Rotor unmanned aerial vehicle micro-motion parameter estimation method

Abstract

The invention discloses a rotor unmanned aerial vehicle micro-motion parameter estimation method, which comprises the steps of performing pulse compression on radar echoes of a rotor unmanned aerial vehicle, and taking slow time data of a distance envelope peak value position as an observation signal of sparse reconstruction; constructing a dictionary matrix based on observation signals, and converting a micro-Doppler parameter estimation problem into a sparse reconstruction problem; and on the basis of the dictionary matrix, utilizing a Bayesian sparse reconstruction algorithm to estimate the rotation frequencies of a plurality of rotors of the rotor unmanned aerial vehicle. The method is applied to the field of radar signal processing, can perform parameter estimation on a plurality of micro-Doppler signals in an aliasing state in a noise environment, has the advantages of high estimation precision and good robustness, can be widely applied to searching and monitoring of air rotor type unmanned aerial vehicle targets, and has wide application prospects. The method can be effectively suitable for micro-motion parameter estimation of the hovering unmanned aerial vehicle under the low signal-to-noise ratio.

Description

Technical Field

[0001] The present invention relates to the technical field of radar signal processing, and in particular to a method for estimating micro-motion parameters of a rotary-wing unmanned aerial vehicle (UAV). Background Art

[0002] In recent years, rotary-wing drones (UAVs) have been widely used in agriculture, transportation, security, and communications. While these drones play an important role, they also raise significant security concerns: many unauthorized flights are used for illegal activities such as surveying and smuggling. Therefore, research on UAV detection and identification technologies is crucial. Radar, due to its ability to operate in various weather conditions, is widely used to detect unknown aerial targets. However, UAVs are often missed by radar due to their small radar cross-section (RCS) and slow movement speed. Furthermore, when a UAV is hovering, traditional moving target detection (MTD) methods cannot distinguish the UAV's echo from ground clutter. In this case, the micro-Doppler signal generated by the UAV's rotating blades can effectively estimate the rotor's rotational frequency, thereby enabling the identification of hovering UAVs.

[0003] Time-frequency (TF) analysis is a typical method for estimating rotor frequency from micro-Doppler signals. However, in windy outdoor environments, drones adjust each rotor to a different frequency to maintain hovering. Multiple micro-Doppler signals of different frequencies coexist, and severe superposition leads to strong cross-interference. When micro-Doppler signals overlap in the time-frequency domain, it is difficult to estimate the parameters of each micro-Doppler component using TF methods.

[0004] In this case, a potential approach is to use special basis functions to decompose the echo signal, thereby separating micro-Doppler components of different frequencies, and finally using compressed sensing methods to estimate the parameters of each micro-Doppler component. In theory, empirical mode decomposition (EMD) is used to obtain the corresponding intrinsic mode function (IMF) components, followed by sparse recovery using the orthogonal matching pursuit (OMP) method to estimate the micro-Doppler parameters of the drone. However, the EMD algorithm suffers from the problem of modal aliasing, which hinders the accurate decomposition of each micro-Doppler component into independent IMF components. At the same time, the OMP algorithm is highly sensitive to noise and usually cannot obtain reliable results in complex environments. Summary of the Invention

[0005] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for estimating micro-motion parameters of a rotary-wing UAV, which can simultaneously estimate the aliased micro-Doppler signal parameters and has the advantages of high estimation accuracy and strong robustness.

[0006] To achieve the above object, the present invention provides a method for estimating micro-motion parameters of a rotary-wing UAV, comprising the following steps:

[0007] Step 1: pulse compress the radar echo of the rotorcraft UAV, and take the slow time data at the peak position of the envelope as the sparsely reconstructed observation signal;

[0008] Step 2: constructing a dictionary matrix based on the observation signal, and converting the micro-Doppler parameter estimation problem into a sparse reconstruction problem;

[0009] Step 3: Based on the dictionary matrix, the Bayesian sparse reconstruction algorithm is used to estimate the rotation frequencies of multiple rotors of the rotorcraft.

