Micro-Doppler signal parameter estimation method for rotorcraft UAV based on hybrid method

Abstract

The present invention relates to a method for estimating micro-Doppler signal parameters of a rotor-type UAV based on a hybrid method, and belongs to the technical field of target signal detection. The present invention uses a hybrid method of short-time Fourier transform, time domain correlation, and parameter compensation to complete the estimation of signal micro-Doppler frequency, rotation frequency, number of rotors and other parameters. The results show that when the signal-to-noise ratio is low, accurate parameter estimation of the signal can still be obtained, and the parameter estimation accuracy is higher than the result obtained by directly using short-time Fourier transform.

Description

Technical Field

[0001] The present invention belongs to the technical field of target signal detection, and in particular relates to a method for estimating micro-Doppler signal parameters of a rotary-wing unmanned aerial vehicle based on a hybrid method. Background Art

[0002] Regarding the micro-Doppler analysis and identification of rotor-type targets, Professor Victor C. Chen studied the micro-Doppler characteristics caused by the rotors of low-altitude UAVs, and pointed out that for multi-rotor targets, the micro-Doppler generated by radial velocity is a very important feature and an important supplement to target feature identification. He also pointed out that the micro-Doppler spectra caused by multi-rotors will affect each other, and further research is needed on high-precision parameter estimation technology.

[0003] At present, the more mature time-frequency analysis methods are mainly divided into two categories. One is linear time-frequency analysis, such as short-time Fourier transform (STFT), Gabor transform, etc. The other is bilinear (nonlinear, quadratic) time-frequency analysis, such as WVD, PWVD, SPWVD, etc., collectively referred to as Cohen-type time-frequency distribution. The linear time-frequency analysis method satisfies the superposition principle and has been widely used in the occasion of processing multi-component signals, but the time-frequency resolution is not high enough. When the bilinear time-frequency analysis method processes multi-component quadratic phase term signals or single-component high-order phase term signals, the cross terms of the autocorrelation function will be displayed in the time-frequency distribution, which seriously affects the effect of time-frequency analysis. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] The technical problem to be solved by the present invention is: in view of the problem that the above-mentioned time-frequency analysis method is difficult to accurately estimate the parameters of the micro-Doppler signal of a multi-rotor target, a method for estimating the micro-Doppler signal parameters of a rotorcraft UAV based on a combination of time-frequency analysis, time domain correlation, and parameter compensation is proposed to improve the parameter estimation accuracy of the micro-Doppler signal.

[0006] (II) Technical solution

[0007] In order to solve the above technical problems, the present invention provides a method for estimating micro-Doppler signal parameters of a rotary-wing UAV based on a hybrid method, characterized in that it comprises the following steps:

[0008] S1: Collect the UAV micro-Doppler echo signal and express it in the form of exponential function s(t), which is expressed as:

[0009]

[0010] Wherein, in formula (1), N is the number of UAV rotors, ξ is the echo signal amplitude in volts, L is the length of the UAV rotor in meters, β is the angle between the radar antenna and the UAV rotor in radians, v0 is the initial speed of the UAV in meters per second, and f r is the rotation frequency of the drone rotor in Hertz, t is the signal duration in seconds, φ i is the initial phase of the i-th rotor of the UAV, in radians, λ is the signal wavelength, in meters, and w(t) is the Gaussian white noise signal;

[0011] S2: Perform short-time Fourier transform on the echo signal s(t) in step 1. The formula is:

[0012]

[0013] Wherein, ω(t) in formula (2) is the window function, and * represents the conjugate operator symbol;

[0014] S3: Perform Fourier transform on formula (2) in S2 with respect to time t, and the formula is:

[0015] S'(f t ,f)=FFT t [S(t,f)] (3)

[0016] S4: Search for the maximum value of the frequency f dimension around the origin of the coordinate axis in formula (3), and record the obtained frequency range as [f a , f b ], the maximum value search formula is:

[0017]

[0018] Among them, f in formula (4) a , f b They represent the minimum and maximum values ​​of the UAV micro-Doppler frequency after the maximum value search, respectively, in Hertz;

