Method for diagnosing attitude of projectile in initial trajectory based on RCS (radar cross section)

Abstract

The invention relates to the field of muzzle initial velocity radars, in particular to an initial trajectory projectile attitude diagnosis method based on RCS, which is suitable for performing projectile flight attitude discrimination by extracting RCS features of a projectile after initial trajectory echo data of the projectile is collected by an initial velocity radar, and then diagnosing the health state of the initial trajectory of the projectile. According to the method, the initial trajectory RCS characteristics of the projectile can be provided, and a basis is provided for judging whether the initial flight state is healthy or not. The method is improved on the basis of the existing muzzle initial speed radar, and extra hardware equipment does not need to be deployed.

Description

Technical Field

[0001] The present invention relates to the field of muzzle velocity radars, and particularly to a method for diagnosing the attitude of a projectile in the initial trajectory based on RCS. It is applicable to diagnosing the health state of the initial trajectory of a projectile by extracting the RCS characteristics of the projectile after the muzzle velocity radar collects the echo data of the initial trajectory of the projectile, and then judging the flight attitude of the projectile. Background Art

[0002] Muzzle velocity radars are usually used to measure the muzzle velocity of projectiles in range firing tests, and then used to correct the muzzle velocity deviation, study the method of setting up gun positions, explore the law of cold gun errors, and determine the barrel life according to the muzzle velocity reduction. The initial flight state of the projectile has a great influence on the shooting accuracy, but only the muzzle velocity data of the projectile is not sufficient to diagnose the health state of the initial trajectory of the projectile. Therefore, the problems to be solved are what features to select and how to extract the selected features from the signals containing feature information. Summary of the Invention

[0003] In view of this, the present invention proposes a method for diagnosing the attitude of a projectile in the initial trajectory based on RCS to solve the problems in the background art.

[0004] The radar cross section (RCS) of a projectile is an equivalent area reflecting the relationship between the power density irradiated on the projectile and the reflected power. It is related to the angle of the radar beam irradiating the projectile and reflects the attitude of the projectile relative to the radar beam.

[0005] After the muzzle velocity radar collects the echo data of the initial trajectory of the projectile, it can directly measure the projectile velocity, and then obtain the flight distance of the projectile by integrating the projectile velocity curve. The echo signal-to-noise ratio is obtained through constant false alarm processing, and then the RCS characteristics of the projectile are inversely calculated through the radar equation, providing a basis for judging the initial flight state of the projectile.

[0006] The problems to be solved by the present invention are realized as follows:

[0007] A method for diagnosing the attitude of a projectile in the initial trajectory based on RCS includes the following processes:

[0008] Step 1: Divide the echo signal into multiple time intervals through short-time Fourier transform, and analyze each time interval using Fourier transform to determine the frequency components existing in each time interval, obtaining a time-frequency diagram;

[0009] Step 2: Adopt cell-averaging constant false alarm processing, set guard cells around the cell to be detected, calculate the noise power and decision threshold by taking several reference cells outside the guard cells, and screen the target echo in the time-frequency diagram according to the decision threshold to obtain the distribution of the projectile velocity and obtain the echo signal-to-noise ratio r SNR; wherein, the unit to be detected is the unit that currently needs to be judged whether it contains the target; the protection unit is several units around the unit to be detected, and is used to isolate the detection unit and the reference unit; the reference unit is multiple units outside the protection unit, and is used to estimate the background noise.

[0010] Step 3, perform least squares fitting on the projectile velocity to obtain the fitted motion parameters.

[0011] Step 4, integrate the projectile velocity obtained after fitting in Step 3 to obtain the projectile flight distance.

[0012] Step 5, describe the relationship between the echo signal-to-noise ratio and the projectile RCS characteristics by the radar equation, that is

[0013] Then the projectile RCS is:

[0014]

[0015] In the formula, σ is the projectile RCS, r SNR is the echo signal-to-noise ratio, P av is the average transmit power, G t , G r are the transmit gain and the receive gain respectively, λ is the wavelength, k is the Boltzmann constant, T sys is the system noise temperature, L is the system loss, T a is the integration time, and R is the projectile distance.

[0016] Step 6, repeat Steps 1 to 5 to obtain the projectile RCS curve, and judge the projectile flight attitude according to the projectile RCS curve.

[0017] Further, the specific process of Step 2 is:

[0018] Record the quantization signal of the frequency component STFT(t,f) obtained in Step 1 as S m,n , m = 0, 1, …, M - 1, n = 0, 1, …, N - 1, M is the number of time units, N is the number of frequency units, then for each m and n in turn, calculate:

[0019]

[0020] If |S m,n | 2 ≥ εD m,n , then record S m,n corresponding to the speed V = λf / 2 and the signal-to-noise ratio r SNR = |S m,n | 2 / D m,n where, N pro is the number of protection units, Nref where \(N\) is the number of reference units and \(\lambda\) is the wavelength of the radar transmitted signal.

