A real-time estimation and prediction method for multi-modal spectrum of sea surface waves from airborne radar

Abstract

The invention discloses a method for real-time estimation and prediction of multimodal spectrum of sea clutter of airborne radar, comprising the following steps: S1, establishing a parameterized model of airborne radar antenna space spectrum; S2, establishing a parameterized model of multimodal scattering spectrum of sea surface by using the multimodal scattering characteristics of sea surface; S3, establishing a parameterized model of multimodal spectrum of sea clutter of airborne radar; S4, estimating the multimodal spectrum of sea clutter of airborne radar by using the sea clutter measurement data of airborne radar; S5, calculating the center frequency, spectrum width and normalized intensity coefficient of each mode in the multimodal scattering spectrum of sea surface, and inverting the multimodal scattering spectrum of sea surface; S6, predicting the multimodal spectrum of sea clutter of airborne radar at the next moment based on the parameterized model of airborne radar antenna space spectrum. The invention adopts the above-mentioned method for real-time estimation and prediction of multimodal spectrum of sea clutter of airborne radar, which can accurately estimate the multimodal spectrum of sea clutter of airborne radar in real time and accurately predict the multimodal spectrum of sea clutter of airborne radar at the next moment.

Description

Technical Field

[0001] The invention relates to the technical field of radar ocean remote sensing monitoring, and in particular to a method for real-time estimation and prediction of multi-modal spectrum of sea surface waves of airborne radar. Background Art

[0002] The spectrum of sea clutter and the autocorrelation function of the sea clutter time series are a pair of Fourier transforms. By obtaining the Doppler distribution of sea clutter, the temporal decorrelation characteristics and spectral distribution characteristics of sea clutter can be estimated. The spectral distribution of sea clutter is affected by factors such as sea conditions, platform motion state, antenna pattern, radar band, and radar polarization mode, and is complex and variable. At present, the sea clutter spectrum estimation methods are mainly divided into histogram estimation method and model estimation method. The histogram estimation method is simple and direct, but it is easily affected by data fluctuations and cannot show the mapping relationship between the sea clutter spectrum distribution and factors such as radar parameters and sea conditions. The model estimation method estimates the parameters of the Doppler distribution model by using measured data. Benefiting from the model constraints, it can reduce the estimation error of the sea clutter spectrum under data fluctuations. At the same time, the fitted model can reflect the specific evolution law of the sea clutter spectrum distribution with multiple factors.

[0003] In 2022, Rosenberg L proposed a two-component sea clutter Doppler model in his published paper. This method represents the spectrum of sea clutter as the linear weighted sum of two Gaussian functions, one of which represents the slowly varying sea clutter scattering component and the other Gaussian function represents the rapidly varying sea clutter scattering component. However, this model is not suitable for simulating the spectral distribution characteristics of sea clutter with more than two scattering components.

[0004] The China Institute of Radiowave Propagation proposed a method for analyzing and comparing the Doppler characteristics of sea clutter in its patent application "A method for analyzing and comparing the Doppler characteristics of sea clutter" (application number: CN201910996586.4, publication number: CN110907907A). This method first estimates the average spectrum of sea clutter, then calculates the center frequency and spectrum width of the maximum spectrum peak, and then uses the center frequency and width caused by the motion of the hull to correct the center frequency and spectrum width of the shipborne platform, and further analyzes the changes of the center frequency and spectrum width with the marine environmental elements and the radar range resolution. When the data fluctuates greatly, the estimation error of the average spectrum of sea clutter, the center frequency of the maximum spectrum peak and its spectrum width will increase, and the acquisition characteristic transformation will also deviate accordingly. Summary of the invention

[0005] The purpose of the present invention is to provide a method for real-time estimation and prediction of multi-modal spectrum of sea surface waves of airborne radar, which can accurately estimate the multi-modal spectrum of sea clutter of airborne radar in real time and accurately predict the multi-modal spectrum of sea clutter of airborne radar at the next moment.

[0006] To achieve the above object, the present invention provides a method for real-time estimation and prediction of multi-modal spectrum of sea surface waves by airborne radar, which specifically comprises the following steps:

[0007] S1. Establishment of spatial spectrum parameterized model of airborne radar antenna

[0008] According to the incident angle, azimuth angle and platform moving speed of the airborne radar electromagnetic wave, the center frequency and spectrum width of the airborne radar antenna spatial spectrum are calculated; the airborne radar antenna spatial spectrum parameterization model is established by using the center frequency and spectrum width of the airborne radar antenna spatial spectrum;

[0009] S2. Using the characteristics of sea surface multimodal scattering, a parameterized model of sea surface multimodal scattering spectrum is established;

[0010] S3, convolving the airborne radar antenna spatial spectrum parameterized model in step S1 with the sea surface scattering spectrum parameterized model in step S2 to establish an airborne radar sea clutter multi-modal spectrum parameterized model;

[0011] S4, using the airborne radar sea clutter measurement data, estimating the multi-modal spectrum of the airborne radar sea clutter;

[0012] S5, using the airborne radar sea clutter multimodal spectrum obtained in step 3, calculating the center frequency, spectrum width and normalized intensity coefficient of each mode in the sea surface multimodal scattering spectrum, and inverting the sea surface multimodal scattering spectrum;

[0013] S6. According to the system parameters of the airborne radar at the next moment and based on the parameterized model of the airborne radar antenna spatial spectrum, predict the multi-modal spectrum of the sea clutter of the airborne radar at the next moment.

