WO2020228455A1 - 一种飞行器旋翼微动特征提取方法 - Google Patents

一种飞行器旋翼微动特征提取方法 Download PDF

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WO2020228455A1
WO2020228455A1 PCT/CN2020/084007 CN2020084007W WO2020228455A1 WO 2020228455 A1 WO2020228455 A1 WO 2020228455A1 CN 2020084007 W CN2020084007 W CN 2020084007W WO 2020228455 A1 WO2020228455 A1 WO 2020228455A1
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rotor
frequency
time
image
low
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宋春毅
谢丛霜
徐志伟
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浙江大学
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/50—Systems of measurement based on relative movement of target
    • G01S13/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/35—Details of non-pulse systems
    • G01S7/352—Receivers
    • G01S7/354—Extracting wanted echo-signals
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/35—Details of non-pulse systems
    • G01S7/352—Receivers
    • G01S7/356—Receivers involving particularities of FFT processing

Definitions

  • the invention relates to the field of radar signal processing, in particular to a method for extracting micro-movement features of a rotor with a low signal-to-noise ratio.
  • Fretting refers to any vibration, rotation and other micro movements of an object except for the translation of the center of mass.
  • the rotation of the rotor is a state of micromotion.
  • the micro-Doppler frequency reflects the change of the speed of the scattering center relative to the radar, and is affected by the physical structure and motion parameters of the micro-moving target. Therefore, extracting the micro-Doppler feature in the echo can provide a basis for the classification and recognition of the flying target.
  • the nonlinear analysis empirical mode decomposition method is used to extract the parameters of the micro-moving target
  • the orthogonal matching tracking algorithm is proposed for the micro-Doppler signal in the form of sinusoidal frequency modulation for sparse approximation to realize the estimation of the micro-moving target parameters.
  • This type of method directly decomposes the radar
  • the echo sequence has good robustness, but when the dimensionality of the estimated parameter is high, the amount of calculation is huge. Due to the time-varying nature of the micro-Doppler frequency of the target echo, the radar echo signal sequence is often processed through time-frequency analysis.
  • Hough transform method to perform multi-dimensional search in the parameter domain to extract the micro-Doppler curve for parameter estimation. Because the micro-Doppler echo energy of the blade tip is small, it is easy to be submerged in the background noise, and the accuracy of this kind of parameter estimation method is not high under the extremely low signal-to-noise ratio.
  • Another method is to detect the turning point of the target signal envelope in the frequency domain and the peak interval in the time-frequency domain to extract the rotor motion parameters. This calculation method has low complexity but poor noise immunity.
  • the present invention proposes a method for extracting the micro-movement features of a rotor with a low signal-to-noise ratio.
  • the specific technical solutions are as follows:
  • An aircraft rotor micro-movement feature extraction method characterized in that the method specifically includes the following steps:
  • L is the length of the rotor
  • R 0 is the distance between the radar and the rotation center of the rotor
  • ⁇ is the emission wavelength of the radar
  • ⁇ is the observation elevation angle of the radar
  • f r is the rotation frequency of the rotor
  • ⁇ 0 is the initial phase of the rotor
  • N is the rotor blade Number of leaves
  • n is the number of the rotor
  • t is the time
  • TF s is the low-rank part of the time-frequency graph TF
  • * is the nuclear norm
  • 1 is the first-order norm
  • ⁇ is a positive equilibrium value
  • p (x, y) represents the gray value of each pixel in TF b
  • p s (x, y) represents the gray value of each pixel in the image TF s ;
  • ⁇ 0 is the frequency resolution of the time-frequency diagram.
  • the straight line detection method is Hough detection.
  • time-frequency analysis of the rotor echo signal sequence s(t) in said S1.2 adopts the fast Fourier transform method:
  • f is the frequency
  • w( ⁇ ) is the window function with the window length W
