Unmanned aerial vehicle target inversion method, system and device under strong sea clutter background based on time-frequency-Doppler domain joint filtering and time-frequency ridge analysis, and medium

Abstract

The invention discloses an unmanned aerial vehicle target inversion method, system and device under a strong sea clutter background based on time-frequency-Doppler domain joint filtering and time-frequency ridge analysis, and a medium, and aims at a linear frequency modulation signal system radar, a multi-rotor unmanned aerial vehicle radar echo model is established, and a multi-rotor unmanned aerial vehicle radar echo is calculated. Actually measured sea surface radar echoes are added into multi-rotor unmanned aerial vehicle radar echoes to form unmanned aerial vehicle radar echoes under a complete strong sea clutter background, namely total radar echoes; performing time-frequency-Doppler domain joint filtering on the total radar echo to obtain a filtered total power spectral density P1; performing time-frequency ridge line extraction on the filtered total power spectral density P1 to verify the fitting degree; the system, the device and the medium are used for bearing and implementing the method. Compared with traditional single-field unmanned aerial vehicle detection, the method can better suppress sea clutters, is wider in application range, and can accurately detect and recognize an unmanned aerial vehicle target in a high-sea-condition environment.

Description

Technical Field

[0001] The present invention belongs to the technical field of ocean data assimilation, and in particular relates to a method, system, equipment and medium for inverting unmanned aerial vehicle targets under a strong sea clutter background based on time-frequency-Doppler domain joint filtering and time-frequency ridge analysis. Background Art

[0002] As one of the typical representatives of low, slow and small targets, the detection and identification of UAVs are seriously affected by various environmental clutter and noise. In addition, their low flight altitude, slow flight speed and small RCS also make UAVs easily obscured by other objects or submerged by clutter, making them difficult to detect by radar. Therefore, establishing an accurate and practical UAV radar echo modulation model in the field of electromagnetic scattering is the premise and basis of research. For sea surface target detection, sea clutter is one of the main factors restricting radar performance. Its complex waveform means that there is currently no unified model that can accurately describe sea clutter. Its non-stationarity and non-Gaussianity make it difficult to suppress sea clutter.

[0003] When using radar to detect low, slow, and small targets on the sea surface, due to its slow speed, the Doppler frequency of the target is easily submerged by sea clutter, and the overall energy of the sea clutter background is strong, resulting in a low signal-to-clutter ratio / signal-to-noise ratio during detection, and the target cannot be effectively detected, which seriously reduces the radar performance and interferes with the normal operation of the radar. When only suppressing sea clutter in a single domain such as time domain and frequency domain, the effect is often poor, and the echo of the submerged low, slow, and small targets cannot be effectively suppressed and detected.

[0004] The paper "Research on UAV Identification Algorithm Based on Micro-Doppler" proposed a joint time-frequency algorithm based on short-time Fourier transform and pseudo-Wigner-Weil distribution (Guo Jia, North China University of Technology, Tangshan 063000). By jointly calculating the short-time Fourier transform with poor time-frequency aggregation and no cross-terms and the pseudo-Wigner-Weil distribution with good time-frequency aggregation and fewer cross-terms, the cross-terms were suppressed while maintaining high time-frequency resolution, thereby effectively detecting UAVs. However, this method only detects UAV targets in a single domain, which is subject to great limitations and a narrow scope of application. Summary of the invention

[0005] In order to overcome the deficiencies of the above-mentioned prior art, the purpose of the present invention is to provide a method, system, device and medium for inversion of UAV targets under strong sea clutter background based on time-frequency-Doppler domain joint filtering and time-frequency ridge analysis, by performing time-domain-frequency-Doppler domain processing on radar target echo data to enhance the target echo energy, and based on the ratio of the mean power spectrum density of clutter and target echo, establish a filter factor calculation model, carry out time-frequency domain filtering, and combine the time-frequency ridge extraction method to obtain the inversion results of UAV motion speed and distance facing the strong sea clutter background. Compared with the traditional single data domain (time domain / frequency domain) UAV detection, the present invention can better suppress sea clutter, has a wider range of application, and realizes UAV target detection and recognition under high sea conditions.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] A method for inverting UAV targets in a strong sea clutter background based on time-frequency-Doppler domain joint filtering and time-frequency ridge analysis includes the following steps:

[0008] Step 1: For the linear frequency modulation signal system radar, a multi-rotor UAV radar echo model is established, and the multi-rotor UAV radar echo is calculated. The measured sea surface radar echo is added to the multi-rotor UAV radar echo to form a complete UAV radar echo under the strong sea clutter background, that is, the total radar echo;

[0009] Step 2: Perform a time-frequency-Doppler domain joint filtering on the total radar echo obtained in step 1 to obtain the total power spectral density P after filtering. l ;

[0010] Step 3: The total power spectrum density P after filtering obtained in step 2 l Time-frequency ridge extraction was performed to verify the goodness of fit.

[0011] The specific method of step 1 is:

[0012] Step 1.1: The time domain expression of the linear frequency modulation signal as the transmitted signal is as follows:

[0013]

[0014] Where A0 is the initial amplitude of the transmitted signal, f0 is the carrier frequency, B is the FM bandwidth, T is the FM period, k=B / T is the FM slope, and φ0 is the initial phase;

[0015] The time domain expression of the target echo signal is:

[0016]

[0017] Among them, τ is the delay;

[0018] Step 1.2: Subtract the phase of the transmitted signal in step 1.1 from the phase of the target echo signal, and the baseband signal phase obtained is:

[0019] P b (t)=2π(f0τ(t)+ktτ(t)-kτ 2 (t) / 2)

[0020] Step 1.3: The beat signal is further obtained from the baseband signal phase in step 1.2:

[0021]

[0022] Step 1.4: Express the distance from the scattering point Q of a blade on a certain rotor of the drone to the radar as:

[0023] R Q =R0+vt+l Q cosβcos(θ0+W m t-α)

[0024] Among them, R0 is the initial slant distance of the target, v is the speed of the UAV, l Q is the distance between the scattering point Q and the blade rotation center, β is the pitch angle, θ0 is the initial rotation angle of the blade, W m is the angular velocity of the mth rotor, α is the azimuth angle;

[0025] Step 1.5: Substitute the distance RQ from the scattering point Q of a blade on a rotor of the drone obtained in step 1.4 to the radar into the echo delay τ = 2R Q / c, where c is the speed of light, and combined with the beat signal obtained in step 1.3, integrated over the entire blade length L, the radar echo expression of the multi-rotor drone is obtained:

[0026]

[0027] Where M is the number of rotors, N is the number of blades on a rotor, and σ mirco is the scattering coefficient of the blade, σ main is the scattering coefficient of the drone body.

[0028] The specific method of step 2 is:

[0029] Step 2.1: Perform clutter suppression on the total radar echo in the slow time dimension of the time domain, filter out the stationary clutter, and retain the moving target to indicate the moving target; the corresponding filter impulse response of the moving target indication is:

[0030]

[0031] Where J is the number of pulse cancellations; the output signal is:

[0032] y1(t)=r(t)*hJ(q)

[0033] Where r(t) is the total radar echo;

[0034] Step 2.2: Set the impulse response function of the matched filter:

[0035] h(t)=k0s * (t0-t)

[0036] Where k0 is a constant not equal to 0, s(t) is the target signal; the output signal y1(t) obtained in step 2.1 is subjected to matched filtering, and the output signal is:

[0037]

[0038] Step 2.3: Perform short-time Fourier transform on the output signal y2(t) obtained in step 2.2:

[0039] STFT(T,F)=∫y2(r)w(rT)e -j2πFr dr

[0040] Among them, w(×) is the window function, and the power spectral density of the transformed signal is:

[0041] P(T,F)=k(T)|STFT(T,F)| 2

[0042]

[0043] Among them, k(T) is the transformation coefficient to compensate for the influence of the window function, f s is the sampling rate when performing short-time Fourier transform;

[0044] Step 2.4: Find the extreme value of the power spectrum density calculated in step 2.3, and select the power spectrum density at the non-extreme frequency to find the average value in different time dimensions as the power spectrum density mean of the clutter, that is:

[0045] P1=P(T1,F1) / L T

[0046] Among them, L T is the time length, and the power spectrum density at the maximum frequency is selected to find the average value in different time dimensions as the target power spectrum density mean, that is:

[0047] P2=max(P(T,F)) / L T

[0048] Step 2.5: Take the ratio of the mean power spectral density of the clutter obtained in step 2.4 to the mean power spectral density of the target as the time-frequency filter factor γ, and establish a filter factor calculation model, that is:

[0049] γ=P1 / P2

[0050] The total power spectrum density is subjected to time-frequency filtering, and the specific expression is:

[0051] P l ={P[P(T,F)<γ*max(P(T,F))]=0}

[0052] At this point, the time-frequency-Doppler domain joint filtering of the total radar echo is achieved.