[0019] S5: Perform autocorrelation operation on formula (1) in S1 to obtain the frequency f between adjacent harmonics ab , the autocorrelation formula is:

[0020] y(t)=xcorr[s(t),s(t)] (5)

[0021]

[0022] Wherein, xcorr in formula (5) represents the mathematical symbol of autocorrelation operation, and y(t a ), y(t b) represent the time points corresponding to adjacent harmonics in y(t);

[0023] S6: According to the frequency f of the adjacent harmonic point ab , obtain the estimated rotation frequency of the drone and record the estimated value as The formula for obtaining is:

[0024]

[0025] Wherein, N' in formula (7) is the estimated number of UAV rotors;

[0026] S7: The minimum and maximum values ​​of the UAV micro-Doppler frequency f obtained according to formula (4) in S4 a , f b Get the estimated length of the number of drone rotors f L , the calculation formula is:

[0027]

[0028] S8: Estimated rotation frequency of the drone obtained according to formula (7) and formula (8) And the estimated length of the UAV rotor f L , perform parameter compensation operation on formula (1) and perform specific target detection, that is:

[0029]

[0030] Among them, φ in formula (9) x is the initial phase compensation estimate in radians;

[0031] S9: Perform Fourier transform on s'(t) in step 8, and select the maximum value of the estimated rotation frequency of the drone and the estimated rotor length of the drone. The formula is:

[0032]

[0033] Among them, A in formula (10) a , A b They represent the time dimension and frequency dimension positions corresponding to the maximum value of the UAV target signal, respectively, and f′ L ∈[f L -0.1f L ,f L +0.1f L ],

[0034] S10: For the signal at the maximum value position in S9 formula (10), a judgment threshold is set using the constant false alarm method to detect the target signal. When the amplitude of the judged target signal is greater than or equal to this threshold, it is considered to be a target signal. At this time, the estimated number of UAV rotors and the estimated length of UAV rotors in s'(t) in formula (10) are the target parameters required. When the amplitude of the judged target signal is less than this threshold, it is considered that the target signal is a false target and the corresponding parameters are not established.

[0035] Among them, the value range of the drone rotor number estimation N' in step S6 is the commonly used value of the drone rotor number. According to different drone rotor number estimations, four groups of different drone rotation frequencies and drone rotor length estimations are obtained, and the maximum value detection is performed on different groups of data to find the target signal.

[0036] Wherein, in step S9, L The step interval in ' is 0.1f L , The step interval is φ x The step interval in is 0.001.

[0037] The constant false alarm rate p in step S10 is the constant false alarm rate p. fa =10 -3 .

[0038] Wherein, the formula (1) in step S1 is an exponential function containing sinusoidal frequency modulation.

[0039] (III) Beneficial effects

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] 1) The present invention improves the micro-Doppler frequency estimation value of weak signals through short-time Fourier transform and time axis Fourier transform for micro-Doppler signals of low signal-to-noise ratio rotor-type UAVs;

[0042] 2) The present invention improves the estimated value of the rotor rotation frequency by using an autocorrelation method for the micro-Doppler signal of a low signal-to-noise ratio rotor-type UAV;

[0043] 3) The present invention aims at the micro-Doppler signal of a low signal-to-noise ratio rotor-type UAV, and improves the accuracy of the estimation of the actual parameters of the micro-Doppler signal by compensating the parameters of the micro-Doppler frequency and the rotation frequency. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the structure of the method of the present invention;

[0045] Figure 2Schematic diagram of the short-time Fourier transform detection result of micro-Doppler signal when N=2 and there is no noise;

[0046] Figure 3 Schematic diagram of detection results of the maximum and minimum values ​​of the micro-Doppler frequency when N=2 and there is no noise;

[0047] Figure 4 Schematic diagram of the detection result of micro-Doppler signal autocorrelation when N=2 and there is no noise;

[0048] Figure 5 Schematic diagram of micro-Doppler signal detection results after parameter compensation when N=2 and there is no noise;