  • the Hamming window is used by default.
  • mi is the index of the position of the window function
  • O l is the overlap length of the signal between the two analysis windows
  • k is the index of the discrete frequency.
  • the method used to solve the NP-Hard problem in S1.3 is an accelerated near-end gradient method.
  • N s t s f s is the total length of the rotor signal, and t s is the sampling time.
  • the method can effectively detect the significant area in the time-frequency diagram of the aircraft rotor echo, and can still have a high extraction accuracy of the rotor micro-motion feature under low signal-to-noise ratio, and has a certain anti-interference ability, which is beneficial to the air rotor monitoring.
  • Fig. 1 is a graph of the standard error results of the micro-motion characteristics of a single-rotor, a double-rotor, a three-rotor, and a quad-rotor estimated by the method of the present invention.
  • An aircraft rotor micro-movement feature extraction method characterized in that the method specifically includes the following steps:
  • L is the length of the rotor
  • R 0 is the distance between the radar and the rotation center of the rotor
  • ⁇ is the emission wavelength of the radar
  • ⁇ is the observation elevation angle of the radar
  • f r is the rotation frequency of the rotor
  • ⁇ 0 is the initial phase of the rotor
  • N is the rotor blade Number of leaves
  • n is the number of the rotor
  • t is the time
  • TF s is the low-rank part of the time-frequency graph TF
  • * is the nuclear norm
  • 1 is the first-order norm
  • the method used to solve the NP-Hard problem is an accelerated near-end gradient method.
  • the low-rank background noise part has been removed at this time.
  • the energy value (gray scale) of the frequency band is obviously higher than that of the background. Therefore, a reasonable selection of the threshold can separate the frequency band from the background.
  • the iterative method is a global binarization method that uses the iterative idea to select the optimal threshold T 0 .
  • p (x, y) represents the gray value of each pixel in TF b
  • p s (x, y) represents the gray value of each pixel in the image TF s ;
  • ⁇ 0 is the frequency resolution of the time-frequency diagram.
  • the electromagnetic wave can cover the entire blade, and the blade is specularly scattered to maximize the scattering intensity.
  • the echo appears in the time-frequency domain from 0 Hz to the maximum Doppler frequency deviation. With, there is "flicker" in the real-time frequency domain.
  • the micro-Doppler effect caused by the scattering point of the rotating center appears as a zero band in the time-frequency diagram.
  • the micro-Doppler effect generated by the blade tip at these moments appears as a sinusoidal envelope in the time-frequency diagram.
  • the frequency band generated is generally a straight line.
  • Hough line detection is not sensitive to line breakpoints and is relatively simple, so Hough detection is used to extract the frequency band length.
  • the rotor In the short time t, the rotor can be regarded as a hovering state, that is, the rotation speed is consistent, so in TF s , the length of the frequency band is consistent.
  • the time-frequency analysis of the rotor echo signal sequence s(t) in said S1.2 adopts a fast Fourier transform method:
  • f is the frequency
  • w( ⁇ ) is the window function with the window length W
  • the Hamming window is used by default.
  • mi is the index of the position of the window function
  • O l is the overlap length of the signal between the two analysis windows
  • k is the index of the discrete frequency.
  • M1, M2, M3, and M4 In order to verify the method of the present invention, set M1, M2, M3, and M4 to represent single-rotor, double-rotor, three-rotor and quad-rotor.
  • the blade lengths are 0.1m, 0.2m, 0.3m and 0.2m, respectively.
  • the numbers are 2, 2, 2, and 1, respectively.
  • the rotor echoes of M1, M2, M3, and M4 were simulated, and the standard error (Root Mean Squared Error, RMSE) results of four rotor models under different signal-to-noise ratios estimated by the method of the present invention were obtained.
  • RMSE Root Mean Squared Error

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Radar Systems Or Details Thereof (AREA)

Abstract

一种飞行器旋翼微动特征提取方法，将低秩分解应用于旋翼回波时频图的显著性检测，使得在低信噪比下仍能够具有较高的旋翼微动特征的提取准确率，对飞行器旋翼回波信号的最大微多普勒频偏估计具有准确性和鲁棒性，具备一定抗干扰能力，有利于空中旋翼监测。