[0053] The specific method of step 3 is:

[0054] The ratio of the mean power spectral density of the clutter obtained in step 2.4 to the mean power spectral density of the target is used as the time-frequency filtering factor, the ridges of the time-frequency graph are extracted, the ridges of the UAV time-frequency graph are compared with the ridges of the UAV time-frequency graph under the sea clutter background, and their mean fitting graph is calculated.

[0055] The present invention also provides a UAV target inversion system under strong sea clutter background based on time-frequency-Doppler domain joint filtering and time-frequency ridge analysis, comprising:

[0056] The UAV radar echo calculation module is used to establish a multi-rotor UAV radar echo model for the linear frequency modulation signal system radar and calculate the multi-rotor UAV radar echo;

[0057] The total radar echo filtering module is used to combine the UAV radar echo with the measured sea surface radar echo to form a complete UAV radar echo under the background of strong sea clutter, that is, the total radar echo, and perform time-frequency-Doppler domain joint filtering on the total radar echo to obtain the total power spectral density P after filtering. l ;

[0058] The time-frequency ridge extraction module is used to extract the total power spectrum density P after filtering. l Time-frequency ridge extraction was performed to verify the goodness of fit.

[0059] The present invention also provides a UAV target inversion device under strong sea clutter background based on time-frequency-Doppler domain joint filtering and time-frequency ridge analysis, comprising:

[0060] Memory: a computer program for storing the above-mentioned method for inverting UAV targets in a strong sea clutter background based on time-frequency-Doppler domain joint filtering and time-frequency ridgeline analysis, which is a computer-readable device;

[0061] Processor: used to implement the UAV target inversion method under strong sea clutter background based on time-frequency-Doppler domain joint filtering and time-frequency ridge analysis when executing the computer program.

[0062] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it can implement the method for inverting UAV targets in a strong sea clutter background based on time-frequency-Doppler domain joint filtering and time-frequency ridge analysis.

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] 1. The present invention proposes a multi-dimensional UAV target motion state inversion method under strong sea clutter background. By performing time-frequency-Doppler domain joint filtering on the signal, the limitations of traditional filtering methods in UAV detection under strong sea clutter conditions are improved, the suppression effect of sea clutter is enhanced, and the distinction between UAV targets and sea clutter in a multi-dimensional field is improved.

[0065] 2. The present invention uses the ratio of the mean power spectral density of clutter to the mean power spectral density of the target as the time-frequency filtering factor to perform time-frequency filtering and ridge extraction, thereby enhancing the filtering effect on sea clutter. The mean fitting of the time-frequency ridge envelope verifies the accuracy of this method in inverting the motion state of the UAV.

[0066] In summary, the present invention effectively suppresses sea clutter and significantly enhances the target echo energy by performing multi-dimensional collaborative filtering in the time domain, frequency domain and Doppler domain on the radar target echo data, and establishes a filter factor calculation model through the ratio of the mean power spectral density of the clutter and the target echo. The time-frequency domain filtering is carried out, and the ridge extraction method is combined to verify that the inversion of the movement speed and distance of the UAV under the background of strong sea clutter by this method is more accurate. Compared with the traditional single data domain (time domain / frequency domain) UAV detection, this method can better suppress sea clutter, has a wider range of application, and can accurately detect and identify UAV targets, providing a better simulation platform for the research on UAV detection in a strong clutter environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a flow chart of the implementation method of the present invention.

[0068] Figure 2 This is the electromagnetic modeling simulation diagram of the drone of the present invention; wherein, Figure 2 (a) is the time domain echo map of the UAV radar. Figure 2 (b) is the filtered UAV radar time domain echo map. Figure 2 (c) is the time-frequency diagram of the drone. Figure 2 (d) is the filtered UAV time-frequency diagram. Figure 2(e) is the Doppler spectrum of the UAV, Figure 2 (f) is the Doppler spectrum of the UAV after filtering.

