Unmanned aerial vehicle rotor parameter decoupling estimation method based on omp algorithm

Abstract

The application discloses a rotor parameter decoupling estimation method for an unmanned aerial vehicle based on an OMP algorithm, and the method comprises the following steps: obtaining a rotor unmanned aerial vehicle radar echo, and completing signal sampling, pulse compression and other pretreatments; constructing an echo matrix and performing motion compensation and filtering on a rigid body part; estimating a target rotor rotating speed according to the jet engine modulation characteristics of the rotor echo; constructing a phase matching item and performing initial phase estimation on a target rotor blade; designing an overcomplete dictionary based on the phase matching item, and utilizing the OMP algorithm to reconstruct the sparse distribution of a target rotor scattering point. The application can realize accurate estimation on the rotating speed, the number and the length of the target rotor blade under the condition of echo azimuth under-sampling, realizes parameter decoupling, effectively reduces the calculation complexity, reduces the algorithm running time, and makes up for the shortcomings of the prior art.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of radar imaging data processing, and particularly relates to a rotor parameter decoupling estimation method for an unmanned aerial vehicle based on an OMP algorithm. BACKGROUND

[0002] Because of low prices and good practicability, unmanned aerial vehicles are widely used in various fields. However, unmanned aerial vehicles also bring serious safety problems while bringing convenience. In the existing low-altitude detection technology, an inverse synthetic aperture radar (ISAR) can reconstruct a radar image of a non-cooperative moving target, and shows great application potential in target detection and identification.

[0003] Traditional ISAR imaging methods usually assume that the target is rigid. However, common small consumer drones are usually driven by rotors, whose radar echoes exhibit a special sinusoidal phase modulation, known as the micro-Doppler (m-D) effect. The m-D effect can degrade the quality of ISAR images, but on the other hand, the m-D characteristics of the target can be used to extract the parameters of the drone rotors, such as the rotation speed, the number of blades, and the rotor size. In the past few decades, target m-D feature analysis has been a major challenge in ISAR research. Since the target m-D frequency is time-varying, early studies used time-frequency analysis methods to extract m-D features. Chen et al. established a signal model for the m-D part and used time-frequency analysis such as short-time Fourier transform and Wigner-Ville distribution to achieve accurate parameter estimation (see reference: Chen V C. Analysis of radar micro-Doppler with time-frequency transform [C]. Proceedings of the Tenth IEEE Workshop on Statistical Signal and Array Processing. IEEE, 2000:463-466.). The time-frequency analysis-based method is simple in structure, but the parameter estimation accuracy is limited due to the trade-off between time resolution and frequency resolution. Similarly, spectral analysis methods have also been used to study the m-D effect (see reference: Ren X, Jahangir M, White D, et al. Estimating physical parameters from multi-rotor drone spectrograms [C]. International Conference on Radar Systems (RADAR 2022). IET, 2022:20-25.). It should be noted that when the radar repetition frequency is low and aliasing occurs in the Doppler domain, neither time-frequency analysis nor spectral analysis is applicable. Another type of m-D analysis method can be summarized as the parametric method. The parametric method uses a parameterized basis function to fit the echo to represent the m-D feature. The radar echo of the target rotor exhibits a sinusoidal phase modulation signal feature, and its instantaneous frequency exhibits a sinusoidal change.Therefore, the Radon transform method with curve detection capability can be used to reconstruct the ISAR image of the rotor to some extent, and the complex-valued inverse Radon transform (CIRT) can significantly improve the resolution of the reconstructed image due to the use of coherent accumulation (see Bai X, Zhou F, Xing M, et al. High Resolution ISAR Imaging of Targets with Rotating Parts [J]. IEEE Transactions on Aerospace & Electronic Systems, 2011, 47(4): 2530-2543.). For common low-altitude detection radars, the distance resolution is limited, and the pulse repetition frequency (PRF) is usually low. The echoes of the rotor of the unmanned aerial vehicle cannot be distinguished in the range dimension, and serious aliasing usually occurs in the Doppler dimension. Therefore, the method based on the real Radon transform cannot detect the sinusoidal curve in the ranging profile, and the CIRT method cannot effectively realize coherent integration. Similarly, the Hough transform method (see Zhang Q, Yeo T S, Tan H S, et al. Imaging of a Moving Target With Rotating Parts Based on the Hough Transform [J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(1): 291-299.) is also a parameterized rotor image reconstruction method. The method estimates the mapping of the rotor scattering point in the parameter plane by performing four-dimensional parameter search in the spectrum, and inevitably faces huge calculation burden. In summary, the existing parameterized methods have high accuracy in m-D feature estimation, but often accompanied by large calculation amount. In addition, under the condition of azimuth under-sampling of low PRF echoes, the performance of these algorithms will be significantly reduced.

