阅读说明：本技术 一种基于散射变换的无人机目标旋翼参数估计方法 (Unmanned aerial vehicle target rotor parameter estimation method based on scattering transformation ) 是由 侯成宇 蔡松言 何佳婕 陈迪 张立宪 于 2020-06-20 设计创作，主要内容包括：本发明公开了一种基于线性调频连续波雷达的无人机旋翼参数估计方法。步骤1：初始化散射变换尺度,生成小波滤波器组；步骤2：对回波信号进行一阶散射变换；步骤3：对散射系数设置能量阈值,并进行第二阶散射变换,估计频谱带宽；步骤4：提取微多普勒分量所在的第二阶散射系数,提取周期分量进行FFT得到频谱,通过频率分量数目对旋翼数目进行判断；步骤5：若无人机旋翼数目为0,则不存在旋翼,跳转至8,否则进入6；步骤6：根据旋翼数目N和峰值间隔频率对转动频率进行估计；步骤7：结合微动频谱带宽和转动频率反推叶片尺寸；步骤8：输出旋翼参数估计结果。本发明能实现线性调频连续波雷达对无人机目标旋翼转速、数目和尺寸的估计。(The invention discloses an unmanned aerial vehicle rotor wing parameter estimation method based on a linear frequency modulation continuous wave radar. Step 1: initializing a scattering transformation scale to generate a wavelet filter bank; step 2: performing first-order scattering transformation on the echo signals; and step 3: setting an energy threshold value for the scattering coefficient, performing second-order scattering transformation, and estimating the bandwidth of the frequency spectrum; and 4, step 4: extracting a second-order scattering coefficient where the micro Doppler component is located, extracting a periodic component, performing FFT to obtain a frequency spectrum, and judging the number of rotors according to the number of frequency components; and 5: if the number of the rotor wings of the unmanned aerial vehicle is 0, the rotor wings do not exist, the unmanned aerial vehicle jumps to 8, and otherwise the unmanned aerial vehicle enters 6; step 6: estimating the rotating frequency according to the number N of the rotors and the peak interval frequency; and 7: reversely deducing the size of the blade by combining the micro-motion frequency spectrum bandwidth and the rotation frequency; and 8: and outputting the rotor wing parameter estimation result. The invention can realize the estimation of the linear frequency modulation continuous wave radar on the rotating speed, the number and the size of the target rotor of the unmanned aerial vehicle.)

1. An unmanned aerial vehicle rotor wing parameter estimation method based on a linear frequency modulation continuous wave radar is characterized by comprising the following steps: the unmanned aerial vehicle rotor wing parameter estimation method comprises the following steps:

step 1: initializing a scattering transformation scale of an echo signal of the linear frequency modulation continuous wave radar so as to generate a wavelet filter bank;

step 2: performing first-order scattering transformation on the echo signals;

and step 3: setting an energy threshold value for a scattering coefficient of a rotor component of the unmanned aerial vehicle, selecting the scattering coefficient of a target to perform second-order scattering transformation, and estimating a frequency spectrum bandwidth f according to a Carson criterion through corresponding covariant components_md；

And 4, step 4: setting an energy threshold value for a scattering coefficient of a rotor wing component of the unmanned aerial vehicle, extracting a second-order scattering coefficient where a micro Doppler component is located, extracting a periodic component, performing FFT (fast Fourier transform) to obtain a frequency spectrum, and judging the number N of the rotor wings of the unmanned aerial vehicle through the number of frequency components;

and 5: if the number N of the rotor wings of the unmanned aerial vehicle is 0, no rotor wing exists, the step 8 is skipped to, and the result is output, otherwise, the step 6 is performed;

step 6: estimation of the rotation frequency from the number N of rotors and the peak interval frequency Deltaf

And 7: combined with the bandwidth f of the micro-motion spectrum_mdAnd a rotational frequency f_rThe size L of the blade is reversely pushed,wherein λ is a transmitted signal wave;

and 8: and outputting the rotor wing parameter estimation result.