[0069] Figure 3 This is a diagram combining the simulated UAV echo and the measured sea surface data of the present invention; wherein, Figure 3 (a) is the pulse-range diagram of the UAV against the sea clutter background. Figure 3 (b) is the pulse-range diagram of the UAV against the sea clutter background after filtering. Figure 3 (c) is the time-frequency diagram of the UAV against the sea clutter background. Figure 3 (d) is the time-frequency diagram of the UAV against the sea clutter background after filtering. Figure 3 (e) is the Doppler spectrum of the UAV against the sea clutter background. Figure 3 (f) is the Doppler spectrum of the UAV after filtering against the sea clutter background.

[0070] Figure 4 It is a comparison diagram of the time-frequency ridge envelope of the UAV of the present invention and the time-frequency ridge envelope of the UAV under the sea clutter background.

[0071] Figure 5 : is a comparison diagram of the time-frequency ridge envelope mean fitting of the UAV of the present invention and the time-frequency ridge envelope mean fitting of the UAV under the sea clutter background; wherein, Figure 5 (a) is the upper envelope comparison diagram, Figure 5 (b) Lower envelope comparison diagram. DETAILED DESCRIPTION

[0072] The technical solution adopted by the present invention is further described below in conjunction with the accompanying drawings and specific embodiments.

[0073] The present invention performs electromagnetic modeling of UAV radar echoes based on the radar target echo theory under the linear frequency modulation signal system and the characteristics of multi-rotor UAVs; the radar echo of the UAV obtained by simulation is combined with the measured sea surface data and subjected to time-frequency-Doppler domain joint filtering; the ratio of the power spectral density mean of the clutter to the power spectral density mean of the target is used as the time-frequency filtering factor to extract the time-frequency ridges. The ridges of the UAV time-frequency graph are compared with the ridges of the UAV time-frequency graph under the background of sea clutter, and their mean fitting is calculated to invert and verify the movement speed and distance of the UAV target. Compared with traditional single-domain UAV detection, the present invention can better suppress sea clutter, has a wider range of application, and can accurately detect and identify UAV targets in high sea conditions.

[0074] like Figure 1 As shown, a method for inverting UAV targets in a strong sea clutter background based on time-frequency-Doppler domain joint filtering and time-frequency ridge analysis includes the following steps:

[0075] Step 1: Based on electromagnetic scattering theory, a multi-rotor UAV radar echo model is established for linear frequency modulation signal system radar, and the multi-rotor UAV radar echo is calculated. The measured sea surface radar echo is added to the multi-rotor UAV radar echo to form a complete UAV radar echo under the background of strong sea clutter, that is, the total radar echo;

[0076] The specific method of step 1 is:

[0077] Step 1.1: The time domain expression of the linear frequency modulation signal as the transmitted signal is as follows:

[0078]

[0079] Where A0 is the initial amplitude of the transmitted signal, f0 is the carrier frequency, B is the FM bandwidth, T is the FM period, k=B / T is the FM slope, and φ0 is the initial phase;

[0080] The time domain expression of the target echo signal is:

[0081]

[0082] in, τ For time delay;

[0083] Step 1.2: Subtract the phase of the transmitted signal in step 1.1 from the phase of the target echo signal, and the baseband signal phase obtained is:

[0084] P b (t)=2π(f0τ(t)+ktτ(t)-kτ 2 (t) / 2)

[0085] Step 1.3: The beat signal is further obtained from the baseband signal phase in step 1.2:

[0086]

[0087] Step 1.4: Express the distance from the scattering point Q of a blade on a certain rotor of the drone to the radar as:

[0088] R Q =R0+vt+l Q cosβcos(θ0+W m t-α)

[0089] Where R0 is the initial slant distance of the target, v is the speed of the drone, lQ is the distance between the scattering point Q and the blade rotation center, β is the pitch angle, θ0 is the initial rotation angle of the blade, W m is the angular velocity of the mth rotor, α is the azimuth angle;

[0090] Step 1.5: Substitute the distance R from the scattering point Q of a blade on a rotor of the drone obtained in step 1.4 to the radar Q Substitute the echo delay τ = 2R Q / c, where c is the speed of light, and combined with the beat signal obtained in step 1.3, integrated over the entire blade length L, the radar echo expression of the multi-rotor drone is obtained:

[0091]

[0092] Where M is the number of rotors, N is the number of blades on a rotor, and σ mirco is the scattering coefficient of the blade, σ main is the scattering coefficient of the drone body.

