CN113866739B - GLCT-GPTF-based multi-rotor target parameter estimation method - Google Patents
GLCT-GPTF-based multi-rotor target parameter estimation method Download PDFInfo
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Abstract
The invention discloses a GLCT-GPTF multi-rotor target physical parameter estimation method, and aims to solve the problems that in the rotor physical parameter estimation method based on a short-time Fourier transform time spectrogram in the prior art, the energy concentration of the time spectrogram is not high, and a time spectrogram quantization threshold needs to be preset to influence the rotor physical parameter estimation precision. The invention mainly comprises the following steps: (1) calculating a rotational speed for each rotor target; (2) Calculating the stationarity of the signals after rotation translation by utilizing generalized linear frequency modulation wavelet transformation-generalized parameterized time-frequency analysis GLCT-GPTF transformation; (3) Obtaining a transformation kernel parameter vector matched with the multi-rotor target radar echo signal by using a longhorn beetle swarm optimization algorithm; (4) calculating the blade length of the rotor. The method has the advantages of more accurately describing the time-frequency characteristics of the multi-rotor target radar echo signals and improving the rotating speed of the rotor and the estimation accuracy of the blade length.
Description
Technical Field
The invention belongs to the technical field of radars, and further relates to a multi-rotor target parameter estimation method based on generalized linear Frequency modulation wavelet transformation-generalized parameterization time-Frequency analysis GLCT-GPTF (GENERAL LINEAR CHIRPLET Transform-General Parameterized Time-Frequency) in the technical field of radar target identification. The method can be used for estimating the rotor wing physical parameters of targets such as helicopters, propeller planes and the like with a plurality of rotor wings, and the estimated rotor wing physical parameters can be used for identifying the targets with a plurality of rotor wings.
Background
The rotation of the rotor blade can generate modulation action on electromagnetic echo of the radar, the electromagnetic echo is expressed as a micro Doppler curve in a sine form in a time-frequency spectrogram of the echo, the period of the micro Doppler curve is consistent with the rotation period of the rotor, and the maximum amplitude is determined by the wavelength of the radar, the length of the rotor blade and the rotation speed of the rotor. Because the physical parameters of different types of multi-rotor targets are different, the rotor physical parameters can be estimated from radar echoes of the multi-rotor targets, and the estimated rotor physical parameters are compared with the rotor physical parameters in a rotor target knowledge base, so that the classification of the multi-rotor targets can be realized. Existing methods can be largely divided into two categories. The first method is a nonparametric method, the method mainly extracts micro Doppler broadening of the blade by image morphology processing, peak detection and other methods on the basis of a time-frequency spectrogram, a micro period estimation method is combined to obtain the rotating speed of the rotor, and finally the length of the rotor blade is estimated according to the relationship between the length of the rotor blade, the micro Doppler broadening and the rotating frequency of the rotor. The performance of the non-parametric method is limited by the resolution of the time-frequency analysis method, and the estimation accuracy of the physical parameters of the rotor wing is not high. The parameterization method converts the rotor wing physical parameter estimation problem into a sinusoidal micro Doppler curve extraction problem in a time-frequency spectrogram, such as Hough transformation, inverse radon transformation and the like, however, due to the existence of a plurality of micro Doppler curves in a multi-rotor wing target time-frequency spectrogram, the energy concentration of echoes on the time-frequency spectrogram is poor, and the rotor wing physical parameter estimation precision is reduced.
The university of electronic technology discloses a multi-rotor unmanned aerial vehicle physical parameter extraction method based on singular vectors in patent literature 'a multi-rotor unmanned aerial vehicle physical parameter extraction method based on singular vectors' (application number 202011024462.9, application publication number CN 112162273A). The method comprises the following specific steps: and carrying out wavelet decomposition on the multi-rotor unmanned aerial vehicle radar echo signals, extracting low-frequency components generated by the blade rotating parts, then carrying out short-time Fourier transform on the components to obtain a time-frequency spectrogram, and extracting physical parameters such as the rotating speed of a rotor, the length of a blade and the like from a singular vector corresponding to the maximum singular value after singular value decomposition of the time-frequency spectrogram. The method has the following defects: when the method is used for estimating the parameters, the energy concentration of the time-frequency spectrogram based on short-time Fourier transform is not high, and the accuracy of singular vector extraction and the accuracy of rotor wing physical parameter estimation are reduced.