[0049] Figure 6 Schematic diagram of the short-time Fourier transform detection results of micro-Doppler signals when N=2 and the signal-to-noise ratio is -13 dB;

[0050] Figure 7 Schematic diagram of detection results of the maximum and minimum values ​​of the micro-Doppler frequency when N=2 and the signal-to-noise ratio is -13 dB;

[0051] Figure 8 Schematic diagram of the short-time Fourier transform detection results of micro-Doppler signals when N=4 and the signal-to-noise ratio is -13 dB;

[0052] Fig. 9 Schematic diagram of the detection result of micro-Doppler signal autocorrelation when N=4 and the signal-to-noise ratio is -13 dB;

[0053] Fig.10 Schematic diagram of the detection results of micro-Doppler signals after compensation when N=4, the signal-to-noise ratio is -13 dB, and the rotation frequency is 8 Hz;

[0054] Fig.11 Schematic diagram of the detection results of micro-Doppler signals after compensation when N=4, the signal-to-noise ratio is -13 dB, and the rotation frequency is 4 Hz;

[0055] Fig.12 It is a schematic diagram of the short-time Fourier transform detection results of micro-Doppler signals after multiple signal parameters are changed;

[0056] Fig.13 A schematic diagram of micro-Doppler signal detection results after parameter compensation after multiple signal parameters are changed; DETAILED DESCRIPTION

[0057] In order to make the purpose, content, and advantages of the present invention more clear, the specific implementation methods of the present invention are further described in detail below in conjunction with the accompanying drawings and examples.

[0058] Example 1

[0059] To solve the above problems, this embodiment provides a method for estimating micro-Doppler signal parameters of a rotorcraft UAV based on a hybrid method. Figure 1 to Figure 5 The joint show includes:

[0060] Step 1: Assume that the typical echo signal to be analyzed is an exponential function with sinusoidal frequency modulation, and the expression is written as:

[0061]

[0062] In formula (1), N = 2, ξ = 1, the unit is volt, L = 6.5, β = 0°, v0 = 1 m / s, f r =4 Hz, t = 1 second, φ1 = 0, λ = 0.06 m, w(t) is 0;

[0063] Step 2: Perform a short-time Fourier transform on formula (1) and write it as:

[0064]

[0065] In formula (2), ω(t) is a Gaussian window function, and the length of the window is selected as 32;

[0066] Step 3: Perform Fourier transform on formula (2) with respect to time t and write it as:

[0067] S'(f t ,f)=FFT t [S(t,f)] (3)

[0068] Step 4: Perform a maximum search for the frequency f dimension of formula (3) about the origin, and record the obtained frequency range as:

[0069]

[0070] In formula (4), f a , f b They represent the minimum and maximum values ​​of the Doppler frequency, and the corresponding frequency points are 30 and 99 respectively;

[0071] Step 5: Perform autocorrelation operation on formula (1) and find the frequency between adjacent harmonics, which can be written as:

[0072] y(t)=xcorr[s(t),s(t)] (5)

[0073]

[0074] In formula (5), xcorr represents the mathematical symbol of autocorrelation operation, and in formula (6), y(t a )=0.75,y(t b)=0.875, in seconds, f ab =8, unit is Hz;

[0075] Step 6: Using f ab The estimated target rotation frequency can be obtained as:

[0076]

[0077] f ab =8 Substituting into formula (7), taking N'=2 or N'=4, we can get or

[0078] Step 7: Using f a , f b Get rotor target parameters The values ​​are:

[0079]

[0080] Substituting the results of formula (4) and formula (7) into formula (8), we can get: L =3.21 or f L =6.42;

[0081] Step 8: Use formula (7) and formula (8) to obtain f L Perform parameter compensation operation on formula (1), namely:

[0082]

[0083] In formula (9), φ x =0 is the initial phase compensation estimate, in radians;

[0084] Step 9: Perform Fourier transform on s'(t) and find the maximum value, which can be expressed as:

[0085]

[0086] In formula (10), A a , A b Respectively represent the time dimension and frequency dimension positions corresponding to the maximum value of the target signal, where

[0087] f′ L ∈[f L -0.1f L ,f L +0.1f L ] (11)

[0088]

[0089]

[0090] The step interval in formula (11) is 0.1f L , the step interval in formula (12) is The step interval in formula (13) is 0.001;

[0091] Step 10: Use the constant false alarm method to detect the target signal based on the result obtained by formula (10). When N'=2, f L =6.42, the constant false alarm rate p is met fa =10 -3 Detection, the parameters used by the target signal are the target parameters.