[0093] The radar echo expression of a multi-rotor UAV includes the micro-motion term of the blades on the UAV rotor, as well as the distance and speed terms of the UAV body. The scattering coefficient of the UAV body is 100-300 times that of its blades, which means that the scattering of the UAV body will mask the micro-Doppler caused by the rotation of its blades to a certain extent. For example, Figure 2 (a) Figure 2 (c) and Figure 2 As shown in (e), only the echo generated by the drone body close to zero frequency can be seen in both the time-frequency diagram and the Doppler spectrum; after the joint filtering operation in the time-frequency-Doppler domain, as shown in Figure 2 (b) Figure 2 (d) and Figure 2 As shown in (f), both the time-frequency diagram and the Doppler spectrum show the Doppler broadening caused by the rotation of the UAV rotor blades, and the micro-Doppler of the blades can be reproduced.

[0094] right Figure 2 (a) to Figure 2 (f) Analysis shows that the period of the signal peak in the radar time domain echo is consistent with the preset period of rotation of the UAV blades, the Doppler broadening in the UAV time-frequency diagram is consistent with the Doppler broadening caused by the rotation of the rotor blades of the preset length, and the Doppler frequency shift in the Doppler spectrum is consistent with the frequency shift caused by the movement speed of the UAV body.

[0095] Step 2: Perform a time-frequency-Doppler domain joint filtering on the total radar echo obtained in step 1 to obtain the total power spectral density P after filtering. l ;

[0096] The specific method of step 2 is:

[0097] Step 2.1: Perform clutter suppression on the total radar echo in the slow time dimension of the time domain, filter out the stationary clutter, and retain the moving target to indicate the moving target; the corresponding filter impulse response of the moving target indication is:

[0098]

[0099] Where J is the number of pulse cancellations; the output signal is:

[0100] y1(t)=r(t)*h J (q)

[0101] Where r(t) is the total radar echo;

[0102] Step 2.2: In order to maximize the filter output signal-to-noise ratio at a specific time, set the impulse response function of the matched filter:

[0103] h(t)=k0s * (t0-t)

[0104] Where k0 is a constant not equal to 0, s(t) is the target signal; the output signal y1(t) obtained in step 2.1 is subjected to matched filtering, and the output signal is:

[0105]

[0106] Step 2.3: In order to analyze the drone radar echo with non-stationary signal characteristics, the output signal y2(t) obtained in step 2.2 is short-time Fourier transformed:

[0107] STFT(T,F)=∫y2(r)w(rT)e -j2πFr dr

[0108] Among them, w(×) is the window function, and the power spectral density of the transformed signal is:

[0109] P(T,F)=k(T)|STFT(T,F)| 2

[0110]

[0111] Among them, k(T) is the transformation coefficient to compensate for the influence of the window function, f s is the sampling rate when performing short-time Fourier transform;

[0112] Step 2.4: Find the extreme value of the power spectrum density calculated in step 2.3, and select the power spectrum density at the non-extreme frequency to find the average value in different time dimensions as the power spectrum density mean of the clutter, that is:

[0113] P1=P(T1,F1) / L T

[0114] Among them, L Tis the time length, and the power spectrum density at the maximum frequency is selected to find the average value in different time dimensions as the target power spectrum density mean, that is:

[0115] P2=max(P(T,F)) / L T

[0116] Step 2.5: Take the ratio of the mean power spectral density of the clutter obtained in step 2.4 to the mean power spectral density of the target as the time-frequency filter factor γ, and establish a filter factor calculation model, that is:

[0117] γ=P1 / P2

[0118] The total power spectrum density is subjected to time-frequency filtering, and the specific expression is:

[0119] P l ={P[P(T,F)<γ*max(P(T,F))]=0}

[0120] At this point, the time-frequency-Doppler domain joint filtering of the total radar echo is achieved.