Description

一种飞行器旋翼微动特征提取方法 技术领域
本发明涉及雷达信号处理领域，具体涉及一种低信噪比下旋翼微动特征提取方法。
背景技术
微动指的是物体除质心平动以外任何振动、转动等微运动。旋翼的旋转就是一种微动状态。微多普勒频率体现的是散射中心速度相对雷达的变化，受到微动目标物理结构、运动参数影响，因此提取回波中微多普勒特征能为飞行目标分类、识别提供依据。
获得旋翼运动回波的数据后，多种从回波信号中提取微多普勒特征的方法被提出。比如，采用非线性分析经验模式分解法提取微动目标参数，针对正弦调频形式的微多普勒信号提出正交匹配追踪算法进行稀疏逼近实现了微动目标参量的估计，该类方法直接分解雷达回波序列，具有较好的鲁棒性，但是在估计参量维数较高时，计算量巨大。由于目标回波的微多普勒频率的时变性，雷达回波信号序列常常经过时频分析再进行处理。或者采用Hough变换方法在参数域中进行多维搜索提取出微多普勒曲线进行参数估计。由于叶尖的微多普勒回波能量小，易淹没于背景噪声中，在极低信噪比下这类参数估计方法的准确度不高。还有一种方法是在频域上检测目标信号包络转折点以及时频域上峰值间隔的方法提取旋翼运动参数，此计算方法复杂度小，但抗噪性较差。
发明内容
针对现有技术的不足，本发明提出一种低信噪比下旋翼微动特征提取方法，其具体技术方案如下：
一种飞行器旋翼微动特征提取方法，其特征在于，该方法具体包括如下步骤：
S1：对旋翼的时频图进行低秩分解得到时频图TF的低秩部分TF s；
S1.1：在连续波雷达下，通过下式的旋翼的回波模型得到旋翼回波信号序列s(t)；
Figure PCTCN2020084007-appb-000001
其中，L是旋翼长度，R 0是雷达与旋翼旋转中心的距离，λ是雷达的发射波长，β是雷达的观测仰角，f r是旋翼旋转频率，φ 0是旋翼初始相位，N是旋翼桨叶数目，n为旋翼的编号， t为时间；
S1.2：对旋翼回波信号序列s(t)进行时频分析，得到时频图TF：
S1.3：通过求解式(2)的NP-Hard问题得到时频图TF的稀疏部分E:
Figure PCTCN2020084007-appb-000002
其中，TF s是时频图TF的低秩部分，||·|| *是核范数，|·| 1是一阶范数，ε是一个正的平衡值；
S1.4：用初始时频图TF减去E即可以得到经过低秩分解后的图像的低秩部分TF s
TF s＝TF-E     (3)
S2：将时频图TF的低秩部分TF s分割为二值图像TF b；
S2.1：将S1.4得到的图像的低秩部分TF s通过迭代法得到二值化的分割阈值T 0；
S2.2：将TF s的每个像素点通过下式得到二值化后的图像TF b中的每个像素点
Figure PCTCN2020084007-appb-000003
其中，p (x，y)代表是TF b中每个像素点的灰度值，p s(x，y)代表是图像TF s中每个像素点的灰度值；
S3：将二值图像TF b沿图像中心水平线分为上半部分TF t和下半部分TF d；
S4：采用直线检测方法提取TF t中的频率带长度集L t、TF d中的频率带长度集L d，通过下式得到估计旋翼微动特征最大多普勒频偏
Figure PCTCN2020084007-appb-000004
Figure PCTCN2020084007-appb-000005
其中，ω 0为时频图的频率分辨率。
进一步地，所述的直线检测方法为Hough检测。
进一步地，所述的S1.2中对旋翼回波信号序列s(t)进行时频分析采用快速傅立叶变换方法：
Figure PCTCN2020084007-appb-000006
其中，f是频率，w(·)是窗长为W的窗函数，默认使用汉明窗。m i是窗函数位置的索引，O l是两个分析窗之间信号的重叠长度，k是离散频率的索引。
进一步地，所述的S1.3中求解NP-Hard问题采用的方法为加速近端梯度法。
进一步地，所述的S1.3中ε取值为：
Figure PCTCN2020084007-appb-000007
其中，N s＝t sf s是旋翼信号的总长度，t s为采样时间。
本发明的有益效果如下：
本方法能够有效检测飞行器旋翼回波中时频图显著区域，在低信噪比下仍能够具有较高的旋翼微动特征的提取准确率，具备一定抗干扰能力，有利于空中旋翼监测。
附图说明
图1为采用本发明的方法估计的单旋翼、双旋翼、三旋翼和四旋翼的微动特征的标准误差结果图。
具体实施方式
下面根据附图和优选实施例详细描述本发明，本发明的目的和效果将变得更加明白，以下结合附图和实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。