2. The drone rotor parameter estimation method according to claim 1, characterized in that: in step 2, specifically, for the time signal x, its first-order scattering transformation is,

wherein the content of the first and second substances,has a center frequency of λ_jPhi is a low-pass filter and phi is a volumeProduct operation, S₁Is the first order scattering coefficient.

3. The drone rotor parameter estimation method according to claim 1, characterized in that: the step 3 is specifically scattering coefficient S₁[λ_j](x) Including a center frequency of λ_jCorresponding high frequency information is formed by the covariant partReserving, and decomposing the signal into different frequency components when the scattering transformation is repeatedly carried out on the signal as an input signal;

with the increase of the scattering transformation order, the time width of the wavelet scale function is reduced, and the frequency resolution is higher;

for a signal, the first order scattering coefficients extract the fundamental frequency components, while the second order scattering coefficients extract the modulation features in the signal.

4. The drone rotor parameter estimation method according to claim 1, characterized in that: the step 4 is specifically that the unmanned aerial vehicle target echo difference frequency signal of the linear frequency modulation continuous wave radar is,

wherein A is₀Representing the amplitude, σ, of the signal_bodyThe radar cross-sectional area of the fuselage is represented,f₀is the carrier frequency of the transmitted signal, B is the radar frequency modulation bandwidth, and c is the speed of light; sigma_blade(i)The radar scattering cross section area of the ith rotor, N is the number of rotors, R_t＝R₀+ vt is the instantaneous distance of the target from the receiving antenna, R₀V is the initial distance and the radial movement speed; l is the blade length, omega_iIs the rotational angular velocity of the ith rotor.

5. The drone rotor parameter estimation method according to claim 4, wherein: the unmanned aerial vehicle target echo consists of a fixed frequency signal and a group of frequency modulation signals.

6. The drone rotor parameter estimation method according to claim 4, wherein: intermediate frequency is determined by the instantaneous distance R_tThe modulation frequency and bandwidth are determined by the periodically rotating rotor parameter L · cos (ω)_iT) determination.

Technical Field

The invention belongs to the technical field; in particular to an unmanned aerial vehicle target rotor wing parameter estimation method based on scattering transformation.

Background

With the increase of the scale of use of commercial drones, security problems caused by abuse of drones are receiving more and more attention. At present, a plurality of means are used for detecting the target of the unmanned aerial vehicle, compared with other methods, a radar system has the advantages of all weather, all day long and long detection distance, and a linear frequency modulation continuous wave radar replaces a pulse system radar by virtue of the advantages of simple structure, high resolution, no distance blind area and the like, and can be used as an important means for medium-short range low-altitude unmanned aerial vehicle detection. However, the unmanned aerial vehicle targets are small in size, slow in movement speed and low in flying height, and are easily mixed with birds or other clutter, so that difficulty is caused in detection and identification.

Since drones typically have one or more rotors, these rotating rotors can create a micro-doppler effect in the radar echo. The micro Doppler effect is generated by micro movement of the structure of the target, the information such as the movement state, the size and the shape of the target can be effectively reflected, the characteristic has uniqueness, and for the unmanned aerial vehicle target, the unmanned aerial vehicle target is classified and threatened by estimating the characteristic parameters such as the size, the rotating speed and the number of the rotor wing through the micro Doppler frequency.

But under the general condition, unmanned aerial vehicle's rotor length is less relatively, generally in the sub-meter level, leads to little Doppler component energy lower, and the rotational speed is relatively very fast, generally for thousands of revolutions per minute, can form great Doppler frequency broadening, causes the difficulty for parameter extraction. And the interaction between the rotor blades can generate higher harmonic components of the rotating frequency in the echo, and the estimation of the rotating frequency is interfered.

The existing method mainly adopts linear time-frequency analysis, such as short-time Fourier transform, the mutual restriction of time resolution and frequency resolution leads to the limitation of energy aggregation effect, and the estimation effect of rotation frequency is reduced.