[0004] In general, the research on m-D feature analysis, especially the rotor analysis, still faces many difficulties. The existing methods usually require sufficient azimuth sampling rate, and have problems such as lack of prior knowledge utilization and large calculation amount, which need to be further improved. SUMMARY

[0005] The purpose of the present application is to provide an unmanned aerial vehicle rotor parameter decoupling estimation method based on OMP algorithm.

[0006] The technical scheme for achieving the object of the application is a rotor parameter decoupling estimation method for unmanned aerial vehicles based on an OMP algorithm, comprising the following steps:

[0007] Step 1: obtaining rotor unmanned aerial vehicle radar echo, completing data preprocessing including signal sampling and pulse compression;

[0008] Step 2: combining a rotor component echo signal model, using the target radar echo in step 1, constructing a target radar echo matrix in units of a frame of data, and performing motion compensation and filtering based on the echo of the rigid body part;

[0009] Step 3: performing fast Fourier transform on the distance unit where the target rotor is located to obtain a frequency spectrum, extracting the periodicity of the line spectrum of the frequency spectrum based on the JEM theory, and estimating the possible value of the target rotor speed;

[0010] Step 4: based on the rotor speed estimated in step 3, combining the rotor component echo signal model to construct a sinusoidal phase matching item Where t m represents slow time, l, ω m , θ0 respectively represent the blade length, speed and initial phase angle of the rotor in the coherent accumulation time, f c is the center frequency of the transmitted signal, and c is the electromagnetic wave propagation speed;

[0011] Step 5: multiplying the echo of the distance unit where the target rotor is located by the phase matching item and then performing fast Fourier transform, and calculating the maximum value A max of the frequency spectrum amplitude near zero Doppler; drawing the trend of A max with the change of the initial phase search direction, estimating the initial phase of the target rotor blade according to the trend of A max and combining the blade number assumption, and determining the correct target rotor speed;

[0012] Step 6: based on the estimated value of the target rotor speed and the initial phase of the blade, performing blade scattering point model reconstruction based on the OMP algorithm to realize blade length estimation; inputting the target rotor echo observation vector h m ; inputting the overcomplete dictionary D = [e sin (t m ; Δl), e sin (t m ; 2Δl), …, e sin (t m ; MΔl)], each column atom d m represents a phase matching item of different scales, M represents the number of atoms in the dictionary, Δl represents the step size, e sin is the phase matching item; inputting a random undersampling matrix S; initializing the residual r0 = h m, projection matrix F0=0, subspace index set maximum iteration number K and energy threshold ε0, wherein the energy threshold ε0 is used to measure the noise degree, and is calculated by a non-blade region in a parameter plane; let iteration number t=1;

[0013] Step 7: find an atom d t-1 with the largest inner product with the residual r m and its index m, i.e. calculate and eliminate the atom d m from the dictionary D, then update the projection matrix F t =F t-1 ∪d m and the subspace index set Λ t =Λ t-1 ∪m;

[0014] Step 8: update the residual and let iteration number t=t+1;

[0015] Step 9: judge the iteration termination condition, if t≥K or ||r t ||<ε0, then jump to Step 10; otherwise, jump to Step 7;

[0016] Step 10: reconstruct the target rotor scattering point distribution function x by using F and Λ t , and complete the estimation of the target rotor blade length.

[0017] Further, on the basis of obtaining the target rotor blade rotating speed and initial phase estimation value in Step 6, the blade scattering point model reconstruction based on the OMP algorithm is performed; the target rotor echo is represented as:

[0018] h m =SDx+n

[0019] wherein x is the distribution function of the scattering points on the blade, and n is the noise input target rotor echo observation vector; then the reconstruction of the rotor sparse scattering points is represented as:

[0020] min||x||0 s.t.||h m -SDx||2<ε

[0021] wherein ||·||0 and ||·||2 represent the L0 norm and the L2 norm respectively, and ε represents the residual.