[0010] In one embodiment, in step 1, the radar echo of the rotary wing UAV is the sum of the fuselage echo and the rotor echo;

[0011] The fuselage echo is:

[0012]

[0013] Among them, s frame (t′,τ m ) represents the fuselage echo, t′ represents the fast time and 0≤t′≤T r , T r is the sweep time width within a pulse; τ m =mT p represents slow time and m=0,1,...,M-1, T p is a pulse repetition interval, M is the total number of pulses; σ f represents the echo scattering coefficient of the fuselage, μ represents the modulation frequency, R0 represents the initial distance between the UAV and the radar, c represents the speed of light, j represents the imaginary unit, and f0 represents the radar carrier frequency;

[0014] The rotor echo is:

[0015]

[0016] Among them, s rotors (t′,τ m ) represents the rotor echo, σ r represents the rotor echo scattering coefficient, I represents the number of rotors of the UAV, N represents the number of rotor blades, L represents the length of the blades, and β represents the pitch angle of the UAV relative to the radar. and They represent the rotation frequency of the i-th rotor of the UAV and the initial angle of the n-th blade of this rotor respectively;

[0017] That is, the radar echo of the rotary-wing UAV is:

[0018]

[0019] Among them, s(t′,τ m ) represents the radar echo of the rotary-wing UAV.

[0020] In one embodiment, in step 1, the observation signal is:

[0021]

[0022] Among them, s(τ m ) represents the observed signal, i.e., the slow-time echo at the peak of the range envelope, A f and A r Represent the amplitudes of the fuselage echo and the rotor echo respectively.

[0023] In one embodiment, step 2 includes:

[0024] Step 201: constructing a basis function capable of extracting micro-motion parameters based on the observation signal;

[0025] Step 202: defining a set of rotation frequency parameter ranges based on the observation signal and the basis function;

[0026] Step 203: construct a dictionary matrix based on the set of rotation frequency parameter ranges.

[0027] In one embodiment, the phase modulation mode in the observation signal is sinusoidal modulation, and the modulation frequency is the micro-motion parameter to be estimated. Therefore, the basis function constructed in step 201 is an exponential complex sine basis, which is expressed as:

[0028]

[0029] Among them, φ k (m) represents the basis function, Δf represents the rotation frequency search step during the sparse reconstruction algorithm, and k represents the search index.

[0030] In one embodiment, in step 202, the set of rotation frequency parameter ranges is:

[0031] Γ f ={kΔf|k=1,2,...,K}

[0032] Among them, Γ frepresents a set of rotation frequency parameter ranges, and K represents the number of elements in the rotation frequency parameter set.

[0033] In one embodiment, in step 203, the dictionary matrix is:

[0034] Φ=[Φ1,Φ2,...,Φ K ]

[0035] Φ k =[φ k (0),φ k (1),...,φ k (M-1)] T

[0036] Among them, Φ represents the dictionary matrix, Φ k represents the matrix atom corresponding to the kth parameter in the parameter set, and T represents the transpose of the matrix.

[0037] In one embodiment, step 3 specifically includes:

[0038] Bring the dictionary matrix into the sparse reconstruction signal model, that is:

[0039] y=Φx+e

[0040] Where y represents the observed signal, x represents the sparse signal, and e represents Gaussian noise;

[0041] The sparse reconstruction process of the sparse signal x is:

[0042]

[0043] in, represents the reconstructed sparse signal, and ε represents the error threshold;

[0044] The sparse reconstruction is performed using the Bayesian sparse reconstruction algorithm with improved Laplace prior, and the sparse signal obtained is The corresponding rotation frequency parameter set Γ f The parameters in the equation are the estimated values ​​of the rotation frequencies of each rotor of the UAV.

[0045] To achieve the above object, the present invention further provides a micro-motion parameter estimation system for a rotary-wing UAV, which adopts the above method and comprises:

[0046] An observation signal acquisition unit is used to perform pulse compression on the radar echo of the rotary-wing UAV and obtain the slow-time data at the peak position of the distance envelope as the observation signal for sparse reconstruction;

[0047] a dictionary matrix construction unit, configured to construct a dictionary matrix according to the observation signal, and convert the micro-Doppler parameter estimation problem into a sparse reconstruction problem;

[0048] The sparse reconstruction unit is used to estimate the rotation frequency of multiple rotors of the rotor UAV by using a Bayesian sparse reconstruction algorithm based on the dictionary matrix.