For the above reasons, conventional non-stationary signal analysis methods are not suitable for estimation of drone rotor parameters.

Disclosure of Invention

The invention provides a method for estimating parameters of a target rotor of an unmanned aerial vehicle based on scattering transformation, which can realize the estimation of the rotating speed, the number and the size of the target rotor of the unmanned aerial vehicle by a linear frequency modulation continuous wave radar.

The invention is realized by the following technical scheme:

an unmanned aerial vehicle rotor parameter estimation method based on a linear frequency modulation continuous wave radar comprises the following steps:

step 1: initializing a scattering transformation scale of an echo signal of the linear frequency modulation continuous wave radar so as to generate a wavelet filter bank;

step 2: performing first-order scattering transformation on the echo signals;

and step 3: setting an energy threshold value for a scattering coefficient of a rotor component of the unmanned aerial vehicle, selecting the scattering coefficient of a target to perform second-order scattering transformation, and estimating a frequency spectrum bandwidth f according to a Carson criterion through corresponding covariant components_md；

And 4, step 4: setting an energy threshold value for a scattering coefficient of a rotor wing component of the unmanned aerial vehicle, extracting a second-order scattering coefficient where a micro Doppler component is located, extracting a periodic component, performing FFT (fast Fourier transform) to obtain a frequency spectrum, and judging the number N of the rotor wings of the unmanned aerial vehicle through the number of frequency components;

and 5: if the number N of the rotor wings of the unmanned aerial vehicle is 0, no rotor wing exists, the step 8 is skipped to, and the result is output, otherwise, the step 6 is performed;

step 6: estimation of the rotation frequency from the number N of rotors and the peak interval frequency Deltaf

And 7: combined with the bandwidth f of the micro-motion spectrum_mdAnd a rotational frequency f_rThe size L of the blade is reversely pushed,wherein λ is a transmitted signal wave;

and 8: and outputting the rotor wing parameter estimation result.

Further, the step 2 is specifically that, for the time signal x, its first-order scattering transform is,

wherein the content of the first and second substances,has a center frequency of λ_jPhi is a low-pass filter, phi is a convolution operation, S₁Is the first order scattering coefficient.

Further, the step 3 is to specify the scattering coefficient S₁[λ_j](x) Including a center frequency of λ_jCorresponding high frequency information is formed by the covariant partReserving, and decomposing the signal into different frequency components when the scattering transformation is repeatedly carried out on the signal as an input signal;

with the increase of the scattering transformation order, the time width of the wavelet scale function is reduced, and the frequency resolution is higher;

for a signal, the first order scattering coefficients extract the fundamental frequency components, while the second order scattering coefficients extract the modulation features in the signal.

Further, the step 4 is specifically that the target echo difference frequency signal of the unmanned aerial vehicle of the chirp continuous wave radar is,

wherein A is₀Representing the amplitude, σ, of the signal_bodyThe radar cross-sectional area of the fuselage is represented,f₀is the carrier frequency of the transmitted signal, B is the radar frequency modulation bandwidth, and c is the speed of light; sigma_blade(i)The radar scattering cross section area of the ith rotor, N is the number of rotors, R_t＝R₀+ vt is the instantaneous distance of the target from the receiving antenna, R₀V is the initial distance and the radial movement speed; l is the blade length, omega_iIs the rotational angular velocity of the ith rotor.

Further, the unmanned aerial vehicle target echo consists of a fixed frequency signal and a group of frequency modulation signals.

Further, the intermediate frequency is determined by the instantaneous distance R_tThe modulation frequency and bandwidth are determined by the periodically rotating rotor parameter L · cos (ω)_iT) determination.

The invention has the beneficial effects that:

the method effectively estimates the rotor wing parameters of the small unmanned aerial vehicle, when the signal-to-noise ratio is higher than-16 dB, the reliability of the rotor wing parameter estimation is over 90 percent, and the estimation error of the rotor wing rotation frequency and the blade length is within 10 percent.