[0121] The measured sea surface data comes from the public data set of the Radar Journal and is obtained by the sea detection test of the Information Fusion Institute of the Naval Aviation University. The test used an X-band solid-state power amplifier surveillance / navigation radar, which was set up on Yangma Island in Yantai, to detect the sea surface and sea surface targets (channel buoys, ships, etc.).

[0122] Figure 3 (a) Figure 3 (c) and Figure 3 (e) shows the pulse-range diagram, time-frequency diagram, and Doppler spectrum obtained when the UAV data under strong sea clutter background is not filtered. Figure 3 (b) Figure 3 (d) and Figure 3 (f) is the pulse-range diagram, time-frequency diagram, and Doppler spectrum after joint filtering in the time-frequency-Doppler domain. Among them, the sea clutter data is level 3-4 sea conditions. Figure 3 (a) Figure 3 (c) and Figure 3 In (e), because the clutter is not suppressed, a buoy and an island echo can be seen in the sea clutter data, which are manifested as strong clutter at range unit 1762 and 2100-2800 in the pulse-range diagram, and strong clutter close to zero frequency in the time-frequency diagram and Doppler spectrum. The buoy is located 4.84 km away and the island is located 6 km behind.

[0123] and Figure 3 (b) Figure 3 (d) and Figure 3In (f), the clutter is suppressed after the time-frequency-Doppler domain joint filtering, and the UAV target can be clearly confirmed in all three figures. Among them, the UAV target is located near the distance dimension of 2000 in the pulse-range diagram, and only a small part of the clutter can be seen in the low-range dimension; the UAV target is located at a frequency of 633Hz in the time-frequency diagram, and the strong sea clutter close to zero frequency is suppressed by filtering; the position of the UAV in the Doppler spectrum is 5.37km, the speed is 10m / s, and the buoy and island echoes close to zero frequency are suppressed.

[0124] Through Figure 3 Analysis shows that if the UAV data under the background of strong sea clutter is not processed, only some UAV target echoes can be seen in the Doppler domain, and their energy intensity is similar to that of clutter, making it difficult to distinguish. After the UAV target is processed by joint filtering in the time-frequency-Doppler domain, the echo energy of the UAV target is more concentrated and easier to distinguish from the clutter. The UAV target can be clearly confirmed in both the pulse-range diagram and the time-frequency diagram.

[0125] Step 3: The total power spectrum density P after filtering obtained in step 2 l Time-frequency ridge extraction was performed to verify the goodness of fit.

[0126] The specific method of step 3 is:

[0127] The ratio of the mean power spectral density of the clutter obtained in step 2.4 to the mean power spectral density of the target is used as the time-frequency filtering factor, the ridges of the time-frequency graph are extracted, the ridges of the UAV time-frequency graph are compared with the ridges of the UAV time-frequency graph under the sea clutter background, and their mean fitting graph is calculated.

[0128] like Figure 4 As shown in the figure, the upper and lower envelope trends of the two are the same, and the difference only fluctuates around 10Hz. The upper and lower envelopes are further analyzed in the form of mean fitting, as shown in the figure. Figure 5 As shown, the trend is basically fitted and in line with expectations, which verifies that the filtering method of the present invention has a good suppression effect on sea clutter and an accurate inversion of UAV targets.

[0129] Therefore, the present invention proposes a UAV target inversion method under strong sea clutter background based on time-frequency-Doppler domain joint filtering and time-frequency ridge analysis, which performs time-frequency-Doppler domain joint filtering on the signal, has a better suppression effect on sea clutter, can more easily distinguish clutter from target echoes, and makes the UAV target echoes better appear in a multi-dimensional field.

[0130] The present invention combines the simulated UAV radar echo data with the measured sea clutter data, simulates the situation of the UAV flying on the sea surface, and performs multi-domain joint filtering operations such as moving target indication filtering in the time domain dimension, matched filtering in the range-Doppler dimension, and time-frequency filtering in the time-frequency dimension on the total echo data to suppress sea clutter, thereby achieving the purpose of detecting UAV targets in multiple dimensions such as time domain, frequency domain, time-frequency domain, and Doppler domain, and at the same time, the blade length, rotation speed, distance, movement speed and other information of the UAV are inferred from the processed data.