The university of electronic technology discloses a helicopter rotor physical parameter extraction method based on a time-frequency spectrogram in a patent document (application number 201910519253.2, application publication number CN 110133600A) applied by the university of electronic technology. The method comprises the following specific steps: firstly, filtering a time-frequency spectrogram of a helicopter, carrying out three-value quantization and segmentation on the filtered time-frequency spectrogram according to two threshold values obtained empirically, weakening background noise of the time-frequency spectrogram, and improving image definition so as to accurately extract a time-frequency signal line; estimating the rotation period of the helicopter rotor by using a least square method; and finally, estimating the length of the blade through the relation between the frequency spectrum width and the length of the blade. The method has the following defects: the threshold for quantizing the spectrogram when estimating the parameters by using the method is obtained empirically, and is difficult to be predetermined for different types of multi-rotor targets, and is difficult to be directly applied to different types of multi-rotor targets in practice.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a rotor wing physical parameter estimation method based on GLCT-GPTF, which aims to solve the problems that the rotor wing physical parameter estimation accuracy is affected by low energy concentration of a time spectrogram and a preset time spectrogram quantization threshold in the rotor wing physical parameter estimation method based on a short-time Fourier transform time spectrogram in the prior art.
The idea for realizing the purpose of the invention is as follows: according to the method, the GLCT-GPTF transformation rotation translation multi-rotor target radar echo signals determined by the transformation kernel parameter vectors are utilized, and the transformation kernel parameter vectors matched with the multi-rotor target radar echo signals are obtained by using a longhorn beetle group optimization algorithm, so that the signals tend to be stable after rotation translation, and the energy concentration of a time-frequency spectrogram can be effectively improved. The invention converts the problem of parameter estimation of the rotor into the problem of maximum stability of the signals after rotation translation because the signal stability based on GLCT-GPTF conversion measures the matching degree of the conversion nuclear parameter vector and the multi-rotor target radar echo signals, the maximum Doppler frequency of the rotor in the conversion nuclear parameter vector is determined by the rotation speed of the rotor and the blade length of the rotor, and the estimation of the rotation speed and the blade length of the rotor can be directly realized through the mapping relation between the conversion nuclear parameter vector and the physical parameters of the rotor, so that the estimation of the maximum Doppler frequency is obtained without the need of quantizing through a spectrogram, and the problem that the estimation precision of the physical parameters of the rotor is influenced due to the preset quantization threshold is avoided.
The specific steps for achieving the purpose of the invention are as follows:
step 1, calculating the rotating speed of each rotor wing target:
(1a) Calculating third-order instantaneous moment of the radar echo signal at each moment and each delay time;
(1b) Calculating third-order fuzzy values of the radar echo signals at each frequency and each delay time;
(1c) The third order fuzzy entropy of the radar echo signal at each delay time is calculated as follows:
Wherein epsilon (tau) represents the third-order fuzzy entropy of the radar echo signal in the delay tau time, 0 < tau < N, N represents the total time in the radar echo signal, N is more than or equal to 2000, |represents the modulo operation, C (f, tau) represents the third-order fuzzy value of the radar echo signal in the f-th frequency, 1 < f < N, ln () represents the logarithmic operation with the natural constant e as the base;
(1d) The rotational speed of the rotor is calculated according to the following formula:
Wherein ω represents the rotational speed of the rotor, pi represents the circumferential rate, prf represents the radar echo signal pulse repetition frequency, prf is equal to or greater than 10000hz, N L represents the total number of rotor blades, S represents the sign coefficient, s=1 when N L is even, s=2 when N L is odd, t represents the delay time corresponding to the maximum value of the fuzzy entropy at all delay times;
step 2, calculating the stability of the signal after rotation translation by utilizing generalized linear frequency modulation wavelet transformation-generalized parameterized time-frequency analysis GLCT-GPTF transformation:
(2a) A transform kernel of sinusoidal form matching the multi-rotor radar echo signal is constructed according to the following formula:
Wherein κ P (t) represents the t moment, and the conversion kernel in sinusoidal form obtained based on the conversion kernel parameter vector P composed of the maximum doppler frequency of the rotor, the rotation speed of the rotor, and the initial phase of the rotor, P (1), P (2), and P (3) represent the first element, the second element, and the third element in the conversion kernel parameter vector P, respectively;
(2b) Calculating the modulus value of each pixel in the three-dimensional time-frequency spectrogram according to the following formula:
wherein S (t, f, c, P) represents the t moment, the f frequency and the c frequency, the model value of the three-dimensional time spectrum diagram obtained based on the transformation nuclear parameter vector P is more than or equal to 1 and less than or equal to 5,s (tau) represents the radar echo signal at the tau moment in the radar echo signal vector, Representing the signal rotation operator derived from transforming the kernel parameter vector P at time τ,Exp denotes an exponential operation based on a natural constant e, j denotes an imaginary symbol, κ P (τ) denotes a transformation kernel of sinusoidal form derived from a transformation kernel parameter vector P at a τ -th moment, sin denotes a sinusoidal operation,Representing the signal translation operator obtained from transforming the kernel parameter vector P at the τ -th moment,/>Kappa P (t) represents a transform kernel of sinusoidal form derived from the transform kernel parameter vector P at time t, tan represents a tangent operation;
(2c) The stationarity of the signal after rotation translation is calculated as follows:
wherein F represents the stationarity of the signals after rotation and translation, E represents the sum of the modulus values of the corresponding pixels on the two-dimensional time-frequency spectrogram S c (t, F, P) of the curve fitted by the transformation kernel, The optimal tuning frequency under the f-th frequency at the t-th moment on the three-dimensional time spectrum diagram is represented, and C represents the sum of points of which the optimal tuning frequency of the curve fitted by the transformation kernel at the corresponding pixel position on the three-dimensional time spectrum diagram is not equal to 3;
Step3, obtaining a transformation kernel parameter vector matched with the multi-rotor target radar echo signal by using a longhorn beetle swarm optimization algorithm:
Iteratively updating the maximum Doppler frequency of the rotor wing and the initial phase of the rotor wing in the transformation kernel parameter vector P by using a longicorn group optimization algorithm until the signal stability is no longer changed, and taking the transformation kernel parameter vector which corresponds to the maximum signal stability of all longicorn in the longicorn group when the iteration is ended as the transformation kernel parameter vector matched with the multi-rotor wing target radar echo signal;
And 4, calculating the blade length of the rotor wing.
Compared with the prior art, the invention has the following advantages:
Firstly, the method calculates the stationarity of the signals after rotation translation by utilizing generalized linear frequency modulation wavelet transformation-generalized parameterized time-frequency analysis GLCT-GPTF transformation, overcomes the defect of low energy concentration of a time-frequency spectrogram in the rotor wing physical parameter estimation method based on the short-time Fourier transformation time-frequency spectrogram in the prior art, and has the advantage of more accurately describing the time-frequency characteristics of the multi-rotor wing target radar echo signals.
Secondly, the method utilizes the longhorn beetle swarm optimization algorithm to obtain the transformation kernel parameter vector matched with the multi-rotor target radar echo signal, solves the problem that the estimation accuracy of rotor wing physical parameters is influenced by the fact that a time-frequency spectrogram quantization threshold is required to be preset in the prior art, and improves the estimation accuracy of the rotating speed of the rotor wing and the length of the blade.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
The specific steps of the present invention will be further described with reference to fig. 1.
And step 1, calculating the rotating speed of each rotor wing target.
The third-order moment of the radar echo signal at each instant and each delay time is calculated as follows:
c(t,τ)＝s(t)[s(t+τ)]*[s(t+τ)]*s(t+2τ)
Wherein c (t, τ) represents a t-th moment in the radar echo signal, a third-order moment of delay τ time is 1.ltoreq.t.ltoreq.N, s (t) represents a radar echo signal at a t-th moment in the radar echo signal vector, s represents the radar echo signal vector, s (t+τ) represents a radar echo signal at a t+τ -th moment in the radar echo signal vector, x represents a conjugation operation, and s (t+2τ) represents a radar echo signal at a t+2τ -th moment in the radar echo signal vector.
The third order ambiguity values for the radar echo signal at each frequency and each delay time are calculated as follows:
C(f,τ)＝∫c(t,τ)exp(-j2πft)dt
wherein C (f, τ) represents the third order ambiguity of the radar echo signal at the f-th frequency, delayed by τ time.
