基于联合分布的雷达目标检测与分类方法研究
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摘要
本文主要研究强杂波或噪声背景下雷达目标检测与分类问题。由于隐身飞机/舰艇、低空/超低空突防武器和综合电子干扰在现代战争中的大规模应用，雷达需要具有在强杂波和噪声中检测和分类目标的能力。本文在雷达回波信号的联合分布（时频分布和时间-调频率分布）和特征提取方面展开研究，并将研究成果应用于海面微弱目标检测、空中机动距离扩展目标检测和空中直升机分类。
本文的研究成果可概括为以下四部分：
1，研究了海面微弱目标检测问题。分析了海面波浪的形成过程及结构特点，由此得到海杂波的非平稳特性。进而根据海杂波和目标回波在时频域的区别提出了基于时频迭代分解的海面慢速微弱目标检测方法：基于特征值分解，我们首先提出快速信号合成方法（FSSM），FSSM可以从信号的维格纳分布（WD）中更精确和快速地恢复出信号；然后，基于遮隔WD（MWD）和FSSM，提出信号迭代分解方法（IDM）；最后应用IDM将海面回波分解成许多分量，并根据各分量的时频聚集性从中找出目标回波，实现目标检测。应用实测海面回波验证该检测方法的结果表明其不仅能够高效地检测海面慢速微弱目标还能够显示目标的瞬时运动状态。
2，研究了高斯白噪声背景下空中机动距离扩展目标检测问题。基于高分辨雷达（HRR）接收到的混频器输出，我们提出了三类距离扩展目标检测方法。(1)基于两个相邻混频器输出时频分解特性的距离扩展目标检测方法：首先，基于奇异值分解我们提出一种信号合成方法，它可以从两信号的互维格纳分布（CWD）中合成两个单位能量的信号，并且将两信号的能量集中在两个奇异值上；接着，提出了两个相邻混频器输出的互S-方法（CSM）。然后，将信号合成方法应用于两相邻混频器输出的CSM，得到奇异值；最后，根据所得奇异值的聚集性实现目标检测。应用没有经过距离走动校正的雷达回波数据验证所提检测方法，结果表明所提方法的检测性能优于传统方法且适用于高速运动目标。另外，该方法具有恒虚警（CFAR）特性。(2)基于一个混频器输出的匹配信号的距离扩展目标检测方法：首先构造混频器输出的匹配信号为频率与混频器输出中目标信号的最大频率相同的正弦函数；然后定义一个混频器输出的匹配模糊函数（MAF）和改进匹配滤波器（MMF）；根据混频器输出在MAF和MMF中零多普勒/频率的聚集性提出了两种距离扩展目标检测方法，分别命名为MAF-D和MMF-D。该类检测器利用一个回波因而可以检测高速（包括平动速度和转动速度）运动目标。应用实测数据的检测结果表明该类方法的检测性能优于传统的检测器且对目标姿态不敏感。(3)基于一个混频器输出的调频率（FR）函数的距离扩展目标检测方法：首先根据三次相位函数（CPF）定义FR函数并确定应用其分析离散线性调频（LFM）信号时的FR范围；然后根据回波信号在零FR处的聚集性实现目标检测。该方法可以检测高速目标。应用于实测距离扩展目标的检测结果表明该方法优于基于HRRP的检测器。
3，针对高阶多项式相位信号的参数估计问题，我们提出了一种高分辨时间-调频率分布（TFRR），并分析了其在目标检测方面的潜在应用。我们推导出该TFRR的分析表达式，并证明了其相对于CPF具有较高的分辨率。该TFRR可以用来分析两个在时间-调频率（TFR）域非常靠近的信号。由于该TFRR是双线性变换，所以当两信号的瞬时频率（IF）信号相交或非常靠近时会受到交叉项的困扰。为了抑制交叉项，我们通过引入一个FR窗提出了平滑TFRR（STFRR）。应用STFRR分析噪声背景下的高阶多项式相位信号，结果表明STFRR具有检测目标的潜能。
4，研究了高脉冲重复频率（PRF）雷达下直升机分类问题。分析了直升机主旋翼的微多普勒调制特征，针对回波信号积累时间是否大于两“闪烁”之间间隔这两种情况，我们提出了两类直升机主旋翼参数估计方法，最终实现直升机分类。(1)针对回波信号积累时间大于两“闪烁”之间间隔这种情况我们提出两种直升机分类技术：第一种方法是匹配滤波器（MF）方法，包括时域匹配滤波器（TMF）和时频域匹配滤波器（TFMF）；第二种方法为时频遮隔模板（TFMs）方法。该类方法可以分类叶片数目不同但微多普勒参数相同的直升机。仿真结果表明它们对于直升机的姿态和微多普勒参数的估计误差都具有鲁棒性。(2)针对回波信号积累时间小于两“闪烁”之间间隔这种情况我们提出了基于最小均方误差（MMSE）的部分周期数据微多普勒参数估计方法：在MMSE准则下应用正弦信号拟合从时频分布中提取出的旋翼叶片微多普勒信号，从而估计出叶片的转速和半径。仿真和实测数据的微多普勒参数估计结果都验证了该方法的有效性与精确性。
This paper is aiming at the radar target detection and classification in strong clutteror noise. In the modern warfare, the stealthy aircrafts/warships, low altitude penetrationweapons, and electronic countermeasures have been widely used. Therefore, the radarshould have the abilities to detect and classify the target in strong clutter or noise. In thisdissertation, we study the joint distribution (including time-frequency distribution andtime-frequency rate distribution) of the radar echoes and the features extraction from thejoint distribution. Then apply the results to detect the weak target in sea clutter and themaneuvering range-spread target. Also they are used to classify the helicopter byestimating the micro-Doppler parameters of the main rotor.
The main content of this dissertation is summarized as follows.