[0021] Further, the specific process of step 3 is as follows:

[0022] Write the relationship between the velocity and the motion parameters in matrix form, i.e.,

[0023] \(\mathbf{V}=\mathbf{H}\mathbf{x}+\Delta\)

[0024] where

[0025] \(\mathbf{x} = [v, a, J, \cdots]^T\) T

[0026] \(\mathbf{V} = [V_1, V_2, \cdots, V_n]^T\) M \(]^T\) T

[0027]

[0028] In the formula, \(V_1, V_2, \cdots, V_n\) M are the projectile velocities calculated at times \(t_1, t_2, \cdots, t_n\), \(\Delta\) is the estimation error, \(v\) is the initial velocity of the projectile, \(a\) is the acceleration, and \(J\) is the jerk; M The motion parameters are obtained by minimizing the fitting mean square error, and the fitting mean square error is:

[0029]

[0030]

[0031] Then the motion parameters obtained by least squares fitting are:

[0032]

[0033] The advantages of the present invention compared with the prior art are:

[0034] (1) The present invention can provide the RCS characteristics of the initial trajectory of the projectile, providing a basis for judging whether the initial flight state is healthy.

[0035] (2) The present invention is based on the existing muzzle velocity radar and is improved, without the need for additional hardware equipment deployment. Brief Description of the Drawings

[0036] Figure 1 is the flowchart of the present invention.

[0037] Figure 2 is the time-frequency diagram of the initial trajectory of a projectile with normal attitude.

[0038] Figure 3 is the time-frequency diagram of the initial trajectory of a projectile with abnormal attitude.

[0039] Figure 4It is the initial ballistic velocity curve of a projectile with normal attitude.

[0040] Figure 5 It is the initial ballistic velocity curve of a projectile with abnormal attitude.

[0041] Figure 6 It is the initial ballistic RCS characteristic curve of a projectile with normal attitude.

[0042] Figure 7 It is the initial ballistic RCS characteristic curve of a projectile with abnormal attitude. Specific implementation mode

[0043] A method for diagnosing the attitude of a projectile in the initial ballistic based on RCS includes the following steps:

[0044] Step 1: Divide the echo signal into multiple time intervals through short-time Fourier transform (STFT), and use Fourier transform to analyze each time interval to determine the frequency components existing in each time interval, and obtain a time-frequency diagram; that is

[0045]

[0046] In the formula, t and f respectively represent time and frequency, s(τ) is the echo signal to be analyzed, and g(τ) is the window function used to divide the time interval.

[0047] Step 2: Adopt cell-averaging constant false alarm rate processing, set guard cells around the cell to be detected, and calculate the noise power and decision threshold by taking several reference cells outside the guard cells, and screen the target echo in the time-frequency diagram according to the decision threshold to obtain the distribution of the projectile velocity, and obtain the echo signal-to-noise ratio r SNR ; among them, the cell to be detected is the cell that needs to judge whether it contains a target currently; the guard cell is several cells around the cell to be detected, used to isolate the detection cell and the reference cell; the reference cell is multiple cells outside the guard cell, used to estimate the background noise;

[0048] Record the quantization signal of the frequency component STFT(t,f) obtained in step 1 as S m,n , m = 0, 1,..., M - 1, n = 0, 1,..., N - 1, M is the number of time units, N is the number of frequency units, then for each m and n in turn, calculate:

[0049]

[0050] If |S m,n | 2 ≥ εD m,n , then record S m,n The corresponding velocity V = λf / 2 and signal-to-noise ratio r SNR = |S m,n | 2 / D m,n , where N pro is the number of protection units, and N ref is the number of reference units, and λ is the wavelength of the radar transmitted signal.

[0051] Step 3: Smooth the projectile velocity using least squares fitting, and write the relationship between the velocity and the motion parameters in matrix form, i.e.,

[0052] V = Hx + Δ

[0053] where

[0054] x = [v, a, J, …] T

[0055] V = [V1, V2, …, V M T

[0056]

[0057] In the formula, V1, V2, …, V M are the projectile velocities calculated at times t1, t2, …, t M , Δ is the estimation error, v is the initial velocity of the projectile, a is the acceleration, and J is the jerk;

[0058] The fitting mean square error is:

[0059]

[0060] Let the fitting mean square error be minimized, then the motion parameters obtained by least squares fitting are:

[0061]

[0062] Step 4: Integrate the projectile velocity to obtain the projectile flight distance:

[0063]

[0064] In the formula, v(τ) is the projectile velocity fitting value in a time interval obtained in Step 3.