[0092] Figure 1 The overall implementation block diagram is as follows: 1. The micro-Doppler signal to be analyzed is in the form of a sinusoidal wave modulated signal; 2. Short-time Fourier transform is performed on the micro-Doppler signal; 3. The result of the short-time Fourier transform is Fourier transformed again with respect to the time axis; 4. Micro-Doppler frequency estimation is performed; 5. Autocorrelation operation is performed on the micro-Doppler signal; 6. The rotor rotation frequency is estimated using the autocorrelation result; 7. The micro-Doppler frequency estimation value and the rotor rotation frequency estimation value are substituted into the initial Doppler signal for echo compensation; 8. Fourier transform is performed on the signal after echo parameter compensation; 9. The number of UAV rotors, the rotation frequency of the UAV and the rotor length of the UAV are estimated. Figure 2 , Figure 3 , Figure 4 , Figure 5 They are the analysis results after short-time Fourier transform, micro-Doppler frequency estimation, autocorrelation, and signal parameter compensation, where the signal sampling frequency is 10K Hz, the number of points of the window function Fourier transform is 128, and Figure 2 It can be seen that in the absence of noise, the micro-Doppler signals of the two-rotor target are two positive curves, Figure 3 The maximum value of the micro-Doppler frequency can be obtained as (99-30)×(10K÷128)=5390.6 Hz, Figure 4 It can be calculated that the frequency interval between two adjacent harmonic signals is 4 Hz, and the estimated value is 4 Hz or 2 Hz, according to Figure 5 It can be obtained that when the signal parameters are matched correctly, the constant false alarm method can be used to obtain the target speed of 1 m / s, the number of rotors is 2, and the rotor length is 6.49 m, which is basically consistent with the theoretical value. The above simulation example diagrams and results show that the method of the present invention can realize the parameter estimation of micro-Doppler signals.

[0093] In the figure: 10 is the Y-axis: it represents the frequency points of the signal in the frequency domain, the unit is Hertz / point; 11 is the X-axis: the time points of the signal in the time domain, the unit is seconds / point; 12 is the amplitude after the signal is transformed, the unit is volt.

[0094] Example 2

[0095] The specific situation of the micro-Doppler signal parameter estimation technology of rotorcraft UAV based on the hybrid method is as follows: Figure 1 , Figure 6 , Figure 7 The specific implementation steps of the micro-Doppler signal parameter estimation technology for rotor-type UAVs based on the hybrid method in this example are as described in Example 1, a total of ten steps, and the detection process is also as described in Example 1. Figure 1 There are a total of 9 processes described in the example, which will not be repeated. Based on the first embodiment, this example further verifies the detection of low signal-to-noise ratio micro-Doppler signals by the method of the present invention. The difference between the micro-Doppler signal parameter estimation technology of rotorcraft UAV based on the hybrid method in this example and the micro-Doppler signal parameter estimation technology of rotorcraft UAV based on the hybrid method in the first embodiment is that -13 dB Gaussian white noise is added to the target signal in the first step. After processing according to the steps of the first embodiment, Figure 6 is the short-time Fourier transform result, Figure 7 This is the micro-Dop frequency detection result. Figure 2 , Figure 6 The results show that when the signal-to-noise ratio decreases, the micro-Doppler curve becomes blurred. Figure 3 , Figure 7 As a result, it can be concluded that the estimated value of the micro-Doppler frequency is still the same, which verifies the effectiveness of the method of the present invention for detecting micro-Doppler signals under low signal-to-noise ratio. The remaining undescribed technology for estimating micro-Doppler signal parameters of rotor-type unmanned aerial vehicles based on the hybrid method is the same as that described in the first embodiment and will not be repeated.