The first part focuses on the weak target detection in the sea clutter. The derivationand the construction of the sea waves are analyzed, indicating that the sea clutter isnon-stationary. Based on the difference between the sea clutter and the target echo inthe time-frequency domain, we propose an efficient method for detecting slow-movingweak targets based on time-frequency iteration decomposition. This method consists ofthree stages: first, we present a fast signal synthesis method (FSSM) based on theeigenvalue decomposition. The FSSM can synthesize a signal faster and moreaccurately from the Wigner distribution (WD). And then, we present a signal iterationdecomposition method (IDM) from the masked WD (MWD) and the FSSM. By theIDM, the small component of a signal can be obtained, even when it is very close to alarge component in the time-frequency plane. Finally, based on the IDM and twocriterions, the detection method is proposed. The proposed method is evaluated byX-band sea echoes with a weak simulated target or a real target. Results demonstratethat it not only detects the slow-moving weak target but shows its instantaneous state.
The second part focuses on the maneuvering range-spread target in strong whiteGaussian noise. Using the mixer output received by the high resolution radar (HRR), wepropose three types of methods to detect a range-spread target.1) Based on thetime-frequency decomposition of the cross S-method (CSM) of two adjacent mixeroutputs, a range-spread target detection method is proposed. This method consists ofthree steps. First, we propose a signal synthesis method (SSM) based on the singular value decomposition. The SSM synthesizes two signals in their normalized forms fromtheir cross Wigner distribution (CWD) and concentrates their energy on two singularvalues. Second, we derive the CSM of two adjacent mixer outputs. Third, wedecompose the CSM of two adjacent mixer outputs by the SSM, thereby obtainingsingular values. The concentration of the singular values is used to detect therange-spread target. The proposed method is evaluated by the raw radar data withoutrange migration correction. Results show that it outperforms the conventional methods.In addition, we prove that the proposed method has the constant false-alarm rate (CFAR)property.2) Based on the matched signal of one mixer output, we propose two rangespread target detector. At the beginning, we derive the matched signal from the mixeroutput directly. The matched signal is a sinusoidal signal and shares the same frequencyas the largest component of the target signal. Then we define the matched ambiguityfunction (MAF) and the modified matched filter (MMF). Based on the concentration ofthe MAF and the MMF at zero Doppler or frequency, we propose two range-spreadtarget detectors: MAF-D and MMF-D. The two detectors use a single mixer output andthus have the ability to detect the target with a high translational velocity and rotationalvelocity. They are evaluated by the recorded radar data. Results show that theyoutperform the conventional detectors and are robust against the target gesture.3) Usingthe frequency rate (FR) function of one mixer output, we propose a range spread targetdetector. From the cubic phase function (CPF), we define a FR function and discuss theFR range of a discrete LFM signal. From the concentration at zero FR of the FRfunction of a mixer output, we derive the range-spread target detector. The detector hasthe ability to detect the target with a high velocity. Finally, experimental results arepresented by the recorded radar data, which show that the proposed detectoroutperforms the detectors using the high resolution range profile (HRRP).