[0049] To achieve the above object, the present invention further provides a terminal device, wherein the terminal device is provided with:

[0050] Memory, used to store programs;

[0051] The processor is configured to execute the program stored in the memory. When the program is executed, the processor is configured to execute the above method.

[0052] Compared with the prior art, the present invention has the following beneficial technical effects:

[0053] The present invention can perform parameter estimation on multiple micro-Doppler signals in an aliased state in a noisy environment, and has the advantages of high estimation accuracy and good robustness. It can be widely used in search and surveillance applications of aerial rotary-wing UAV targets, and can be effectively applied to the micro-motion parameter estimation of hovering UAVs under low signal-to-noise ratio, and has great application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0055] Figure 1 Flowchart of a method for estimating micro-motion parameters of a rotary-wing UAV according to an embodiment of the present invention;

[0056] Figure 2 Schematic diagram of simulation experiment results in an embodiment of the present invention;

[0057] Figure 3 This is a comparison chart of the fan rotation frequency estimation accuracy of the method in the embodiment of the present invention, the time-frequency analysis method, and the existing sparse reconstruction algorithm under measured data, where: Figure 3 (a) is a schematic diagram of the short-time Fourier transform spectrum of the echo signal. Figure 3 (b) is a comparison chart of the rotation frequency estimation results of the method of this embodiment and the existing sparse reconstruction algorithm;

[0058] Figure 4This is a comparison chart of the accuracy of UAV rotor frequency estimation using the method in the embodiment of the present invention, the time-frequency analysis method, and the existing sparse reconstruction algorithm under measured data, where: Figure 4 (a) is a schematic diagram of the short-time Fourier spectrum of the UAV echo signal. Figure 4 (b) is a comparison chart of the rotation frequency estimation results of the method of this embodiment and the existing sparse reconstruction algorithm;

[0059] Figure 5 This is a structural block diagram of a micro-motion parameter estimation system for a rotary-wing UAV according to an embodiment of the present invention;

[0060] Figure 6 2 is a structural block diagram of a terminal device in an embodiment of the present invention.

[0061] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0062] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0063] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0064] In addition, the technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the fact that ordinary technicians in this field can implement it. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0065] Example 1

[0066] This embodiment discloses a method for estimating micro-motion parameters of a rotary-wing UAV. Aiming at the problem of estimating micro-Doppler parameters of a rotary-wing UAV in a hovering state, a suitable dictionary matrix is ​​designed by combining the special modulation mode of the rotor echo, and a sparse reconstruction method is used to achieve parameter estimation of the aliased micro-Doppler signal in a noisy environment. Figure 1 The method for estimating micro-motion parameters of a rotary-wing UAV in this embodiment specifically includes the following steps:

[0067] Step 1: pulse compress the radar echo of the rotorcraft UAV, and take the slow time data at the peak position of the envelope as the sparsely reconstructed observation signal;

[0068] Step 2: Construct a dictionary matrix based on the observed signal and transform the micro-Doppler parameter estimation problem into a sparse reconstruction problem;

[0069] Step 3: Based on the dictionary matrix, the Bayesian sparse reconstruction algorithm is used to estimate the rotation frequencies of multiple rotors of the rotorcraft.

[0070] In the specific implementation process of step 1, the radar echo s(t′,τ m ) is the fuselage echo s frame (t′,τ m ) and rotor echo s rotors (t′,τ m ) and.

[0071] The fuselage echo expression is:

[0072] s frame (t′,τ m )=A f exp[j2πf0(t′+τ m -t d )+jπμ(t′+τ m -t d ) 2 ] (1)

[0073] Where t′ represents the fast time and 0′≤t′≤T r , T r is the sweep time width within a pulse; τ m =mT p represents slow time and m=0,1,...,M-1, T p is a pulse repetition interval, M is the total number of pulses, A f represents the amplitude of the fuselage echo, j represents the imaginary unit, f0 represents the radar carrier frequency, μ represents the modulation frequency, t d Indicates echo delay;

[0074]

[0075] Where R0 represents the initial distance between the UAV and the radar, and c represents the speed of light;

[0076] In this embodiment, the fuselage echo signal is pulse compressed by matched filtering, that is:

[0077]

[0078] Among them, σ fRepresents the fuselage echo scattering coefficient.