[0096] Example 3

[0097] The specific situation of the micro-Doppler signal parameter estimation technology of rotorcraft UAV based on the hybrid method is as follows: Figure 1 , Figure 8 , Fig. 9 , Fig.10 , Fig.11 The specific implementation steps of the micro-Doppler signal parameter estimation technology for rotor-type UAVs based on the hybrid method in this example are as described in Example 1, a total of ten steps, and the detection process is also as described in Example 1. Figure 1 There are a total of 9 processes described in the previous section, which will not be repeated here.

[0098] This example further verifies the detection and parameter estimation of micro-Doppler signals of more than two rotors by the method of the present invention on the basis of the first and second embodiments. The difference between the micro-Doppler signal parameter estimation technology of rotor-type UAV based on the hybrid method in this example and the micro-Doppler signal parameter estimation technology of rotor-type UAV based on the hybrid method in the first and second embodiments is that N=4 in the analyzed signal. After processing according to the steps of the first embodiment, Figure 8 is the short-time Fourier transform result, Fig. 9 is the result of autocorrelation processing, Fig.10 This is the compensation detection result of the rotation frequency of 8 Hz. Fig.11 This is the compensation test result of the rotation frequency of 4 Hz. Figure 8 , Figure 2 The results show that the increase in the number of rotors leads to more serious mutual influence of multiple curves in the micro-Doppler spectrum. Fig. 9 , Figure 4 As a result, it can be concluded that the increase in the number of rotors leads to a smaller interval between adjacent harmonics of the autocorrelation and a larger frequency. Fig. 9 The displayed frequency is 16 Hz, and the estimated rotation frequency is 8 Hz or 4 Hz; Fig.10 , Fig.11 The results are the compensation post-processing results of the rotation frequency of 4 Hz and 8 Hz, respectively. It can be seen that when the compensation result is consistent with the actual value, the location of the target can be obtained using the constant false alarm method, which verifies that the method of the present invention can realize the detection and parameter estimation of multi-rotor micro-Doppler signals. The rest of the micro-Doppler signal parameter estimation technology of the rotor-type UAV based on the hybrid method in this example is the same as that described in Example 1 to Example 2, and will not be repeated.

[0099] Example 4

[0100] The specific situation of the micro-Doppler signal parameter estimation technology of rotorcraft UAV based on the hybrid method is as follows: Figure 1 The specific implementation steps of the micro-Doppler signal parameter estimation technology for rotor-type UAVs based on the hybrid method in this example are as described in Example 1, a total of ten steps, and the detection process is also as described in Example 1. Figure 1 There are a total of 9 processes described in the previous section, which will not be repeated here.

[0101] This example further verifies the detection of multi-component signals by the maximum value detection and constant false alarm detection methods in the method of the present invention on the basis of the first to third embodiments. The micro-Doppler signal parameter estimation technology of rotor-type UAV based on the hybrid method in this example is different from the micro-Doppler signal parameter estimation technology of rotor-type UAV based on the hybrid method in the first to third embodiments: 1. In the step 1, the number of target rotors is 2, the rotor length is 0.3 meters, the wavelength is 0.03 meters, and the added Gaussian white noise is 0 decibels. 2. The length of the window function in the step 2 is 64. Fig.12 is the short-time Fourier transform result, Fig.13 This is the result after compensation. Fig.12 , Figure 2 The results show that when the rotor length is shortened, the micro-Doppler frequency becomes smaller. Fig.13 , Figure 5 The results show that when the compensation parameters are matched, the obtained spectrum graph structure is consistent, which verifies that the method of the present invention can realize the detection and parameter estimation of targets with different rotor lengths. The remaining parameters of the micro-Doppler signal parameter estimation technology of rotor-type UAV based on the hybrid method in this example are the same as those described in Examples 1 to 3, and will not be repeated.