The third part focuses on the parameters estimation of the high-order polynomialphase signal. In this part, we propose a high resolution time-frequency raterepresentation (TFRR), which is a potential way to detect a target. The analyticalformula of the TFRR is presented. And the TFRR is shown to have a narrowerfrequency rate (FR) support than the cubic phase function (CPF). Consequently, theTFRR can be used to analyze the signal with close components in the time-frequencyrate (TFR) domain. Due to the bilinear transform, the TFRR suffers the cross term whenthe instantaneous frequency (IF) functions of the components are cross or very close. Tosuppress the cross term, we propose the smoothed TFRR (STFRR) by introducing anFR window to the TFRR. In addition, the application of the STFRR in analyzing a noisy high-order polynomial phase signal is given, which indicates the potential in targetdetection.
The fourth part focuses on the helicopter classification in high pulse repetitionfrequency (PRF) radar. After analyzing the micro-Doppler of main rotor, we proposetwo types of micro Doppler parameter estimation methods due to whether the pulseaccumulative time is longer than the time interval between two successive flashes. Thenthe estimated parameters are used to classify the helicopter.1) We propose twohelicopter classification methods when the pulse accumulative time is longer than thetime interval between two successive flashes: The first one is based on the matchedfilter (MF), including the time MF (TMF) and the time-frequency MF (TFMF). Thesecond one derives from time-frequency masks (TFMs). Simulation results demonstratethat both methods have the ability to classify helicopters with the same micro-Dopplerparameters. Also, they are robust against errors of the estimated parameters and thegesture of the helicopter.2) We propose a method to estimate the blade rotational rateand radius when the pulse accumulative time is shorter than the time interval betweentwo successive flashes. Error function is constructed between a sinusoid and themicro-Doppler signal extracted from the time-frequency distribution of the echo.Solving the error function in criterion MMSE, we can obtain the micro-Dopplerparameters, i.e. rotational rate and radius. The validity and accuracy of the proposedmethod are evaluated via both synthetic and experimental data.
本文的研究成果可概括为以下四部分：
1，研究了海面微弱目标检测问题。分析了海面波浪的形成过程及结构特点，由此得到海杂波的非平稳特性。进而根据海杂波和目标回波在时频域的区别提出了基于时频迭代分解的海面慢速微弱目标检测方法：基于特征值分解，我们首先提出快速信号合成方法（FSSM），FSSM可以从信号的维格纳分布（WD）中更精确和快速地恢复出信号；然后，基于遮隔WD（MWD）和FSSM，提出信号迭代分解方法（IDM）；最后应用IDM将海面回波分解成许多分量，并根据各分量的时频聚集性从中找出目标回波，实现目标检测。应用实测海面回波验证该检测方法的结果表明其不仅能够高效地检测海面慢速微弱目标还能够显示目标的瞬时运动状态。