[0079] The echo signal s modulated by the UAV rotor rotors (t′,τ m ) can be regarded as the sum of the echo signals of each rotor of the UAV. Assume that the UAV has a total of I rotors, each rotor has N blades, and the length of the blades is L, where and Denote the rotation frequency of the i-th rotor of the UAV and the initial angle of the n-th blade of this rotor respectively, and β denotes the pitch angle of the UAV relative to the radar. Then, the expression of the echo signal modulated by the UAV rotor can be obtained as follows:

[0080]

[0081] Among them, σ r represents the rotor echo scattering coefficient;

[0082] Therefore, the final radar echo of the rotor UAV is:

[0083]

[0084] Among them, s(t′,τ m ) represents the radar echo of the rotary-wing UAV.

[0085] Taking the slow-time echo at the peak of the range-dimension envelope, the observed signal can be obtained as:

[0086]

[0087] Among them, s(τ m ) represents the observed signal, i.e., the slow-time echo at the peak of the range envelope, A f and A r Represent the amplitudes of the fuselage echo and the rotor echo respectively.

[0088] In the specific implementation process of step 2, the construction process of the dictionary matrix is ​​as follows:

[0089] Step 201: construct a basis function capable of extracting micro-motion parameters based on the observed signal;

[0090] Step 202 , defining a set of rotation frequency parameter ranges based on the observed signal and the basis function;

[0091] Step 203: construct a dictionary matrix based on the set of rotation frequency parameter ranges.

[0092] In the specific implementation process, the observation signal of formula (6) can be further expressed as:

[0093]

[0094] Among them, m=0,1,...,M-1, A'=A f exp(-j4πf0R0 / c), A″=A r exp(-j4πf0R0 / c), A′ and A″ are both complex constants. The phase modulation mode in the observed signal is sinusoidal modulation, and the modulation frequency is the micro-motion parameter to be estimated Therefore, in order to extract the micro-motion parameters, the constructed basis function is an exponential complex sine basis, which is expressed as:

[0095]

[0096] Among them, φ k (m) represents the basis function, Δf represents the rotation frequency search step size during the sparse reconstruction algorithm, and k represents the search index;

[0097] Based on the observed signal and the exponential complex sine basis, the set of rotation frequency parameter ranges is defined as:

[0098] Γ f ={kΔf|k=1,2,...,K} (9)

[0099] Among them, Γ f represents a set of rotation frequency parameter ranges, and K represents the number of elements in the rotation frequency parameter set;

[0100] According to the set of rotation frequency parameter ranges, the matrix atom Φ is defined k =[φ k (0),φ k (1),...,φ k (M-1)] T , is the matrix atom corresponding to the kth parameter in the parameter set, T represents the transpose of the matrix, and the dictionary matrix consists of K matrix atoms, namely:

[0101] Φ=[Φ1,Φ2,...,Φ K ] (10)

[0102] Where Φ represents the dictionary matrix.

[0103] In the specific implementation process of step 3, the process of estimating the rotation frequencies of multiple rotors of the rotary-wing UAV is as follows:

[0104] First, the observation signal and the dictionary matrix are brought into the sparse reconstruction signal model, that is:

[0105] y=Φx+e (11)

[0106] Where y represents the observed signal, Represents Gaussian noise, sparse signal And s = Φx, s is the ideal sparse representation of the UAV echo signal, and the sparse reconstruction process of the sparse signal x is:

[0107]

[0108] in, Represents the reconstructed sparse signal, ε represents the error threshold, and the reconstructed sparse signal and the rotation frequency parameter set Γ f The parameters in correspond to each other;

[0109] Then, the sparse reconstruction is performed using the Bayesian sparse reconstruction algorithm with improved Laplace prior, and the sparse signal obtained is The corresponding rotation frequency parameter set Γ f The parameters in the equation are the estimated values ​​of the rotation frequencies of each rotor of the UAV.