The third order fuzzy entropy of the radar echo signal at each delay time is calculated as follows:
wherein epsilon (tau) represents the third-order fuzzy entropy of the radar echo signal in the delay tau time, 0 < tau < N, N represents the total time in the radar echo signal, N is more than or equal to 2000, ||represents the modulo operation, C (f, tau) represents the third-order fuzzy value of the radar echo signal in the f-th frequency, 1 < f < N, ln () represents the logarithmic operation based on the natural constant e.
The rotational speed of the rotor is calculated according to the following formula:
Wherein ω represents the rotational speed of the rotor, pi represents the circumferential rate, prf represents the radar echo signal pulse repetition frequency, prf is equal to or greater than 10000hz, N L represents the total number of rotor blades, S represents the sign coefficient, s=1 when N L is even, s=2 when N L is odd, and t represents the delay time corresponding to the maximum value of the fuzzy entropy at all delay times.
And 2, calculating the stability of the signals after rotation translation by utilizing generalized linear frequency modulation wavelet transformation-generalized parameterized time-frequency analysis GLCT-GPTF transformation.
A transform kernel of sinusoidal form matching the multi-rotor radar echo signal is constructed according to the following formula:
Wherein, κ P (t) represents the t moment, the sine-form transformation kernel is obtained based on the transformation kernel parameter vector P composed of the maximum doppler frequency of the rotor, the rotation speed of the rotor and the initial phase of the rotor, and P (1), P (2) and P (3) represent the first element, the second element and the third element in the transformation kernel parameter vector P, respectively.
Calculating the modulus value of each pixel in the three-dimensional time-frequency spectrogram according to the following formula:
wherein S (t, f, c, P) represents the t moment, the f frequency and the c frequency, the model value of the three-dimensional time spectrum diagram obtained based on the transformation nuclear parameter vector P is more than or equal to 1 and less than or equal to 5,s (tau) represents the radar echo signal at the tau moment in the radar echo signal vector, Representing the signal rotation operator derived from transforming the kernel parameter vector P at time τ,Exp denotes an exponential operation based on a natural constant e, j denotes an imaginary symbol, κ P (τ) denotes a transformation kernel of sinusoidal form derived from a transformation kernel parameter vector P at a τ -th moment, sin denotes a sinusoidal operation,Representing the signal translation operator obtained from transforming the kernel parameter vector P at the τ -th moment,/>Kappa P (t) represents the sine form of the transform kernel derived from the transform kernel parameter vector P at time t, tan represents the tangent operation.
The optimal tuning frequency under the f-th frequency at the t-th moment on the three-dimensional time-frequency spectrogramIs obtained by the following formula:
Wherein, The value of c is obtained when the modulus S (t, f, c, P) of the three-dimensional time-frequency spectrum diagram at the f-th frequency takes the maximum value at the t-th time when the transformation kernel parameter P is given.
The stationarity of the signal after rotation translation is calculated as follows:
wherein F represents the stationarity of the signals after rotation and translation, E represents the sum of the modulus values of the corresponding pixels on the two-dimensional time-frequency spectrogram S c (t, F, P) of the curve fitted by the transformation kernel, And C represents the sum of points of which the optimal tuning frequency of the curve fitted by the transformation kernel at the corresponding pixel position on the three-dimensional time-frequency spectrogram is not equal to 3.
And step 3, obtaining a transformation kernel parameter vector matched with the multi-rotor target radar echo signal by using a longhorn beetle swarm optimization algorithm.
And iteratively updating the maximum Doppler frequency of the rotor wing and the initial phase of the rotor wing in the transformation kernel parameter vector P by using a longicorn group optimization algorithm until the signal stability is no longer changed, and taking the transformation kernel parameter vector corresponding to the maximum signal stability of all longicorn in the longicorn group when the iteration is ended as the transformation kernel parameter vector matched with the multi-rotor wing target radar echo signal.
The longhorn beetle swarm optimization algorithm is a combination of a particle swarm optimization algorithm and a longhorn beetle whisker algorithm, and can better avoid the problems of low stability, large operand and easy sinking into local optimum of the traditional particle swarm algorithm. Each particle in the particle swarm algorithm is replaced by a longhorn beetle for searching at the same time, and the initialization of the position and the speed of the longhorn beetle is obtained by the following formula:
Wherein X im represents the position vector of the mth longicorn in the ith iteration, i is more than 0 and less than or equal to 300,0 and m is more than or equal to 30, First element representing the position vector of the mth longhorn at the ith iteration corresponding to the maximum Doppler frequency of the rotor,/>From interval/>Is randomly selected by/>A second element representing the position vector of the mth longhorn beetle at the ith iteration,Randomly selected from the interval [0,2 pi ], V im represents the velocity vector of the mth longicorn at the ith iteration,/>Represents the speed of the first dimension of the mth longhorn at the ith iteration,/>Represents the second dimension speed of the mth longhorn at the ith iteration,/>And/>Randomly selected from the intervals [ -2,2 ].