Drawings

FIG. 1 is a flow chart of the algorithm of the present invention.

FIG. 2 is a graph showing the results of the first order scattering coefficient.

FIG. 3 is a diagram showing a second-order scattering coefficient.

Figure 4 is a schematic diagram of the spectral broadening of the micro-doppler components.

FIG. 5 is a schematic diagram of a periodic component spectrum.

FIG. 6 is a diagram showing the accuracy of parameter estimation under different SNR conditions.

Detailed Description

The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Example 1

There is a need to study methods for estimating parameters of a rotorcraft. The wavelet transformation realizes multi-resolution analysis by changing a wavelet scale function to obtain higher time-frequency resolution, on the basis, the scattering transformation keeps the local deformation stability of the signal by the iterative average of wavelet modulus operators, retains local detail information, prevents signal energy from dispersing, and is also beneficial to extracting time-varying frequency modulation information. Therefore, the estimation of the rotor parameters of the unmanned aerial vehicle can be realized through scattering transformation.

An unmanned aerial vehicle rotor parameter estimation method based on a linear frequency modulation continuous wave radar comprises the following steps:

step 1: initializing a scattering transformation scale of an echo signal of the linear frequency modulation continuous wave radar so as to generate a wavelet filter bank;

step 2: performing first-order scattering transformation on the echo signals;

and step 3: setting an energy threshold value for a scattering coefficient of a rotor component of the unmanned aerial vehicle, selecting the scattering coefficient of a target to perform second-order scattering transformation, and estimating a frequency spectrum bandwidth f according to a Carson criterion through corresponding covariant components_md；

And 4, step 4: setting an energy threshold value for a scattering coefficient of a rotor wing component of the unmanned aerial vehicle, extracting a second-order scattering coefficient where a micro Doppler component is located, extracting a periodic component, performing FFT (fast Fourier transform) to obtain a frequency spectrum, and judging the number N of the rotor wings of the unmanned aerial vehicle through the number of frequency components;

and 5: if the number N of the rotor wings of the unmanned aerial vehicle is 0, no rotor wing exists, the step 8 is skipped to, and the result is output, otherwise, the step 6 is performed;

step 6: considering that each rotor generally has two blades, according to the rotationEstimation of the rotation frequency by the number of wings N and the peak interval frequency Δ f

And 7: combined with the bandwidth f of the micro-motion spectrum_mdAnd a rotational frequency f_rThe size L of the blade is reversely pushed,wherein λ is a transmitted signal wave;

and 8: and outputting the rotor wing parameter estimation result.

Further, the step 2 is specifically that, for the time signal x, its first-order scattering transform is,

wherein the content of the first and second substances,has a center frequency of λ_jPhi is a low-pass filter, phi is a convolution operation, S₁Is the first order scattering coefficient.

Further, the step 3 is to specify the scattering coefficient S₁[λ_j](x) Including a center frequency of λ_jCorresponding high frequency information is formed by the covariant partReserving, and decomposing the signal into different frequency components when the scattering transformation is repeatedly carried out on the signal as an input signal;

with the increase of the scattering transformation order, the time width of the wavelet scale function is reduced, and the frequency resolution is higher;

for a signal, the first order scattering coefficients extract the fundamental frequency components, while the second order scattering coefficients extract the modulation features in the signal.

Further, the step 4 is specifically that the target echo difference frequency signal of the unmanned aerial vehicle of the chirp continuous wave radar is,

wherein A is₀Representing the amplitude, σ, of the signal_bodyThe radar cross-sectional area of the fuselage is represented,f₀is the carrier frequency of the transmitted signal, B is the radar frequency modulation bandwidth, and c is the speed of light; sigma_blade(i)The radar scattering cross section area of the ith rotor, N is the number of rotors, R_t＝R₀+ vt is the instantaneous distance of the target from the receiving antenna, R₀V is the initial distance and the radial movement speed; l is the blade length, omega_iIs the rotational angular velocity of the ith rotor.