[0065] Step 5: Describe the relationship between the echo signal-to-noise ratio and the projectile RCS characteristics using the radar equation, i.e.,

[0066]

[0067] Then the projectile RCS is:

[0068]

[0069] In the formula, σ is the projectile RCS, r​SNR is the echo signal-to-noise ratio, P av is the average transmit power, G t , G r are the transmit gain and receive gain respectively, λ is the wavelength, k is the Boltzmann constant, T sys is the system noise temperature, L is the system loss, T a is the integration time, and R is the projectile distance.

[0070] Step 6: Repeat Steps 1 to 5, and the RCS curve of the projectile can be obtained. The projectile flight distance obtained in Step 4 is an accumulation process. According to the RCS curve, the flight attitude of the projectile can be judged. If the fluctuation of the RCS curve is not obvious, it means that the overall attitude of the projectile is stable; if the fluctuation of the RCS curve is relatively obvious, it means that the attitude of the projectile is unstable.

[0071] In the embodiment, taking the analysis results of two actual measurement data as an example, for the time-frequency diagram of the initial trajectory of the projectile as shown in Figure 2 , Figure 3 , after constant false alarm processing, the initial trajectory velocity curve of the projectile is as shown in Figure 4 , Figure 5 ; the results of the present invention are as shown in Figure 6 , Figure 7 . After velocity integration, according to the radar equation, the RCS characteristic curve of the initial trajectory of the projectile is obtained. From the RCS fluctuation curve, Figure 6 the overall attitude of the projectile in Figure 7 is stable, the attitude of the projectile in has obvious fluctuations in the first half, and weakens but is still unstable in the second half, which is consistent with the actual situation, indicating that the present invention can judge the flight attitude of the projectile by extracting the RCS characteristics of the projectile, and further diagnose the health state of the initial trajectory of the projectile.

Claims

1. A method for diagnosing the attitude of a projectile in the initial trajectory based on RCS, characterized in that, It includes the following processes: Step 1: Divide the echo signal into multiple time intervals through short-time Fourier transform, and use Fourier transform to analyze each time interval to determine the frequency components existing in each time interval, and obtain a time-frequency diagram; Step 2: Perform cell averaging constant false alarm rate (CA-CFAR) processing. Set guard cells around the cell to be detected, calculate the noise power and decision threshold by taking several reference cells outside the guard cells, screen the target echoes in the time-frequency diagram based on the decision threshold to obtain the distribution of the projectile velocity, and obtain the echo signal-to-noise ratio r SNR ; where the cell to be detected is the cell that currently needs to be judged whether it contains a target; the guard cells are several cells around the cell to be detected, used to isolate the detection cell and the reference cells; the reference cells are multiple cells outside the guard cells, used to estimate the background noise Step 3: Smooth the projectile velocity by using least squares fitting to obtain the fitted motion parameters; Step 4: Integrate the projectile velocity obtained after fitting in Step 3 to obtain the flight distance of the projectile; Step 5, describe the relationship between the echo signal-to-noise ratio and the projectile RCS characteristics using the radar equation, i.e., Then the RCS of the projectile is: Where, σ is the RCS of the projectile, r SNR is the echo signal-to-noise ratio, P av is the average transmit power, G t and G r are the transmit gain and receive gain respectively, λ is the wavelength, k is the Boltzmann constant, T sys is the system noise temperature, L is the system loss, T a is the integration time, R is the distance of the projectile; Step 6: Repeat Steps 1 to 5 to obtain the RCS curve of the projectile, and judge the flight attitude of the projectile according to the RCS curve of the projectile.

2. The method for diagnosing the attitude of a projectile in the initial trajectory based on RCS according to claim 1, characterized in that, The specific process of Step 2 is: Denote the quantized signal of the frequency component STFT(t, f) obtained in step 1 as S m,n , where m = 0, 1, …, M - 1, n = 0, 1, …, N - 1, M is the number of time units, and N is the number of frequency units. Then, for each m and n in sequence, calculate: If |S m,n | 2 ≥εD m,n , record S m,n corresponding to the speed V = λf / 2 and the signal-to-noise ratio r SNR = |S m,n | 2 / D m,n , where N pro is the number of protection units, N ref is the number of reference units, and λ is the wavelength of the radar transmitted signal.

3. A method for diagnosing the attitude of a muzzle ballistic projectile based on RCS according to claim 1, characterized in that The specific process of Step 3 is: Write the relationship between the velocity and the motion parameters in the form of a matrix, that is V = Hx + Δ where x = [v, a, J, …] T V = [V1, V2, …, V M T ​ Where V1, V2, …, V M are the projectile velocities calculated at times t1, t2, …, t M , Δ is the estimation error, v is the initial velocity of the projectile, a is the acceleration, and J is the jerk; Obtain the motion parameters by minimizing the fitting mean square error, and the fitting mean square error is: Then the motion parameters obtained by using least squares fitting are:

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