一种飞行器旋翼微动特征提取方法，其特征在于，该方法具体包括如下步骤：
S1：对旋翼的时频图进行低秩分解得到时频图TF的低秩部分TF s；
S1.1：在连续波雷达下，通过下式的旋翼的回波模型得到旋翼回波信号序列s(t)；
Figure PCTCN2020084007-appb-000008
其中，L是旋翼长度，R 0是雷达与旋翼旋转中心的距离，λ是雷达的发射波长，β是雷达的观测仰角，f r是旋翼旋转频率，φ 0是旋翼初始相位，N是旋翼桨叶数目，n为旋翼的编号，t为时间；
S1.2：对旋翼回波信号序列s(t)进行时频分析，得到时频图TF：
S1.3：通过求解式(2)的NP-Hard问题得到时频图TF的稀疏部分E:
Figure PCTCN2020084007-appb-000009
其中，TF s是时频图TF的低秩部分，||·|| *是核范数，|·| 1是一阶范数，ε是一个正的平衡值；优选地，
Figure PCTCN2020084007-appb-000010
其中，N s＝t sf s是旋翼信号的总长度，t s为采样时间。
优选地，求解NP-Hard问题采用的方法为加速近端梯度法。
S1.4：用初始时频图TF减去E即可以得到经过低秩分解后的图像的低秩部分TF s
TF s＝TF-E
S2：将时频图TF的低秩部分TF s分割为二值图像TF b；
对于获得的低秩部分TF s，此时低秩背景噪声部分已经去除。当叶片与雷达视线垂直时，由于频率带部分能量值(灰度)明显高于背景，因此，合理选择阈值，能够将频率带从背景中分割出来。迭代法是一种全局二值化方法，采用迭代思想选取最佳阈值T 0。
S2.1：将S1.4得到的图像的低秩部分TF s通过迭代法得到二值化的分割阈值T 0；
S2.2：将TF s的每个像素点通过下式得到二值化后的图像TF b中的每个像素点
Figure PCTCN2020084007-appb-000011
其中，p (x，y)代表是TF b中每个像素点的灰度值，p s(x，y)代表是图像TF s中每个像素点的灰度值；
S3：将二值图像TF b沿图像中心水平线分为上半部分TF t和下半部分TF d；
S4：采用直线检测方法提取TF t中的频率带长度集L t、TF d中的频率带长度集L d，通过下式得到估计旋翼微动特征最大多普勒频偏
Figure PCTCN2020084007-appb-000012
Figure PCTCN2020084007-appb-000013
其中，ω 0为时频图的频率分辨率。
在雷达照射旋转叶片的场景中，当叶片与雷达视线垂直时刻，电磁波能够覆盖整个叶片，叶片发生镜面散射使散射强度最大，回波在时频域出现自0Hz到最大多普勒频偏的频率带,即时频域存在“闪烁”。在其他时刻，在其他的照射角度下，旋转中心散射点引起的微多普勒效应在时频图表现为零频带。叶尖产生的在这些时刻产生的微多普勒效应在时频图呈现为正弦曲线包络形式。
由于叶片的材质一般是均匀的，因此产生的频率带部分一般都为直线。Hough直线检测对直线断点不敏感且较为简单，因此采用Hough检测提取频率带长度。在短时t中，可以将旋翼看作悬停状态，即转速一致，因此在TF s中，频率带长度一致。
优选地，所述的S1.2中对旋翼回波信号序列s(t)进行时频分析采用快速傅立叶变换方法：
Figure PCTCN2020084007-appb-000014
其中，f是频率，w(·)是窗长为W的窗函数，默认使用汉明窗。m i是窗函数位置的索引，O l是两个分析窗之间信号的重叠长度，k是离散频率的索引。
为了验证本发明的方法的，设定M1、M2、M3和M4代表单旋翼、双旋翼、三旋翼和四旋翼，其叶片长度分别为0.1m,0.2m,0.3m和0.2m，其叶片个数分别为2、2、2和1。对M1、M2、M3和M4的旋翼回波进行了仿真，获得不同信噪比下四种旋翼模型采用本发明的方法估计得到的微动特征的标准误差(Root Mean Squared Error,RMSE)结果，如图1所示，从图中可以看出，在较低信噪比下，本方法能够达到较好的微动特征估计准确度，说明本方法具有一定抗噪性，具备一定抗干扰能力，有利于空中旋翼监测。
本领域普通技术人员可以理解，以上所述仅为发明的优选实例而已，并不用于限制发明，尽管参照前述实例对发明进行了详细的说明，对于本领域的技术人员来说，其依然可以对前述各实例记载的技术方案进行修改，或者对其中部分技术特征进行等同替换。凡在发明的精神和原则之内，所做的修改、等同替换等均应包含在发明的保护范围之内。