Further, the unmanned aerial vehicle target echo consists of a fixed frequency signal (main body echo) and a group of frequency modulation signals (rotor echo).

Further, the intermediate frequency is determined by the instantaneous distance R_tThe modulation frequency and bandwidth are determined by the periodically rotating rotor parameter L · cos (ω)_iT) determination.

Extracting the motion state of the main body by taking the frequency generated by the position of the target main body as a central frequency in the first-order scattering coefficients, selecting corresponding first-order scattering coefficients, and performing second-order scattering transformation on the first-order scattering coefficients, wherein the micro Doppler frequency generated by the echo rotor is reflected as the change of the intensity of the second-order scattering coefficients, and the rotating frequency is directly extracted through the corresponding second-order scattering coefficients so as to estimate the rotating frequency;

covariant part U corresponding to first-order scattering coefficient₁(x,λ_j) The echo signal contains all frequency components of the echo signal, and the spectrum width is the maximum spectrum broadening f of the micro Doppler frequency_mdThus obtaining an estimation result ω of the rotational frequency_iThe size of the rotor blade can then be estimated.

Example 2

In the embodiment of the invention, the parameters of the linear frequency modulation continuous wave radar signal are set as follows:

carrier frequency	16GHz
		Bandwidth of frequency modulation	30MHz
Frequency modulation period	1ms
		Sampling frequency	2MHz

The target rotor of the unmanned aerial vehicle has a rotation frequency of 30Hz, a blade length of 12cm, and two blades per rotor. The target distance radar antenna is set to be 300m, and the movement speed is 1 m/s.

And 128 frequency modulation periods are selected for accumulation, the signal length is 0.128s, and in the process, the rotating speed of the rotors can be approximately regarded as not to have sudden change due to the short accumulation time, and when the state of the unmanned aerial vehicle is stable, the rotating speed of each rotor can be regarded as approximately the same.

S1, performing first-order scattering transformation on the echo, and obtaining a scattering coefficient of a target signal component in a fifth scale through threshold detection, wherein the result is shown in FIG. 2;

s2, performing a second-order scattering coefficient on the rotor wing doppler signal, wherein the result is shown in fig. 3, and the rotor wing doppler signal component can be obtained to be located in the second-order scattering coefficient of the 6 th scale through threshold detection;

s3, after the estimation of the rotation speed is finished, estimating the micro Doppler bandwidth through the micro motion component separation result, such asFIG. 5 shows that according to Carson's criterion, a single-sided micro-Doppler spread can be obtained with a frequency of about f_md＝2.574kHz；

S4, extracting a periodic variation result of the scattering coefficient, and performing FFT on the periodic variation result to obtain a frequency spectrum as shown in FIG. 4, wherein the number of rotors of a target can be judged to be 1 because only one frequency peak exists;

s5, judging whether the number of peak values is larger than 0, and if so, continuing to execute the operation, wherein a rotor wing exists;

s6, and the variation frequency is 62.5Hz, and the rotor rotation frequency is estimated to be f due to the two blades on the rotor_r＝31.25Hz；

S7, the wavelength of the radar signal isSubstitution intoCalculating to obtain the size of the blade of 12.29 cm;

and S8, outputting a rotor parameter estimation result.

By the method, the rotor wing parameters can be effectively estimated, and the result is consistent with the set parameters.

The results of estimating the performance of the method under the conditions of different signal-to-noise ratios and different rotor numbers through the monte carlo experiment are shown in fig. 6, the signal-to-noise ratio is set to change from-20 dB to 20dB by taking 5dB as a step length, the rotor numbers are respectively set to be 1, 2 and 4, and 400 monte carlo experiments are carried out, and as can be seen from fig. 6, the method can obtain a better estimation effect under the condition that the signal-to-noise ratio is higher than-16 dB.