[0102] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for estimating micro-Doppler signal parameters of a rotary-wing UAV based on a hybrid method, characterized in that: It includes the following steps: S1: Collect the UAV micro-Doppler echo signal and express it in the form of exponential function s(t), which is expressed as: Wherein, in formula (1), N is the number of UAV rotors, ξ is the echo signal amplitude in volts, L is the length of the UAV rotor in meters, β is the angle between the radar antenna and the UAV rotor in radians, v0 is the initial speed of the UAV in meters per second, and f r is the rotation frequency of the drone rotor in Hertz, t is the signal duration in seconds, φ i is the initial phase of the i-th rotor of the UAV, in radians, λ is the signal wavelength, in meters, and w(t) is the Gaussian white noise signal; S2: Perform short-time Fourier transform on the echo signal s(t) in step 1. The formula is: Wherein, ω(t) in formula (2) is the window function, and * represents the conjugate operator symbol; S3: Perform Fourier transform on formula (2) in S2 with respect to time t, and the formula is: S'(f t ,f)=FFT t [S(t,f)] (3) S4: Search for the maximum value of the frequency f dimension around the origin of the coordinate axis in formula (3), and record the obtained frequency range as [f a , f b ], the maximum value search formula is: Among them, f in formula (4) a , f b They represent the minimum and maximum values ​​of the UAV micro-Doppler frequency after the maximum value search, respectively, in Hertz; S5: Perform autocorrelation operation on formula (1) in S1 to obtain the frequency f between adjacent harmonics ab , the autocorrelation formula is: y(t)=xcorr[s(t),s(t)] (5) Wherein, xcorr in formula (5) represents the mathematical symbol of autocorrelation operation, and y(t a ), y(t b ) represent the time points corresponding to adjacent harmonics in y(t); S6: According to the frequency f of the adjacent harmonic point ab , obtain the estimated rotation frequency of the drone and record the estimated value as The formula for obtaining is: Wherein, N' in formula (7) is the estimated number of UAV rotors; S7: The minimum and maximum values ​​of the UAV micro-Doppler frequency f obtained according to formula (4) in S4 a , f b Get the estimated length of the number of drone rotors f L , the calculation formula is: S8: Estimated UAV rotation frequency obtained according to formula (7) and formula (8) And the estimated length of the UAV rotor f L , perform parameter compensation operation on formula (1) and perform specific target detection, that is: Among them, φ in formula (9) x is the initial phase compensation estimate in radians; S9: Perform Fourier transform on s'(t) in step 8, and select the maximum value of the estimated rotation frequency of the drone and the estimated rotor length of the drone. The formula is: Among them, A in formula (10) a , A b They represent the time dimension and frequency dimension positions corresponding to the maximum value of the UAV target signal, respectively, and f′ L ∈[f L -0.1f L ,f L +0.1f L ], S10: For the signal at the maximum value position in S9 formula (10), a judgment threshold is set using the constant false alarm method to detect the target signal. When the amplitude of the judged target signal is greater than or equal to this threshold, it is considered to be a target signal. At this time, the estimated number of UAV rotors and the estimated length of UAV rotors in s'(t) in formula (10) are the target parameters required. When the amplitude of the judged target signal is less than this threshold, it is considered that the target signal is a false target and the corresponding parameters are not established.

2. The method for estimating micro-Doppler signal parameters of a rotary-wing UAV as claimed in claim 1, characterized in that: The value range of the drone rotor number estimate N' in step S6 is the commonly used value of the drone rotor number. According to different drone rotor number estimates, four groups of different drone rotation frequencies and drone rotor length estimates are obtained, and the maximum value detection is performed on different groups of data to find the target signal.

3. The method for estimating micro-Doppler signal parameters of a rotary-wing UAV as claimed in claim 1, wherein f′ in step S9 L The step interval in is 0.1f L , The step interval is φ x The step interval in is 0.

001.

4. The micro-Doppler signal parameter estimation method for a rotary-wing UAV as claimed in claim 1, wherein the constant false alarm rate p is the constant false alarm rate. fa =10 -3 .

5. The method for estimating micro-Doppler signal parameters of a rotary-wing UAV as claimed in claim 1, characterized in that: The formula (1) in step S1 is an exponential function containing sinusoidal frequency modulation.

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