[0022] An electronic device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the above method when executing the program.

[0023] A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the above method.

[0024] A computer program product comprising a computer program which, when executed by a processor, implements the steps of the above method.

[0025] Compared with the prior art, the present application has the following advantages:

[0026] (1) A parameter phase matching term is introduced to complete the estimation of the rotor parameters. In addition, a parameter decoupling strategy is designed to fully utilize the prior knowledge of the rotor to solve the problem, simplifying the multi-dimensional parameter search problem to a one-dimensional parameter estimation problem. Compared with existing methods, the method proposed in the present application has low computational complexity and short running time.

[0027] (2) The present application can realize accurate estimation of rotor parameters under the condition of azimuth under-sampling. With the help of compressed sensing theory, an over-complete dictionary based on phase matching term is designed, and the blade length estimation is converted into a sparse reconstruction problem, and the OMP algorithm is used to effectively estimate the rotor blade length.

[0028] (3) Experiments show that the rotor parameter estimation method proposed in the present application has strong robustness to noise, and the estimation accuracy of rotor parameters is high enough. Compared with the traditional exhaustive algorithm (reference literature: Zhang Q, Yeo T S, Tan H S, et al. Imaging of a Moving Target With Rotating Parts Based on the Hough Transform [J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(1): 291-299; Ji G, Song C, Huo H. Detection and identification of low-slow-small rotor unmanned aerial vehicle using micro-Doppler information [J]. IEEE Access, 2021, 9: 99995-100008.), the running time of the algorithm can be saved by about 70%. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 The flow chart of the unmanned aerial vehicle rotor parameter decoupling estimation method based on the OMP algorithm of the present application.

[0030] Figure 2 The target ISAR observation schematic diagram in Example 1.

[0031] Figure 3 Target Range-Doppler image of measured data in Example 1.

[0032] Figure 4 JEM characteristics of measured target rotor echo in Example 1.

[0033] Figure 5 A max value curve of the measured data in Example 1 under the assumption of two-blade. value curve.

[0034] Figure 6 A max value curve of the measured data in Example 1 under the assumption of three-blade. value curve.

[0035] Figure 7 Reconstruction of rotor scattering point distribution function based on OMP algorithm for measured data in Example 1. DETAILED DESCRIPTION

[0036] The application will be further described below in conjunction with the accompanying drawings of the specification.

[0037] Small rotor unmanned aerial vehicles have the characteristics of low flight height, slow flight speed, and small radar reflection cross-sectional area, which is a challenge to traditional radar detection. The rotor is an important feature of the rotor unmanned aerial vehicle, and estimating the rotor parameters of the unmanned aerial vehicle is an important means for identifying and classifying threats. The rotation of the unmanned aerial vehicle rotor will cause the radar echo to have a micro-Doppler effect, and by analyzing the micro-Doppler characteristics of the echo, the rotor parameters of the unmanned aerial vehicle can be estimated. As an effective method for analyzing micro-Doppler characteristics, the parameter method can accurately estimate the rotor parameters, but the existing method has a large amount of calculation, and the performance decreases or even fails under the condition of azimuth undersampling of the echo. The present application can quickly and accurately estimate the rotor parameters of small unmanned aerial vehicles under the condition of azimuth undersampling of the echo. The present application introduces a parameter phase matching term to complete the estimation of the rotor parameters, and designs a parameter decoupling strategy to simplify the multi-dimensional parameter search problem into a series of one-dimensional parameter estimation problems, effectively reducing the amount of calculation. With the help of compressed sensing theory, the present application designs an over-complete dictionary based on the phase matching term, and converts the blade length estimation into a sparse reconstruction problem of the rotor scattering point distribution, and effectively estimates the rotor blade length by using the Orthogonal Matching Pursuit (OMP) algorithm.