[0110] It is worth noting that although this embodiment Figure 1 The steps in the diagram are shown in the order indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0111] The following is a further explanation of the micro-motion parameter estimation method of the rotor UAV in this embodiment in conjunction with a specific simulation experiment. The radar system parameters and UAV parameters of the simulation experiment are shown in Table 1 below:

[0112] Table 1 Parameters of radar system and UAV in simulation experiment

[0113] Radar system parameters Value (unit) Drone parameters Value (unit) <![CDATA[Carrier frequency f0]]> 17GHz <![CDATA[Initial distance R0]]> 150m Bandwidth B 20MHz Number of rotors K 4 <![CDATA[Pulse repetition interval T p > 1ms Number of blades I 2 <![CDATA[Pulse internal sweep time width T r > 10μs Blade length L 0.1194m

[0114] A simulation experiment was conducted based on the above-mentioned radar system parameters and UAV parameters. First, the UAV rotor rotation frequency was set to a random number in the range of 60 to 120 Hz. Then, 1 to 4 different rotation frequencies were set simultaneously in the four UAV rotors to obtain the UAV simulated echo. Finally, Gaussian complex noise was added, and the signal-to-noise ratio range of the signal was adjusted by controlling the noise amplitude. 100 Monte Carlo experiments were performed at each signal-to-noise ratio. Finally, the root mean square error of the estimation result was calculated and its performance was compared with that of the relevance vector machine (RVM) and the Bayesian compressive sensing algorithm using Laplace Priors (BCS-LP). Figure 2 The simulation results are shown below. It can be seen that the RMS error of the RVM and BCS-LP algorithms decreases slowly with increasing SNR, remaining consistently above 15 Hz. However, the RMS error of the proposed method (CBCS-MLP) decreases rapidly with increasing SNR, remaining around 2 Hz at a SNR of -7 dB. Therefore, the simulations demonstrate that the proposed method has greater accuracy and robustness than other algorithms.

[0115] The effectiveness of the method proposed in this embodiment is further verified by actual measured data.

[0116] Taking the fan experiment as an example, the radar used in the experiment is the AWR2243BOOST linear frequency modulated pulse radar, with a radar carrier frequency f0 = 77 GHz, a modulation frequency μ = 30 MHz / μs, an intra-pulse sweep width of 25.6 μs and a pulse repetition interval of 16 ms. In the experiment, the fan was 1.5 m away from the radar, and each fan blade was 0.05 m long. Figure 3 This is a comparison chart of the fan rotation frequency estimation accuracy of the method of this embodiment, the time-frequency analysis method, and the existing sparse reconstruction algorithm under measured data, where Figure 3 (a) is the short-time Fourier transform spectrum of the echo signal. The blade rotation frequency can be estimated based on the peak interval in the figure. The fan rotation frequency is measured by the frequency meter as f r =65.2Hz, and the frequency estimation error of the time-frequency analysis method is 4.3%. Figure 3 (b) shows the rotation frequency estimation results of the method proposed in this embodiment and the existing sparse reconstruction algorithm. It can be seen that the RVM algorithm estimation result is 81Hz, and the estimation error is 24%. The estimation results of the method proposed in this embodiment and the BCS-LP algorithm are both The estimated error is 0.3%. Therefore, when estimating the rotation frequency of a fan at close range, the accuracy of the method proposed in this embodiment is better than that of time-frequency analysis methods.

[0117] refer to Figure 4 This figure compares the accuracy of drone rotor frequency estimation using measured data using the method in this embodiment, time-frequency analysis methods, and existing sparse reconstruction algorithms. In this experiment, the radar parameters used were consistent with those in the simulation experiment, and the drone to be detected was the DJI Air2. Figure 4 (a) is the short-time Fourier spectrum of the drone's echo signal. Due to the different rotation frequencies of the drone's rotors, the micro-Doppler components in the echo's short-time Fourier spectrum are severely aliased, resulting in a lack of periodicity in the energy distribution in the spectrum and making it impossible to estimate the rotor rotation frequency. Figure 4 (b) shows the rotation frequency estimation results of the method proposed in this embodiment and the existing sparse reconstruction algorithm. It can be seen that the method proposed in this embodiment successfully estimates the rotation frequency of the four rotors of the drone at the same time. r1 =91Hz and f r2 =75Hz, but both the RVM algorithm and the BCS-LP algorithm failed to estimate the rotor rotation frequency.

