推得的检测器进行比较。随后,研究了非高斯杂波中MIMO雷达的目标CFAR检测问题,分析了在K分布杂波中进行目标检测的性能。最后应用蒙特卡洛方法进行仿真并进行分析,仿真结果表明在高斯和非高斯杂波环境中,MIMO雷达广义似然比检测器能够保持较好的检测性能,而在K分布杂波环境中,MIMO雷达广义似然比检测器明显优于高斯杂波环境中的最优检测器。
基于极值理论和柯尔莫诺夫-斯米尔诺夫检验,对MIMO雷达的检测性能进行了分析。首先对极值理论和柯尔莫诺夫-斯米尔诺夫检验进行介绍,然后将极值理论用到MIMO雷达的目标检测中。本文采用的方法能够在背景杂波分布未知和多目标环境下根据回波数据自适应地确定检测门限,能够准确估计目标的个数,从而提高了MIMO雷达在多目标条件下的检测性能。
针对MIMO雷达发射正交信号形成波形分集的特点,研究了基于空时波形自适应处理的MIMO雷达的目标检测问题,将广义似然比检验的检测算法应用到MIMO雷达的空时波形自适应处理后的目标检测问题中。对回波数据进行空时波形自适应处理后,其概率统计分布的参数会发生变化,广义似然比检验可以在参数未知的高斯杂波环境中对目标进行检测,具有一定的通用性。因此本文采用广义似然比检验的检测算法并进行了相应的理论分析,并用蒙特卡洛方法来检验提出的检测器的目标检测性能。
Inspired by multiple-input multiple-output (MIMO) communications, fishler proposed the concept of MIMO radar. Currently, the researches on MIMO radar are focused on two main configurations: the first one is base on the phased-array radar with the distance between its arrays is half wavelength. It realizes the MIMO characteristic by utilizing waveform diversity; the second one is base on the multi-static radar. The space between the arrays is far enough in this type of MIMO radar, so it can get space diversity, thus can improve the performance of target detection and parameter estimate. This paper mainly researches the target detection of MIMO radar, including the following issues:
Base on the cell-average constant false alarm rate detector, we proposal a double threshold Constant False Alarm Rate (CFAR) detector. Fist, we analyze the detection performance of the double threshold CFAR detector, and then compare it to the single threshold detector, and analyze this detector’s performance in multiple targets environment last. The simulation results show that: applying double threshold detection in MIMO radar, we can improve the constant false alarm rate detection performance, especially in multiple targets environments. Besides it has the advantage of simplicity, low data transmission rate and so on.
In the non-Gaussian clutter environments, which are modeled as Spherically Invariant Random Vector (SIRV), we analyze the adaptive target detection problem on MIMO radar. After extending the generalized likelihood rate test (GLRT) to the MIMO radar detection, we derive the detector in SIRV clutter environments. In order to prove this new detector’s robustness, we compare it with the detector derived in the Gaussian clutter environments. And then researches the CFAR detection problem of MIMO radar in non-Gaussian clutter environment and analyses the target detection performance in K-distribute clutter environment. At last we make some simulations with Monte-Carlo method. The simulation results show that: the MIMO-GLTR detector maintains good detection performance in Gaussian and non-Gaussian clutter environment and performances obviously better than the optimization Gaussian detector.
Based on Extreme Value Theory and Kolmogorov-Smirnov statistical test, this paper analyses the detection performance of MIMO radar. After introducing the extreme value theory and kolmogorov-smirnov statistical test, we use this theory to detect target in MIMO radar. The method proposed by this paper can estimate the detection threshold accurately under the environments that the distribution of the backgrounds is unknown and multiple targets are presented. Besides, it can accurately estimate the targets’number, thus improves the detection performance under multiple targets environment.
In allusion to the waveform diversity in MIMO radar, we research the MIMO radar target detection problem base on space time waveform adaptive process, and then apply the generalized likelihood rate test to this problem. The parameters of the probability statistics distribute change after space time waveform adaptive process, and the generalized likelihood rate test can detect targets in the unknown Gaussian clutter environment. This paper adopts the generalized likelihood rate test detection algorithm and makes some theoretic analysis, then we checkout the detector’performance in the way of Monte-Carlo simulation.
引文
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[3] D. W. Bliss, K. W. Forsythe, Multiple-input multiple-output (MIMO) radar and imaging: degrees of freedom and resolution. in Proceedings of 37th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, Nov. 2003: 54-59.?
[4] Haimovich A M, Blum R S, Cimini L. MIMO radar with widely separated antennas. IEEE Signal Process. Mag.. 2008: 116-129.
[5] DAI Xi-zeng, XU Jia, PENG Ying-ning. A New Method of Improving theWeak Target Detection Performance Based on the MIMO Radar [C] IEEE Int. Conf. Radar. Shanghai, China, 2006:24-27.
[6] Li J, Stoica P. MIMO radar with colocated antennas. IEEE Signal Process. Mag.. 2007, 24(5): 106-114.
[7] Rabideau D J, Parker P. Ubiquitous MIMO multifunction digital array radar. Proc. 37th Asilomar Conference Record of the 37th Asilomar Conference on Signals, Systems and Computers. 2003: 1057-1064.