[0131] The present invention also provides a UAV target inversion system under strong sea clutter background based on time-frequency-Doppler domain joint filtering and time-frequency ridge analysis, comprising:

[0132] The UAV radar echo calculation module is used to implement the linear frequency modulation signal system radar in step 1, establish a multi-rotor UAV radar echo model, and calculate the multi-rotor UAV radar echo;

[0133] The total radar echo filtering module is used to realize the combination of the UAV radar echo obtained in step 1 and the measured sea surface radar echo in step 2 to form a complete UAV radar echo under the background of strong sea clutter, that is, the total radar echo, and perform time-frequency-Doppler domain joint filtering on the total radar echo to obtain the total power spectral density P after filtering. l ;

[0134] The time-frequency ridge extraction module is used to implement the total power spectrum density P obtained in step 2 after filtering in step 3. l Time-frequency ridge extraction was performed to verify the goodness of fit.

[0135] The present invention also provides a UAV target inversion device under strong sea clutter background based on time-frequency-Doppler domain joint filtering and time-frequency ridge analysis, comprising:

[0136] Memory: a computer program for storing the above-mentioned method for inverting UAV targets in a strong sea clutter background based on time-frequency-Doppler domain joint filtering and time-frequency ridgeline analysis, which is a computer-readable device;

[0137] Processor: used to implement the UAV target inversion method under strong sea clutter background based on time-frequency-Doppler domain joint filtering and time-frequency ridge analysis when executing the computer program.

[0138] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it can implement the method for inverting UAV targets in a strong sea clutter background based on time-frequency-Doppler domain joint filtering and time-frequency ridge analysis.

Claims

1. A method for inverting UAV targets in strong sea clutter background based on time-frequency-Doppler domain joint filtering and time-frequency ridge analysis, characterized in that: The following steps are involved: Step 1: For the linear frequency modulation signal system radar, a multi-rotor UAV radar echo model is established, and the multi-rotor UAV radar echo is calculated. The measured sea surface radar echo is added to the multi-rotor UAV radar echo to form a complete UAV radar echo under the strong sea clutter background, that is, the total radar echo; Step 2: Perform time-frequency-Doppler domain joint filtering on the total radar echo obtained in step 1 to obtain the total power spectrum density P after filtering. l ; Step 3: The total power spectrum density P after filtering obtained in step 2 l Time-frequency ridge extraction was performed to verify the goodness of fit.

2. The method for inverting UAV targets in a strong sea clutter background based on time-frequency-Doppler domain joint filtering and time-frequency ridge analysis according to claim 1 is characterized in that: The specific method of step 1 is: Step 1.1: The time domain expression of the linear frequency modulation signal as the transmitted signal is as follows: Where A0 is the initial amplitude of the transmitted signal, f0 is the carrier frequency, B is the FM bandwidth, T is the FM period, k=B / T is the FM slope, and φ0 is the initial phase; The time domain expression of the target echo signal is: Among them, τ is the delay; Step 1.2: Subtract the phase of the transmitted signal in step 1.1 from the phase of the target echo signal, and the baseband signal phase obtained is: P b (t)=2π(f0τ(t)+ktτ(t)-kt 2 (t) / 2) Step 1.3: The beat signal is further obtained from the baseband signal phase in step 1.2: Step 1.4: Express the distance from the scattering point Q of a blade on a certain rotor of the drone to the radar as: R Q =R0+vt+l Q cosβcos(θ0+Ω m t-a) Among them, R0 is the initial slant distance of the target, v is the speed of the UAV, l Q is the distance between the scattering point Q and the blade rotation center, β is the pitch angle, θ0 is the initial rotation angle of the blade, Ω m is the angular velocity of the mth rotor, α is the azimuth angle; Step 1.5: Substitute the distance R from the scattering point Q of a blade on a rotor of the drone obtained in step 1.4 to the radar Q Substitute the echo delay τ = 2R Q / c, where c is the speed of light, and combined with the beat signal obtained in step 1.3, integrated over the entire blade length L, the radar echo expression of the multi-rotor drone is obtained: Where M is the number of rotors, N is the number of blades on a rotor, and σ mirco is the scattering coefficient of the blade, σ main is the scattering coefficient of the drone body.