[0118] In summary, compared with time-frequency analysis methods, relevance vector machine (RVM) and Bayesian compressed sensing algorithm based on Laplace prior, the micro-Doppler parameter estimation method for rotorcraft UAV proposed in this paper can perform parameter estimation on multiple micro-Doppler signals in an aliased state in a noisy environment, with higher estimation accuracy and stronger robustness.

[0119] Example 2

[0120] Based on the micro-motion parameter estimation method of the rotary-wing UAV in Example 1, this embodiment discloses a micro-motion parameter estimation method system for the rotary-wing UAV, referring to Figure 5 The micro-motion parameter estimation method system of the rotary-wing UAV includes an observation signal acquisition unit, a dictionary matrix construction unit, and a sparse reconstruction unit. Specifically:

[0121] The observation signal acquisition unit is used to perform pulse compression on the radar echo of the rotorcraft UAV and take the slow time data at the peak position of the envelope as the sparsely reconstructed observation signal;

[0122] The dictionary matrix construction unit is used to construct a dictionary matrix based on the observation signal, transforming the micro-Doppler parameter estimation problem into a sparse reconstruction problem;

[0123] The sparse reconstruction unit is used to estimate the rotation frequencies of multiple rotors of the rotorcraft using the Bayesian sparse reconstruction algorithm based on the dictionary matrix.

[0124] In this embodiment, the specific working process and working principle of the observation signal acquisition unit, the dictionary matrix construction unit, and the sparse reconstruction unit are the same as those in the method of Example 1, and therefore will not be described in detail in this embodiment. Each unit module can be implemented in whole or in part by software, hardware, or a combination thereof. Each unit module can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above unit modules.

[0125] Example 3

[0126] like Figure 6 The terminal device disclosed in this embodiment includes a transmitter, a receiver, a memory, and a processor. The transmitter is used to send instructions and data, the receiver is used to receive instructions and data, the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions stored in the memory to implement the method in the above-mentioned embodiment 1.

[0127] It should be noted that the above memory can be independent or integrated with the processor. When the memory is independently provided, the terminal device further includes a bus for connecting the memory and the processor.

[0128] The above description is only a preferred embodiment of the present invention and does not limit the scope of protection of the present invention. All equivalent structural transformations made by using the contents of the present invention description and drawings under the inventive concept of the present invention, or direct / indirect application in other related technical fields are included in the scope of protection of the present invention.

Claims

1. A method for estimating micro-motion parameters of a rotary-wing UAV, characterized in that: The steps include: Step 1: pulse compress the radar echo of the rotorcraft UAV, and take the slow time data at the peak position of the envelope as the sparsely reconstructed observation signal; Step 2: constructing a dictionary matrix based on the observation signal, and converting the micro-Doppler parameter estimation problem into a sparse reconstruction problem; Step 3: Based on the dictionary matrix, the Bayesian sparse reconstruction algorithm is used to estimate the rotation frequencies of multiple rotors of the rotorcraft.

2. The method for estimating micro-motion parameters of a rotary-wing UAV according to claim 1, characterized in that: In step 1, the radar echo of the rotary-wing UAV is the sum of the fuselage echo and the rotor echo; The fuselage echo is: Among them, s frame (t′,τ m ) represents the fuselage echo, t′ represents the fast time and 0≤t′≤T r , T r is the sweep time width within a pulse; τ m =mT p represents slow time and m=0,1,...,M-1, T p is a pulse repetition interval, M is the total number of pulses; σ f represents the echo scattering coefficient of the fuselage, μ represents the modulation frequency, R0 represents the initial distance between the UAV and the radar, c represents the speed of light, j represents the imaginary unit, and f0 represents the radar carrier frequency; The rotor echo is: Among them, s rotors (t′,τ m ) represents the rotor echo, σ r represents the rotor echo scattering coefficient, I represents the number of rotors of the UAV, N represents the number of rotor blades, L represents the length of the blades, and β represents the pitch angle of the UAV relative to the radar. and They represent the rotation frequency of the i-th rotor of the UAV and the initial angle of the n-th blade of this rotor respectively; That is, the radar echo of the rotary-wing UAV is: Among them, s(t′,τ m ) represents the radar echo of the rotary-wing UAV.