2，研究了高斯白噪声背景下空中机动距离扩展目标检测问题。基于高分辨雷达（HRR）接收到的混频器输出，我们提出了三类距离扩展目标检测方法。(1)基于两个相邻混频器输出时频分解特性的距离扩展目标检测方法：首先，基于奇异值分解我们提出一种信号合成方法，它可以从两信号的互维格纳分布（CWD）中合成两个单位能量的信号，并且将两信号的能量集中在两个奇异值上；接着，提出了两个相邻混频器输出的互S-方法（CSM）。然后，将信号合成方法应用于两相邻混频器输出的CSM，得到奇异值；最后，根据所得奇异值的聚集性实现目标检测。应用没有经过距离走动校正的雷达回波数据验证所提检测方法，结果表明所提方法的检测性能优于传统方法且适用于高速运动目标。另外，该方法具有恒虚警（CFAR）特性。(2)基于一个混频器输出的匹配信号的距离扩展目标检测方法：首先构造混频器输出的匹配信号为频率与混频器输出中目标信号的最大频率相同的正弦函数；然后定义一个混频器输出的匹配模糊函数（MAF）和改进匹配滤波器（MMF）；根据混频器输出在MAF和MMF中零多普勒/频率的聚集性提出了两种距离扩展目标检测方法，分别命名为MAF-D和MMF-D。该类检测器利用一个回波因而可以检测高速（包括平动速度和转动速度）运动目标。应用实测数据的检测结果表明该类方法的检测性能优于传统的检测器且对目标姿态不敏感。(3)基于一个混频器输出的调频率（FR）函数的距离扩展目标检测方法：首先根据三次相位函数（CPF）定义FR函数并确定应用其分析离散线性调频（LFM）信号时的FR范围；然后根据回波信号在零FR处的聚集性实现目标检测。该方法可以检测高速目标。应用于实测距离扩展目标的检测结果表明该方法优于基于HRRP的检测器。
3，针对高阶多项式相位信号的参数估计问题，我们提出了一种高分辨时间-调频率分布（TFRR），并分析了其在目标检测方面的潜在应用。我们推导出该TFRR的分析表达式，并证明了其相对于CPF具有较高的分辨率。该TFRR可以用来分析两个在时间-调频率（TFR）域非常靠近的信号。由于该TFRR是双线性变换，所以当两信号的瞬时频率（IF）信号相交或非常靠近时会受到交叉项的困扰。为了抑制交叉项，我们通过引入一个FR窗提出了平滑TFRR（STFRR）。应用STFRR分析噪声背景下的高阶多项式相位信号，结果表明STFRR具有检测目标的潜能。
4，研究了高脉冲重复频率（PRF）雷达下直升机分类问题。分析了直升机主旋翼的微多普勒调制特征，针对回波信号积累时间是否大于两“闪烁”之间间隔这两种情况，我们提出了两类直升机主旋翼参数估计方法，最终实现直升机分类。(1)针对回波信号积累时间大于两“闪烁”之间间隔这种情况我们提出两种直升机分类技术：第一种方法是匹配滤波器（MF）方法，包括时域匹配滤波器（TMF）和时频域匹配滤波器（TFMF）；第二种方法为时频遮隔模板（TFMs）方法。该类方法可以分类叶片数目不同但微多普勒参数相同的直升机。仿真结果表明它们对于直升机的姿态和微多普勒参数的估计误差都具有鲁棒性。(2)针对回波信号积累时间小于两“闪烁”之间间隔这种情况我们提出了基于最小均方误差（MMSE）的部分周期数据微多普勒参数估计方法：在MMSE准则下应用正弦信号拟合从时频分布中提取出的旋翼叶片微多普勒信号，从而估计出叶片的转速和半径。仿真和实测数据的微多普勒参数估计结果都验证了该方法的有效性与精确性。
This paper is aiming at the radar target detection and classification in strong clutteror noise. In the modern warfare, the stealthy aircrafts/warships, low altitude penetrationweapons, and electronic countermeasures have been widely used. Therefore, the radarshould have the abilities to detect and classify the target in strong clutter or noise. In thisdissertation, we study the joint distribution (including time-frequency distribution andtime-frequency rate distribution) of the radar echoes and the features extraction from thejoint distribution. Then apply the results to detect the weak target in sea clutter and themaneuvering range-spread target. Also they are used to classify the helicopter byestimating the micro-Doppler parameters of the main rotor.
The main content of this dissertation is summarized as follows.
The first part focuses on the weak target detection in the sea clutter. The derivationand the construction of the sea waves are analyzed, indicating that the sea clutter isnon-stationary. Based on the difference between the sea clutter and the target echo inthe time-frequency domain, we propose an efficient method for detecting slow-movingweak targets based on time-frequency iteration decomposition. This method consists ofthree stages: first, we present a fast signal synthesis method (FSSM) based on theeigenvalue decomposition. The FSSM can synthesize a signal faster and moreaccurately from the Wigner distribution (WD). And then, we present a signal iterationdecomposition method (IDM) from the masked WD (MWD) and the FSSM. By theIDM, the small component of a signal can be obtained, even when it is very close to alarge component in the time-frequency plane. Finally, based on the IDM and twocriterions, the detection method is proposed. The proposed method is evaluated byX-band sea echoes with a weak simulated target or a real target. Results demonstratethat it not only detects the slow-moving weak target but shows its instantaneous state.