In the iteration process, the updating rule of the position of each longhorn beetle is combined with the optimal global position of all longhorn beetles and the self optimal position of each longhorn beetle, a longhorn beetle tentacle searching mechanism is introduced, the signal stationarity corresponding to the position vectors of the tentacles at the left side and the right side of each longhorn beetle is compared in the iteration process, and the position with higher stationarity is used for updating the position of each longhorn beetle. The accuracy and the speed of the transformation kernel parameter vector estimation can be effectively improved by using the longhorn beetle swarm optimization algorithm.
Step 4, calculating the blade length of the rotor according to the following formula:
Where L represents the blade length of the rotor, λ represents the wavelength of the radar return signal, P max represents the transformation kernel parameter vector matching the multi-rotor target radar return signal, and P max (1) represents the first element in the transformation kernel parameter vector matching the multi-rotor target radar return signal.
The effects of the present invention are further described below in connection with simulation experiments.
1. Simulation experiment conditions:
The hardware platform of the simulation experiment of the invention is: the processor is Intel i7 6700k CPU, the main frequency is 4.0GHz, and the memory is 16GB.
The software platform of the simulation experiment of the invention is: windows 10 operating system and Matlab2020a.
The radar echo data used in the simulation experiment of the invention is the actual measurement radar data of high signal to noise ratio of the 'eight-in-motion' aircraft collected under the cooperation condition. An "eight-rotor" aircraft is a four-rotor propeller aircraft, with a blade length l=1.8m, a rotor rotational speed ω r =40pi rad/s, and a number of blades per rotor n=4; the radar pulse repetition frequency is 12KHz, the residence time is 1s, and the carrier frequency is 5GHz.
2. Simulation content and result analysis:
the simulation experiment of the invention adopts the method of the invention and two prior arts (RSP-CFD method, instantaneous frequency-FFT method) to estimate the rotor rotation speed and the blade length of the radar data actually measured in high signal to noise ratio of the 'eight-in-motion' aircraft.
In simulation experiments, two prior art techniques employed refer to:
The prior art RSP-CFD method is a multi-rotor unmanned aerial vehicle target rotor parameter estimation method, namely an RSP-CFD method, which is proposed by He et al in the 'small-sized rotor unmanned aerial vehicle inching feature extraction [ J ] signal processing based on the RSP-CFD method, 2021,37 (3): 399-408'.
The prior art instantaneous frequency-FFT method is a target rotor parameter estimation method of the multi-rotor unmanned aerial vehicle, which is proposed by Ma Jiao et al in the university of Chinese academy of sciences, J, 2019,36 (2): 235-243, for short, an instantaneous frequency-FFT method.
The maximum doppler frequency of the rotor, the rotational speed of the rotor, and the blade length of the rotor obtained from the simulation results of the present invention are shown in table 1. The parameter of the "maximum doppler frequency" in table 1 is the first element in the transformed kernel parameter vector matched with the multi-rotor target radar echo signal obtained by the antenna farm optimization algorithm in step 3 of the present invention. The parameters of the "rotational speed" in table 1 are the rotational speeds of the rotors estimated by the third order fuzzy entropy in step 1 of the present invention. The parameters of the "blade length" of the present invention in table 1 are obtained using the calculation formula of the blade length of the rotor in step 4 of the present invention. The parameters of the RSP-CFD method "maximum doppler frequency" in table 1 are obtained by maximum value search on the quantized RSP time-frequency spectrum of the radar echo signal. The parameters of the RSP-CFD method "rotation speed" in table 1 are obtained by maximum value search on the quantized CFD time-frequency spectrogram of the radar echo signal. The parameters of the RSP-CFD method "blade length" in table 1 are calculated using the mapping relationship between the maximum doppler frequency and the rotational speed and the blade length. The parameters of the instantaneous frequency-FFT method "maximum Doppler frequency" in Table 1 are obtained by maximum value search on the quantized short-time Fourier transform time-frequency spectrogram of the radar echo signal. The parameters of the instantaneous frequency-FFT method "rotation speed" in Table 1 are obtained by performing Fourier transform search maxima on the quantized short-time Fourier transform time-frequency spectrogram of the radar echo signal. The parameters of the instantaneous frequency-FFT method "blade length" in Table 1 are calculated using the mapping relationship between the maximum Doppler frequency and rotational speed and blade length. The parameters of the true value "rotational speed" and "blade length" in table 1 are measured by the partner instrument. The parameters of the true value "maximum Doppler frequency" in Table 1 are calculated by using the mapping relation between the maximum Doppler frequency and the rotation speed and the blade length.