[0038] In combination with Figure 1 , a rotor parameter decoupling estimation method based on the OMP algorithm, comprising the following steps:

[0039] Step 1: Obtain the echo signal of the rotor unmanned aerial vehicle target by radar, and complete the signal sampling and pulse compression and other pretreatment work. Model the rotor echo signal of the target, and establish an O-XY plane coordinate system on the target projection plane, wherein the origin O represents the rotation center of the rigid body, and the rotor blade rotation center is Q(x q ,y q ). The distance R m (t m,P ) from the rotor scattering point P to the radar at time t m is:

[0040] R m,P (t m )≈R0(t m )+x q θ(t m )+y q +l p cos(ω m t m -θ 0,P )

[0041] wherein l p =L p cosβ represents the distance between P and Q on the target projection plane, L p is the distance between P and Q on the blade, β is the elevation angle of the target relative to the radar; R0(t m ) is the distance between the radar and the origin O, θ(t m ) = ω r t m represents the instantaneous rotation angle of the target rigid body part at time t m , ω r is the equivalent rotation speed of the target rigid body, ω m is the rotor speed, and θ 0,P is the rotation initial phase of the scattering point P.

[0042] Step 2: Construct the radar echo matrix by taking the target echo signal obtained in step 1 as a unit. The translational compensation rotor echo satisfies the following formula:

[0043]

[0044] wherein t m is the slow time, τ is the fast time, the rotor part is composed of K scattering points, σ m,k is the echo amplitude of the kth scattering point, B is the bandwidth of the transmitted signal, f c is the center frequency of the transmitted signal, and c is the electromagnetic wave propagation speed. Based on the echo of the rigid body part, motion compensation and filtering are performed, and the phase term can be compensated by estimating the Doppler frequency of the target rigid body part.

[0045] Step 3: Based on the echo matrix from Step 2, perform an azimuth Fourier transform to obtain the range-Doppler image of the target echo. Locate the range cell containing the rotor by broadening the spectral distribution of the range-Doppler image in the azimuth direction. Select the echo from the range cell containing the target rotor, perform a Fourier transform, and according to JEM (Jet Engine Modulation) theory, select spectral lines exhibiting periodic distribution. Estimate the rotor blade speed of the target rotor based on the periodicity of these spectral lines. in f is an estimated value of the rotor blade speed. T Let α be the periodic line interval, and N be the number of propeller blades. When N is even, α = 1; when N is odd, α = 2. Since the number of propeller blades of the UAV under test is often unknown, ω... m It is estimated that there will be multiple possible outcomes, and ω needs to be further determined. m The actual estimated value.

[0046] Step 4: Based on the target rotor speed parameters estimated in Step 3, construct a sinusoidal phase-matching term. It satisfies:

[0047]

[0048] in This represents an estimate of the initial phase of the target rotor blades. λ represents the estimated length of the target rotor blade, and λ is the wavelength of the radar transmitted signal.

[0049] Step 5: Based on the phase matching term in Step 4, let For a smaller value, such as Where Δr is the length of a distance cell; let In the range [0, 2π), increment by step. Uniformly selected values, such as At this point, the phase matching term can be expressed as: Phase matching and fast Fourier transform (HFT) are performed on the rotor echo signal. m,n (f) = FFT[h m (t m )⊙e sin,n (t m Finally, near the zero Doppler frequency, such as in the range of [-0.2PRF, 0.2PRF], the spectrum H is calculated and recorded. m,n The maximum amplitude of (f), i.e., Amax = max(|H m,n (f)|), f∈[-0.2PRF,0.2PRF]. Traverse within [0,2π). Repeat the above calculations and plot A.max The change curve, the peak of the curve corresponds to This is the estimated value of the initial phase of the rotor blades. Furthermore, due to the rotational speed ω in step 3... m The estimation has multiple possible results, and in step 5, ω will be... m All possible results are calculated using the phase-matching method described above. When ω m When the estimated value is close to the true value, A max The trend of change will show the same peak as the number of blades; conversely, when ω m When the estimated value deviates from the true value, A max The changing trend will exhibit multiple disordered peaks, contradicting the assumption of the number of blades. Therefore, the correct ω can be determined. m The estimated value and number of blades are used to resolve the issue of fuzzy rotation speed.