The second part focuses on the maneuvering range-spread target in strong whiteGaussian noise. Using the mixer output received by the high resolution radar (HRR), wepropose three types of methods to detect a range-spread target.1) Based on thetime-frequency decomposition of the cross S-method (CSM) of two adjacent mixeroutputs, a range-spread target detection method is proposed. This method consists ofthree steps. First, we propose a signal synthesis method (SSM) based on the singular value decomposition. The SSM synthesizes two signals in their normalized forms fromtheir cross Wigner distribution (CWD) and concentrates their energy on two singularvalues. Second, we derive the CSM of two adjacent mixer outputs. Third, wedecompose the CSM of two adjacent mixer outputs by the SSM, thereby obtainingsingular values. The concentration of the singular values is used to detect therange-spread target. The proposed method is evaluated by the raw radar data withoutrange migration correction. Results show that it outperforms the conventional methods.In addition, we prove that the proposed method has the constant false-alarm rate (CFAR)property.2) Based on the matched signal of one mixer output, we propose two rangespread target detector. At the beginning, we derive the matched signal from the mixeroutput directly. The matched signal is a sinusoidal signal and shares the same frequencyas the largest component of the target signal. Then we define the matched ambiguityfunction (MAF) and the modified matched filter (MMF). Based on the concentration ofthe MAF and the MMF at zero Doppler or frequency, we propose two range-spreadtarget detectors: MAF-D and MMF-D. The two detectors use a single mixer output andthus have the ability to detect the target with a high translational velocity and rotationalvelocity. They are evaluated by the recorded radar data. Results show that theyoutperform the conventional detectors and are robust against the target gesture.3) Usingthe frequency rate (FR) function of one mixer output, we propose a range spread targetdetector. From the cubic phase function (CPF), we define a FR function and discuss theFR range of a discrete LFM signal. From the concentration at zero FR of the FRfunction of a mixer output, we derive the range-spread target detector. The detector hasthe ability to detect the target with a high velocity. Finally, experimental results arepresented by the recorded radar data, which show that the proposed detectoroutperforms the detectors using the high resolution range profile (HRRP).
The third part focuses on the parameters estimation of the high-order polynomialphase signal. In this part, we propose a high resolution time-frequency raterepresentation (TFRR), which is a potential way to detect a target. The analyticalformula of the TFRR is presented. And the TFRR is shown to have a narrowerfrequency rate (FR) support than the cubic phase function (CPF). Consequently, theTFRR can be used to analyze the signal with close components in the time-frequencyrate (TFR) domain. Due to the bilinear transform, the TFRR suffers the cross term whenthe instantaneous frequency (IF) functions of the components are cross or very close. Tosuppress the cross term, we propose the smoothed TFRR (STFRR) by introducing anFR window to the TFRR. In addition, the application of the STFRR in analyzing a noisy high-order polynomial phase signal is given, which indicates the potential in targetdetection.
The fourth part focuses on the helicopter classification in high pulse repetitionfrequency (PRF) radar. After analyzing the micro-Doppler of main rotor, we proposetwo types of micro Doppler parameter estimation methods due to whether the pulseaccumulative time is longer than the time interval between two successive flashes. Thenthe estimated parameters are used to classify the helicopter.1) We propose twohelicopter classification methods when the pulse accumulative time is longer than thetime interval between two successive flashes: The first one is based on the matchedfilter (MF), including the time MF (TMF) and the time-frequency MF (TFMF). Thesecond one derives from time-frequency masks (TFMs). Simulation results demonstratethat both methods have the ability to classify helicopters with the same micro-Dopplerparameters. Also, they are robust against errors of the estimated parameters and thegesture of the helicopter.2) We propose a method to estimate the blade rotational rateand radius when the pulse accumulative time is shorter than the time interval betweentwo successive flashes. Error function is constructed between a sinusoid and themicro-Doppler signal extracted from the time-frequency distribution of the echo.Solving the error function in criterion MMSE, we can obtain the micro-Dopplerparameters, i.e. rotational rate and radius. The validity and accuracy of the proposedmethod are evaluated via both synthetic and experimental data.
引文
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