Table 1 Table of results of rotor parameter estimation in accordance with the present invention and various prior art in simulation experiments
| True value | The method of the invention | RSP-CFD | Instantaneous frequency-FFT | |
| Maximum Doppler frequency (Hz) | 4484.85 | 4411.09 | 4210.20 | 4250.50 |
| Rotating speed (pi rad/s) | 40.00 | 40.00 | 39.92 | 39.96 |
| Blade length (m) | 1.80 | 1.77 | 1.69 | 1.70 |
As can be seen from Table 1, the maximum Doppler frequency, the rotational speed and the estimated value of the blade length and the true value of the rotor wing of the method are lower than those of 2 prior art methods, and the method has higher rotor wing parameter estimation accuracy.
The simulation experiment shows that: according to the method, the accurate estimation of the rotating speed of the rotor is realized by using third-order fuzzy entropy, the GLCT-GPTF transformation rotation translation multi-rotor target radar echo signals determined by the transformation kernel parameter vectors are utilized, the transformation kernel parameter vectors matched with the multi-rotor target radar echo signals are obtained by using a longhorn swarm optimization algorithm, so that the rotation translation signals tend to be stable, the energy concentration of a time-frequency spectrogram can be effectively improved, the problem of rotor parameter estimation is converted into the problem of maximum stability of the rotation translation signals based on the time-frequency spectrogram obtained by GLCT-GPTF transformation, the estimation of the maximum Doppler frequency is obtained without the need of quantizing the time-frequency spectrogram, the problem of low energy concentration of the time-frequency spectrogram and the problem of influence on the estimation precision of rotor physical parameters due to the need of presetting of the time-frequency spectrogram quantization threshold in the prior art method are very practical multi-rotor target parameter estimation methods.
Claims (4)
1. A multi-rotor target physical parameter estimation method based on GLCT-GPTF is characterized in that the rotational speed of a rotor is estimated by using third-order fuzzy entropy, and the matching degree of a transformation nuclear parameter vector and a multi-rotor target radar echo signal is measured based on the signal stability transformed by GLCT-GPTF; the estimation method comprises the following steps:
step 1, calculating the rotating speed of each rotor wing target:
(1a) Calculating third-order instantaneous moment of the radar echo signal at each moment and each delay time;
(1b) Calculating third-order fuzzy values of the radar echo signals at each frequency and each delay time;
(1c) The third order fuzzy entropy of the radar echo signal at each delay time is calculated as follows:
wherein epsilon (tau) represents the third-order fuzzy entropy of the radar echo signal in the delay tau time, 0 < tau < N, N represents the total time in the radar echo signal, N is more than or equal to 2000, |represents the modulo operation, C (f, tau) represents the third-order fuzzy value of the radar echo signal in the f-th frequency, 1 < f < N, ln () represents the logarithmic operation with the natural constant e as the base;
(1d) The rotational speed of the rotor is calculated according to the following formula:
Wherein ω represents the rotational speed of the rotor, pi represents the circumferential rate, prf represents the radar echo signal pulse repetition frequency, prf is equal to or greater than 10000hz, N L represents the total number of rotor blades, S represents the sign coefficient, s=1 when N L is even, s=2 when N L is odd, t represents the delay time corresponding to the maximum value of the fuzzy entropy at all delay times;
step 2, calculating the stability of the signal after rotation translation by utilizing generalized linear frequency modulation wavelet transformation-generalized parameterized time-frequency analysis GLCT-GPTF transformation:
(2a) A transform kernel of sinusoidal form matching the multi-rotor radar echo signal is constructed according to the following formula:
Wherein κ P (t) represents the t moment, and the conversion kernel in sinusoidal form obtained based on the conversion kernel parameter vector P composed of the maximum doppler frequency of the rotor, the rotation speed of the rotor, and the initial phase of the rotor, P (1), P (2), and P (3) represent the first element, the second element, and the third element in the conversion kernel parameter vector P, respectively;
(2b) Calculating the modulus value of each pixel in the three-dimensional time-frequency spectrogram according to the following formula:
wherein S (t, f, c, P) represents the t moment, the f frequency and the c frequency, the model value of the three-dimensional time spectrum diagram obtained based on the transformation nuclear parameter vector P is more than or equal to 1 and less than or equal to 5,s (tau) represents the radar echo signal at the tau moment in the radar echo signal vector, Representing the signal rotation operator derived from transforming the kernel parameter vector P at time τ,Exp denotes an exponential operation based on a natural constant e, j denotes an imaginary symbol, κ P (τ) denotes a transformation kernel of sinusoidal form derived from a transformation kernel parameter vector P at a τ -th moment, sin denotes a sinusoidal operation,Representing the signal translation operator obtained from transforming the kernel parameter vector P at the τ -th moment,/>Kappa P (t) represents a transform kernel of sinusoidal form derived from the transform kernel parameter vector P at time t, tan represents a tangent operation;
(2c) The stationarity of the signal after rotation translation is calculated as follows:
wherein F represents the stationarity of the signals after rotation and translation, E represents the sum of the modulus values of the corresponding pixels on the two-dimensional time-frequency spectrogram S c (t, F, P) of the curve fitted by the transformation kernel, The optimal tuning frequency under the f-th frequency at the t-th moment on the three-dimensional time spectrum diagram is represented, and C represents the sum of points of which the optimal tuning frequency of the curve fitted by the transformation kernel at the corresponding pixel position on the three-dimensional time spectrum diagram is not equal to 3;
Step3, obtaining a transformation kernel parameter vector matched with the multi-rotor target radar echo signal by using a longhorn beetle swarm optimization algorithm:
Iteratively updating the maximum Doppler frequency of the rotor wing and the initial phase of the rotor wing in the transformation kernel parameter vector P by using a longicorn group optimization algorithm until the signal stability is no longer changed, and taking the transformation kernel parameter vector which corresponds to the maximum signal stability of all longicorn in the longicorn group when the iteration is ended as the transformation kernel parameter vector matched with the multi-rotor wing target radar echo signal;
step 4, calculating the blade length of the rotor by using the following formula:
Where L represents the blade length of the rotor, λ represents the wavelength of the radar return signal, P max represents the transformation kernel parameter vector matching the multi-rotor target radar return signal, and P max (1) represents the first element in the transformation kernel parameter vector matching the multi-rotor target radar return signal.
2. The method for estimating multiple rotor target physical parameters based on GLCT-GPTF according to claim 1, wherein the third-order moment of the radar echo signal at each time and each delay time in step (1 a) is obtained by the following formula:
c(t,τ)＝s(t)[s(t+τ)]*[s(t+τ)]*s(t+2τ)
Wherein c (t, τ) represents a t-th moment in the radar echo signal, a third-order moment of delay τ time is 1.ltoreq.t.ltoreq.N, s (t) represents a radar echo signal at a t-th moment in the radar echo signal vector, s represents the radar echo signal vector, s (t+τ) represents a radar echo signal at a t+τ -th moment in the radar echo signal vector, x represents a conjugation operation, and s (t+2τ) represents a radar echo signal at a t+2τ -th moment in the radar echo signal vector.
3. The method for estimating multiple rotor target physical parameters based on GLCT-GPTF according to claim 1, wherein the third order ambiguity values of the radar echo signal at each frequency and each delay time in step (1 b) are obtained by the following equation:
C(f,τ)＝∫c(t,τ)exp(-j2πft)dt
wherein C (f, τ) represents the third order ambiguity of the radar echo signal at the f-th frequency, delayed by τ time.
4. The method for estimating multiple rotor target physical parameters based on GLCT-GPTF as defined in claim 1, wherein the optimal tuning frequency at the f-th frequency at the t-th time on the three-dimensional time-frequency spectrogram in step (2 b)Is obtained by the following formula:
Wherein, The value of c is obtained when the modulus S (t, f, c, P) of the three-dimensional time-frequency spectrum diagram at the f-th frequency takes the maximum value at the t-th time when the transformation kernel parameter P is given. /(I)
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