[0050] Step 6: Based on the obtained target rotor blade rotational speed and initial phase estimates, perform blade scattering point model reconstruction using the OMP algorithm. The target rotor echo can be represented as:

[0051] h m =SDx+n

[0052] Among them, h m Here, S is the observed signal vector, x is the random undersampling matrix, x is the distribution function of the scattering points on the blade, n is the noise input target rotor echo observation vector, and D represents the overcomplete dictionary, whose columns are originally d m The sub-terms are phase-matching terms at different scales, i.e., D = [e sin (t m ;Δl),e sin (t m ;2Δl),…,e sin (t m ;MΔl)]. Where, e sin Let M be the phase-matching term, M represent the number of atoms in the dictionary, and Δl represent the step size, taken as Δl = 0.005m. Then the reconstruction of the rotor's rarefaction scattering points can be expressed as:

[0053] min||x||0 st||h m -SDx||2<ε

[0054] Where ||·||0 and ||·||2 represent the L0 norm and L2 norm, respectively, and ε represents the residual.

[0055] Input target rotor echo observation vector h m 1. A complete dictionary D and a random undersampling matrix S; initialize the residual r0 = h m Projection matrix F0 = 0, subspace index The maximum number of iterations K and the energy threshold ε0 are defined. K is related to the sparsity of the scene and can be obtained through radar wavelength and prior knowledge of the target; the energy threshold ε0 can be calculated from the region in the parameter plane that does not contain the propeller blades; let the number of iterations t = 1.

[0056] Step 7: Find the dictionary D containing the remainder r. t-1 The atom with the largest inner product d m and its index m, i.e., calculation And remove the atom d from dictionary D. m Then update the projection matrix F t =F t-1 ∪d m and subspace index set Λ t =Λ t-1 ∪m;

[0057] Step 8: Update residuals Let the iteration number t = t + 1;

[0058] Step 9: Determine the iteration termination condition if t≥K or ||r t If ||<ε0, then jump to step 10; otherwise, jump to step 7.

[0059] Step 10: Utilize and Λ t Reconstruct the target rotor scattering point distribution function x to estimate the length of the target rotor blades.

[0060] The present invention will now be described in detail with reference to the embodiments and accompanying drawings.

[0061] Example

[0062] Combination Figure 1 A method for decoupling and estimating UAV rotor parameters based on the OMP algorithm includes the following steps:

[0063] Step 1: Acquire the echo signal of the rotary-wing UAV target using radar, and complete preprocessing work including signal sampling and pulse compression. The ISAR observation of the target is as follows: Figure 2 As shown, the rotor echo signal of the target is modeled. An O-XY plane coordinate system is established on the target projection plane, where the origin O represents the rotation center of the rigid body. Let the rotation center of the rotor blade be Q(x q ,y q ). Then t m The distance R from the rotor scattering point P to the radar at any given moment m,P (t m )for:

[0064] R m,P (t m )≈R0(tm )+x q θ(t m )+y q +l p cos(ω m t m -θ 0,P )

[0065] where l p =L p cosβ represents the distance of P and Q on the target projection plane, L p is the distance of P and Q on the blade, β is the elevation angle of the target relative to the radar; R0(t m ) is the distance between the radar and the origin O, θ(t m ) = ω r t m represents the instantaneous rotation angle of the target rigid body part at t m , ω r is the equivalent rotation speed of the target rigid body, ω m is the rotor speed, and θ 0,P is the initial phase of rotation of the scattering point P.

[0066] The radar used in this embodiment is of Frequency Modulated Continuous Wave (FMCW) system, and the signal form of the transmitted waveform is a frequency-modulated continuous sawtooth wave, with an initial frequency of 77 GHz, i.e. f c is 77 GHz; the effective bandwidth is 2.8 GHz, the distance unit length is 0.0625 m, the PRF is 10 KHz, and one frame of data contains 2048 chirp signals; the unmanned aerial vehicle used is a DJI mini2 small unmanned aerial vehicle, and the rotor diameter thereof is about 0.12 m, so the rotor blade echo accounts for about 2 distance units.

[0067] Step 2: The target echo signal obtained in step 1 is constructed into a radar echo matrix in units of one frame, and the translational compensation rotor echo satisfies the following formula:

[0068]

[0069] where t m is the slow time, τ is the fast time, the rotor part is composed of K scattering points, σ m,k is the echo amplitude of the kth scattering point, B is the bandwidth of the transmitted signal, f c is the center frequency of the transmitted signal, and c is the electromagnetic wave propagation speed. The phase term can be compensated by estimating the Doppler frequency of the target rigid body part.