[8] Fishler E, Haimovich A, Blum R, et al.. Performance of MIMO radar systems: Advantages of angular diversity. Prco. 38th Asilomar Conf. Signals, Syst. Comput.. Pacific Grove, CA, United States, 2004: 305-309.
[9] Fishler E, Haimovich A, Blum R, et al.. Spatial diversity in radars-Models and detection performance. IEEE Trans. Signal Process.. 2006, 54 (3): 823-838.
[10] Hammad A. Khan, David J. Edwards. Doppler Problems in Orthogonal MIMO Radars. IEEE International Radar conference, Apr. 2006:24-27.
[11] H. Deng. Polyphase code design for orthogonal netted radar system. IEEE Transactions on Signal Processing, 2004, 52(11): 3126-3135.
[12] H. Deng. Discrete frequency-coding waveform design for netted radar system. IEEE Signal Processing Letters, 2004, 11(2): 179-182.
[13]杨明磊,陈伯孝,齐飞林等.多载频MIMO雷达的模糊函数[J].系统工程与电子技术, 2009年1月,?Vol.31, No.1
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[15] J. Li, L. Xu, P. Stoica, et al.. Range Compression and Waveform Optimization for MIMO Radar: A Cramér–Rao Bound Based Study. IEEE Transactions on Signal Processing, Jan. 2008, 56(1): 218-232.
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[2] Fishler E., Haimovich A., Blum, R., et al. MIMO Radar: An idea whose time has come[C]. Proceedings of the IEEE Radar Conference, April 2004: 71-78.
[3] D. W. Bliss, K. W. Forsythe, Multiple-input multiple-output (MIMO) radar and imaging: degrees of freedom and resolution. in Proceedings of 37th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, Nov. 2003: 54-59.?
[4] Haimovich A M, Blum R S, Cimini L. MIMO radar with widely separated antennas. IEEE Signal Process. Mag.. 2008: 116-129.
[5] DAI Xi-zeng, XU Jia, PENG Ying-ning. A New Method of Improving theWeak Target Detection Performance Based on the MIMO Radar [C] IEEE Int. Conf. Radar. Shanghai, China, 2006:24-27.
[6] Li J, Stoica P. MIMO radar with colocated antennas. IEEE Signal Process. Mag.. 2007, 24(5): 106-114.
[7] Rabideau D J, Parker P. Ubiquitous MIMO multifunction digital array radar. Proc. 37th Asilomar Conference Record of the 37th Asilomar Conference on Signals, Systems and Computers. 2003: 1057-1064.
[8] Fishler E, Haimovich A, Blum R, et al.. Performance of MIMO radar systems: Advantages of angular diversity. Prco. 38th Asilomar Conf. Signals, Syst. Comput.. Pacific Grove, CA, United States, 2004: 305-309.
[9] Fishler E, Haimovich A, Blum R, et al.. Spatial diversity in radars-Models and detection performance. IEEE Trans. Signal Process.. 2006, 54 (3): 823-838.
[10] Hammad A. Khan, David J. Edwards. Doppler Problems in Orthogonal MIMO Radars. IEEE International Radar conference, Apr. 2006:24-27.
[11] H. Deng. Polyphase code design for orthogonal netted radar system. IEEE Transactions on Signal Processing, 2004, 52(11): 3126-3135.
[12] H. Deng. Discrete frequency-coding waveform design for netted radar system. IEEE Signal Processing Letters, 2004, 11(2): 179-182.
[13]杨明磊,陈伯孝,齐飞林等.多载频MIMO雷达的模糊函数[J].系统工程与电子技术, 2009年1月,?Vol.31, No.1
[14] Daniel R. Fuhrmann, Geoffrey San Antonio. Transmit Beamforming for MIMO Radar Systems Using Partial Signal Correlation. Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, Pacific Grove,CA,Nov.2004,1:295-299.
[15] J. Li, L. Xu, P. Stoica, et al.. Range Compression and Waveform Optimization for MIMO Radar: A Cramér–Rao Bound Based Study. IEEE Transactions on Signal Processing, Jan. 2008, 56(1): 218-232.
[16]?曾建奎,何子述.?慢起伏目标的多输入多输出雷达检测性能分析.?电波科学学报.?2008, 23(1): 158-161.
[17] Du C, Thompson J S, Petillot Y. Predicted detection performance of MIMO radar. IEEE Signal Processing Letters. 2008(15): 83-86.
[18] Blum, R. S.; Limiting Case of a Lack of Rich Scattering Environment for MIMO Radar Diversity. IEEE Signal Processing Letters. 2009, 16(10): 901-904.
[19]戴喜增,彭应宁,汤俊.?MIMO雷达检测性能.?清华大学学报(自然科学版).2007, 47(1):88-91.
[20] Sammartino, P. Baker, C. Griffiths, H. Adaptive MIMO radar system in clutter [C]. Radar Conference, 2007 IEEE 17-20 April 2007 Page(s):276– 281
[21] Chin Y C, Frederic P, Ovarlez J P, and Lesturgie M. MIMO Radar Detection in Non-Gaussian and Heterogeneous Clutter. IEEE J. Selected Topics Signal Process.. 2010, 4(1): 115-126.
[22] N. Lehmann. Some Contributions on MIMO Radar, Ph.D. dissertation, New Jersey Institute of Technology, 2006.
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