3. The method for inverting UAV targets in a strong sea clutter background based on time-frequency-Doppler domain joint filtering and time-frequency ridge analysis according to claim 1 is characterized in that: The specific method of step 2 is: Step 2.1: Perform clutter suppression on the total radar echo in the slow time dimension of the time domain, filter out the stationary clutter, and retain the moving target to indicate the moving target; the corresponding filter impulse response of the moving target indication is: Where J is the number of pulse cancellations; the output signal is: y1(t)=r(t)*h J (q) Where r(t) is the total radar echo; Step 2.2: Set the impulse response function of the matched filter: h(t)=k0s * (t0-t) Where k0 is a constant not equal to 0, s(t) is the target signal; the output signal y1(t) obtained in step 2.1 is subjected to matched filtering, and the output signal is: Step 2.3: Perform short-time Fourier transform on the output signal y2(t) obtained in step 2.2: STFT(T,F)=∫y2(r)w(r-T)e -j2πFr dr Among them, w(×) is the window function, and the power spectral density of the transformed signal is: P(T,F)=k(T)|STFT(T,F)| 2 Among them, k(T) is the transformation coefficient to compensate for the influence of the window function, f s is the sampling rate when performing short-time Fourier transform; Step 2.4: Find the extreme value of the power spectrum density calculated in step 2.3, and select the power spectrum density at the non-extreme frequency to find the average value in different time dimensions as the power spectrum density mean of the clutter, that is: P1=P(T1,F1) / L T Among them, L T is the time length, and the power spectrum density at the maximum frequency is selected to find the average value in different time dimensions as the target power spectrum density mean, that is: P2=max(P(T,F)) / L T Step 2.5: Take the ratio of the mean power spectral density of the clutter obtained in step 2.4 to the mean power spectral density of the target as the time-frequency filter factor γ, and establish a filter factor calculation model, namely: γ=P1 / P2 The total power spectrum density is subjected to time-frequency filtering, and the specific expression is: P l ={P[P(T,F)<γ*max(P(T,F))]=0} At this point, the time-frequency-Doppler domain joint filtering of the total radar echo is achieved.

4. The method for inverting UAV targets in a strong sea clutter background based on time-frequency-Doppler domain joint filtering and time-frequency ridge analysis according to claim 1 is characterized in that: The specific method of step 3 is: The ratio of the mean power spectral density of the clutter obtained in step 2.4 to the mean power spectral density of the target is used as the time-frequency filtering factor, the ridges of the time-frequency graph are extracted, the ridges of the UAV time-frequency graph are compared with the ridges of the UAV time-frequency graph under the sea clutter background, and their mean fitting graph is calculated.

5. A UAV target inversion system in a strong sea clutter background based on the method according to any one of claims 1 to 4 based on time-frequency-Doppler domain joint filtering and time-frequency ridge analysis, characterized in that: include: The UAV radar echo calculation module is used to establish a multi-rotor UAV radar echo model for the linear frequency modulation signal system radar and calculate the multi-rotor UAV radar echo; The total radar echo filtering module is used to combine the UAV radar echo with the measured sea surface radar echo to form a complete UAV radar echo under the background of strong sea clutter, that is, the total radar echo, and perform time-frequency-Doppler domain joint filtering on the total radar echo to obtain the total power spectral density P after filtering. l ; The time-frequency ridge extraction module is used to extract the total power spectrum density P after filtering. l Time-frequency ridge extraction was performed to verify the goodness of fit.

6. A UAV target inversion device under strong sea clutter background based on time-frequency-Doppler domain joint filtering and time-frequency ridge analysis, characterized in that: include: Memory: a computer program storing a method for inverting a UAV target in a strong sea clutter background based on time-frequency-Doppler domain joint filtering and time-frequency ridge analysis as described in any one of claims 1 to 4, which is a computer-readable device; Processor: used to implement the method for inverting UAV targets in a strong sea clutter background based on time-frequency-Doppler domain joint filtering and time-frequency ridge analysis as described in any one of claims 1 to 4 when executing the computer program.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, can implement a method for inverting UAV targets in a strong sea clutter background based on time-frequency-Doppler domain joint filtering and time-frequency ridge analysis as described in any one of claims 1 to 4.

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