[0070] Step 3: The distance-Doppler image of the echo matrix from Step 2 is as follows. Figure 3 As shown, the spectral broadening indicates that the target rotor portion is located in range cells 208-209 and 211-212. Taking range cell 208 as an example, a Fast Fourier Transform (FFT) is performed on the echo from this range cell, and the result is as follows. Figure 4 As shown; by selecting spectral lines exhibiting a periodic distribution in the spectrum and combining them with JEM theory, the blade rotational speed of the target rotor can be estimated to be approximately in f is an estimated value of the rotor blade speed. T The periodic line interval is denoted by N, where N is the number of blades. When N is even, α = 1; when N is odd, α = 2. From... Figure 4 It can be known that f T =112.13Hz. Since most small rotary-wing UAVs have two-bladed or three-bladed rotors, this prior information allows us to calculate the possible values ​​for the target rotor speed. (corresponding to twin propellers) and (Corresponding to three blades)

[0071] Step 4: Based on the target rotor speed parameters estimated in Step 3, construct a sinusoidal phase-matching term. It satisfies:

[0072]

[0073] in This represents an estimate of the initial phase of the target rotor blades. λ represents the estimated length of the target rotor blade, where λ = c / f c The wavelength of the radar transmitted signal.

[0074] Step 5: Based on the phase matching term in Step 4, let For a smaller value, such as Where Δr is the length of a distance cell; let In the range [0, 2π), increment by step. Uniformly selected values, such as At this point, the phase matching term can be expressed as: Phase matching and fast Fourier transform (HFT) are performed on the rotor echo signal. m,n (f) = FFT[h m (t m )⊙e sin,n (t m Finally, near the zero Doppler frequency, such as in the range of [-0.2PRF, 0.2PRF], the spectrum H is calculated and recorded. m,n The maximum amplitude of (f), i.e., Amax = max(H) m,n(f)), f e [-0.2PRF, 0.2PRF]. Traverse in [0, 2π) and repeat the above calculation to draw the curve of A max , the corresponding of the curve peak is the estimation of the initial phase of the rotor blade. Under the assumption of double-blade, the curve of A max is shown in Figure 5 , Figure 5 There are two obvious peaks in the curve, which is consistent with the assumption; under the assumption of three-blade, the curve of A max is shown in Figure 6 , Figure 6 There are multiple irregular peaks in the curve. Therefore, it can be judged that the target rotor is double-blade, and the initial phase of the blade is about 0.74 rad and 3.88 rad, respectively.

[0075] Step 6: On the basis of obtaining the estimation of the target rotor blade speed and initial phase, the blade scattering point model reconstruction based on OMP algorithm is performed. The target rotor echo can be represented as:

[0076] h m = SDx + n

[0077] Where h m is the observed signal vector, S is a random undersampling matrix, x is the distribution function of the scattering points on the blade, n is the noise input target rotor echo observation vector, D represents the over-complete dictionary, and each column of the original d m subscript of D is a phase matching item of different scales, that is, D = [e sin (t m ; Δl), e sin (t m ; 2Δl), …, e sin (t m ; MΔl)]. Where e sin is a phase matching item, M represents the number of atoms in the dictionary, Δl represents the step, and Δl = 0.005 m. Then the reconstruction of the rotor sparse scattering points can be represented as:

[0078] min||x||0 s.t.||h m - SDx||2 < ε

[0079] Where ||·||0 and ||·||2 represent L0 norm and L2 norm, respectively, and ε represents the residual.

[0080] Input the target rotor echo observation vector h mThe target rotor is located in a distance cell with a size of 1×2048; input an overcomplete dictionary D and a random undersampled matrix S; initialize the residual r0 = h m Projection matrix F0 = 0, subspace index The maximum number of iterations K and the energy threshold ε0. K is related to the sparsity of the scene and can be obtained through the radar wavelength and prior knowledge of the target. The radar wavelength is λ = c / f. c =4mm, the blade length of a small rotary-wing UAV is usually between 5-20cm, so the size of K can be inferred. In this example, K=18; the energy threshold ε0 can be calculated from the region in the parameter plane that does not contain the blade; let the number of iterations t=1.

[0081] Step 7: Find the dictionary D containing the remainder r. t-1 The atom with the largest inner product d m and its index m, i.e., calculation And remove the atom d from dictionary D. m Then update the projection matrix F t =F t-1 ∪d m and subspace index set Λ t =Λ t-1 ∪m;

[0082] Step 8: Update residuals Let the iteration number t = t + 1;

[0083] Step 9: Determine the iteration termination condition if t≥K or ||r t If ||<ε0, then jump to step 10; otherwise, jump to step 7.