Claims (5)

  1. 一种飞行器旋翼微动特征提取方法，其特征在于，该方法具体包括如下步骤：
    S1：对旋翼的时频图进行低秩分解得到时频图TF的低秩部分TF s；
    S1.1：在连续波雷达下，通过下式的旋翼的回波模型得到旋翼回波信号序列s(t)；
    Figure PCTCN2020084007-appb-100001
    其中，L是旋翼长度，R 0是雷达与旋翼旋转中心的距离，λ是雷达的发射波长，β是雷达的观测仰角，f r是旋翼旋转频率，φ 0是旋翼初始相位，N是旋翼桨叶数目，n为旋翼的编号，t为时间。
    S1.2：对旋翼回波信号序列s(t)进行时频分析，得到时频图TF：
    S1.3：通过求解式(2)的NP-Hard问题得到时频图TF的稀疏部分E:
    Figure PCTCN2020084007-appb-100002
    其中，TF s是时频图TF的低秩部分，||·|| *是核范数，|·| 1是一阶范数，ε是一个正的平衡值；
    S1.4：用初始时频图TF减去E即可以得到经过低秩分解后的图像的低秩部分TF s
    TF s＝TF-E    (3)
    S2：将时频图TF的低秩部分TF s分割为二值图像TF b；
    S2.1：将S1.4得到的图像的低秩部分TF s通过迭代法得到二值化的分割阈值T 0；
    S2.2：将TF s的每个像素点通过下式得到二值化后的图像TF b中的每个像素点
    Figure PCTCN2020084007-appb-100003
    其中，p(x，y)代表是TF b中每个像素点的灰度值，p s(x，y)代表是图像TF s中每个像素点的灰度值；
    S3：将二值图像TF b沿图像中心水平线分为上半部分TF t和下半部分TF d；
    S4：采用直线检测方法提取TF t中的频率带长度集L t、TF d中的频率带长度集L d，通过下式得到估计旋翼微动特征最大多普勒频偏
    Figure PCTCN2020084007-appb-100004
    Figure PCTCN2020084007-appb-100005
    其中，ω 0为时频图的频率分辨率。
  2. 根据权利要求1所述的飞行器旋翼微动特征提取方法，其特征在于，所述的直线检测方法为Hough检测。
  3. 根据权利要求1所述的飞行器旋翼微动特征提取方法，其特征在于，所述的S1.2中对旋翼回波信号序列s(t)进行时频分析采用快速傅立叶变换方法：
    Figure PCTCN2020084007-appb-100006
    其中，f是频率，w(·)是窗长为W的窗函数，默认使用汉明窗。m i是窗函数位置的索引，O l是两个分析窗之间信号的重叠长度，k是离散频率的索引。
  4. 根据权利要求1所述的飞行器旋翼微动特征提取方法，其特征在于，所述的S1.3中求解NP-Hard问题采用的方法为加速近端梯度法。
  5. 根据权利要求1所述的飞行器旋翼微动特征提取方法，其特征在于，所述的S1.3中ε取值为：
    Figure PCTCN2020084007-appb-100007
    其中，N s＝t sf s是旋翼信号的总长度，t s为采样时间。
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