3. The method for estimating micro-motion parameters of a rotary-wing UAV according to claim 2, characterized in that: In step 1, the observation signal is: Among them, s(τ m ) represents the observed signal, i.e., the slow-time echo at the peak of the range envelope, A f and A r Represent the amplitudes of the fuselage echo and the rotor echo respectively.

4. The method for estimating micro-motion parameters of a rotary-wing UAV according to claim 3, characterized in that: Step 2 includes: Step 201: constructing a basis function capable of extracting micro-motion parameters based on the observation signal; Step 202: defining a set of rotation frequency parameter ranges based on the observation signal and the basis function; Step 203: construct a dictionary matrix based on the set of rotation frequency parameter ranges.

5. The method for estimating micro-motion parameters of a rotary-wing UAV according to claim 4, characterized in that: The phase modulation mode in the observation signal is sinusoidal modulation, and the modulation frequency is the micro-motion parameter to be estimated. Therefore, the basis function constructed in step 201 is an exponential complex sine basis, which is expressed as: Among them, φ k (m) represents the basis function, Δf represents the rotation frequency search step during the sparse reconstruction algorithm, and k represents the search index.

6. The method for estimating micro-motion parameters of a rotary-wing UAV according to claim 5, characterized in that: In step 202, the set of rotation frequency parameter ranges is: C f ={kΔf|k=1,2,...,K} Among them, Γ f represents a set of rotation frequency parameter ranges, and K represents the number of elements in the rotation frequency parameter set.

7. The method for estimating micro-motion parameters of a rotary-wing UAV according to claim 6, characterized in that: In step 203, the dictionary matrix is: Φ=[Φ1,Φ2,...,Φ K ] F k =[φ k (0),φ k (1),...,φ k (M-1)] T Among them, Φ represents the dictionary matrix, Φ k represents the matrix atom corresponding to the kth parameter in the parameter set, and T represents the transpose of the matrix.

8. The method for estimating micro-motion parameters of a rotary-wing UAV according to claim 7, characterized in that: Step 3 specifically includes: Bring the dictionary matrix into the sparse reconstruction signal model, that is: y=Φx+e Where y represents the observed signal, x represents the sparse signal, and e represents Gaussian noise; The sparse reconstruction process of the sparse signal x is: in, represents the reconstructed sparse signal, and ε represents the error threshold; The sparse reconstruction is performed using the Bayesian sparse reconstruction algorithm with improved Laplace prior, and the sparse signal obtained is The corresponding rotation frequency parameter set Γ f The parameters in the equation are the estimated values ​​of the rotation frequencies of each rotor of the UAV.

9. A micro-motion parameter estimation system for a rotary wing UAV, characterized in that: According to any one of claims 1 to 8, the micro-motion parameter estimation system for the rotary-wing UAV comprises: An observation signal acquisition unit is used to perform pulse compression on the radar echo of the rotary-wing UAV and obtain the slow-time data at the peak position of the distance envelope as the observation signal for sparse reconstruction; a dictionary matrix construction unit, configured to construct a dictionary matrix according to the observation signal, and convert the micro-Doppler parameter estimation problem into a sparse reconstruction problem; The sparse reconstruction unit is used to estimate the rotation frequency of multiple rotors of the rotor UAV by using a Bayesian sparse reconstruction algorithm based on the dictionary matrix.

10. A terminal device, characterized in that: The terminal device is provided with: Memory, used to store programs; A processor is configured to execute the program stored in the memory, wherein when the program is executed, the processor is configured to execute the method according to any one of claims 1 to 8.

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