[0084] Step 10: Utilize and Λ t The target rotor scattering point distribution function x is reconstructed, and the result is as follows: Figure 7 As shown, the trend characterized by the sparse scattering points indicates that the blade length is approximately 0.05 m.

[0085] It should be understood that, in order to simplify the present invention and help those skilled in the art understand its various aspects, in the above description of exemplary embodiments of the present invention, various features of the present invention are sometimes described in a single embodiment or with reference to a single figure. However, the present invention should not be construed as including all features in the exemplary embodiments as essential technical features of the claims of this patent.

Claims

1. A method for decoupling and estimating UAV rotor parameters based on the OMP algorithm, characterized in that, Includes the following steps: Step 1: Acquire radar echoes from rotary-wing UAVs and complete data preprocessing including signal sampling and pulse compression; Step 2: Combining the rotor component echo signal model, using the target radar echo from Step 1, construct the target radar echo matrix in units of one frame of data, and perform motion compensation and filtering based on the echo of the rigid body part. Step 3: Perform a fast Fourier transform on the distance cell where the target rotor is located to obtain the spectrum. Based on JEM theory, extract the periodic line spectrum in the spectrum to estimate the possible values ​​of the target rotor speed. Step 4: Based on the rotor speed estimated in Step 3, construct a sinusoidal phase matching term by combining the rotor component echo signal model. Where t m Representing slow time, l, ω m θ0 and θ0 represent the rotor blade length, rotational speed, and initial phase angle during the coherent accumulation time, respectively, and f c Where c is the center frequency of the transmitted signal, and c is the speed of electromagnetic wave propagation. Step 5: Multiply the range cell echo of the target rotor by the phase matching term, perform a Fast Fourier Transform, and calculate the maximum value A of the spectral amplitude near zero Doppler. max ; Draw A max With the changing trend of the initial phase search direction, according to A max Based on the changing trend and the assumption of the number of blades, the initial phase of the target rotor blades is estimated, and the correct target rotor speed is determined. Step 6: Based on the estimated target rotor speed and blade initial phase, perform blade scattering point model reconstruction based on the OMP algorithm to estimate the blade length; input the target rotor echo observation vector h. m Input an overcomplete dictionary D = [e sin (t m ;Δl),e sin (t m ;2Δl),…,e sin (t m ;MΔl)], whose columns of atoms d m Represents phase-matching terms at different scales, M represents the number of atoms in the dictionary, Δl represents the step size, and e sin For phase matching terms; input random undersampling matrix S; initialize residual r0 = h m Projection matrix F0 = 0, subspace index set The maximum number of iterations K and the energy threshold ε0, where ε0 is used to measure the noise level and is calculated from the non-blade region in the parameter plane; let the number of iterations t = 1; Step 7: Find the dictionary D containing the remainder r. t-1 The atom with the largest inner product d m and its index m, i.e., calculation And remove the atom d from dictionary D. m Then update the projection matrix F t =F t-1 ∪d m and subspace index set Λ t =Λ t-1 ∪m; Step 8: Update residual r t =h m -F t (F t H F t ) -1 F t H h m And let the iteration number t = t + 1; Step 9: Determine the iteration termination condition if t≥K or ||r t If ||<ε0, then jump to step 10; otherwise, jump to step 7. Step 10: Using x = (F t H F t ) -1 F t H h m and Λ t Reconstruct the target rotor scattering point distribution function x to estimate the length of the target rotor blades.

2. The method for decoupling and estimating UAV rotor parameters based on the OMP algorithm according to claim 1, characterized in that, In step 6, based on the obtained target rotor blade rotational speed and initial phase estimates, blade scattering point model reconstruction based on the OMP algorithm is performed; the target rotor echo is represented as: h m =SDx+n Where x is the distribution function of the scattering points on the blade, and n is the noise input target rotor echo observation vector; then the reconstruction of the sparse scattering points of the rotor is expressed as: min||x||0s.t.||h m -SDx||2ϵ Where ||·||0 and ||·||2 represent the L0 norm and L2 norm, respectively, and ε represents the residual.

3. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method as described in any one of claims 1-2.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1-2.

5. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method described in any of claims 1-2.

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