[0014] Preferably, in step S1, the airborne radar antenna spatial spectrum parameterized model is:

[0015]

[0016] Where G(f) represents the spatial spectrum parameterized model of airborne radar antenna, G 0 represents the two-way gain of the airborne radar antenna, f p represents the center frequency of the airborne radar antenna spatial spectrum, w p Represents the spectral width of the airborne radar antenna spatial spectrum.

[0017] Preferably, in step S1, the f p The calculation method of the center frequency of the airborne radar antenna spatial spectrum is:

[0018] f p =2v a sin(θ 0 )cos(α 0) / λ

[0019] Among them, v a represents the speed of the airborne radar platform, θ 0 and α 0 They represent the incident angle and azimuth of the airborne radar antenna beam center respectively, and λ represents the wavelength of the airborne radar.

[0020] Preferably, in step S2, the sea surface multimodal scattering spectrum parameterization model is:

[0021]

[0022] Where S(f) represents the parameterized model of the sea surface multimodal scattering spectrum of the airborne radar, Σ represents the summation operation, n = 1, 2, 3…, N, n represents the nth mode, N represents the total number of modes of the sea surface multimodal scattering spectrum, A n represents the intensity coefficient of the nth mode of the sea surface multimodal scattering spectrum, f A_n represents the center frequency of the nth mode of the sea surface multimodal scattering spectrum, w A_n Represents the spectral width of the nth mode of the sea surface multimodal scattering spectrum.

[0023] Preferably, in step S3, the airborne radar sea clutter multi-modal spectrum parameterized model is:

[0024]

[0025] Wherein, P(f) represents the multi-modal spectrum parameterized model of airborne radar sea clutter, Σ represents the summation operation, n=1,2,3…,N, n represents the nth mode of the multi-modal spectrum parameterized model of airborne radar sea clutter, N represents the total number of modes of the multi-modal spectrum parameterized model of airborne radar sea clutter, B n represents the intensity coefficient of the nth mode of the multi-modal spectrum parameterized model of airborne radar sea clutter, f B_n represents the center frequency of the nth mode of the multi-modal spectrum parameterized model of airborne radar sea clutter, w B_n represents the spectrum width of the nth mode of the multi-modal spectrum parameterized model of airborne radar sea clutter, σ n Represents the noise power of the receiver.

[0026] Preferably, in step S4, the specific steps of estimating the multi-modal spectrum of airborne radar sea clutter are as follows:

[0027] S41, performing Fast Fourier Transform (FFT) on the airborne radar sea clutter measurement data after pulse compression in the pulse time domain to obtain a frequency spectrum point cloud of the airborne radar sea clutter;

[0028] S42, estimating the noise power of the receiver using the calibration data within the airborne radar;

[0029] S43, initializing parameter settings for the mode number of the airborne radar sea clutter multi-mode spectrum parameterized model and the intensity coefficient, frequency center and spectrum width of each mode;

[0030] S44, according to the initialization parameter setting, based on the airborne radar sea clutter multi-modal spectrum parameterization model, calculating the airborne radar sea clutter multi-modal spectrum distribution curve;

[0031] S45, normalized root mean square error between computer-borne radar sea clutter multimodal spectrum distribution curve and airborne radar sea clutter spectrum point cloud;

[0032] S46, if the normalized root mean square error in step S45 is less than a given threshold, execute step S47, otherwise, update the number of modes and the intensity coefficient, frequency center and spectrum width parameters of each mode in the airborne radar sea clutter multi-modal spectrum parameterized model, and repeat steps S45, S46 and S47;

[0033] S47, output the airborne radar sea clutter multi-modal spectrum distribution curve as the airborne radar sea clutter multi-modal spectrum; record the intensity coefficient, frequency center and spectrum width of each mode of the airborne radar sea clutter multi-modal spectrum, which are N represents the total number of modes in the multi-modal spectrum of sea clutter of airborne radar, and the estimated receiver noise power is recorded as

[0034] Preferably, in step S5, the central frequency of each mode in the airborne radar sea surface scattering spectrum is calculated as follows:

[0035]

[0036] Among them, f A_n Represents the center frequency of the nth mode of the sea surface scattering spectrum, n=1,2,3…,N, represents the frequency center of the nth mode of the airborne radar sea clutter spectrum, f p Represents the center frequency of the antenna spatial spectrum;

[0037] The calculation method of the spectrum width of each mode in the airborne radar sea surface scattering spectrum is:

[0038]

[0039] Among them, w A_n Represents the spectrum width of the nth mode of the sea surface scattering spectrum, n = 1, 2, 3..., N, represents the spectrum width of the nth mode of the airborne radar sea clutter spectrum, w p Represents the spectral width of the antenna spatial spectrum;

[0040] The calculation method of the intensity coefficient of each mode in the airborne radar sea surface scattering spectrum is:

[0041]

[0042] Among them, A n It represents the normalized intensity coefficient of the nth mode of the sea surface scattering spectrum, n=1,2,3…,N, N represents the total number of modes of the sea clutter spectrum of the airborne radar, represents the intensity coefficient of the nth mode of the airborne radar sea clutter spectrum, represents the spectrum width of the nth mode of the airborne radar sea clutter spectrum, w p represents the spectral width of the antenna spatial spectrum, μ represents the normalization coefficient,

[0043] Preferably, in step S5, the method of inverting the sea surface multimodal scattering spectrum is:

[0044]

[0045] Where S(f) represents the multimodal scattering spectrum of the sea surface, Σ represents the summation operation, n = 1, 2, 3…, N, n represents the nth mode, N represents the total number of modes of the multimodal scattering spectrum of the sea surface, and A n represents the intensity coefficient of the nth mode of the sea surface multimodal scattering spectrum, w A_n represents the spectrum width of the nth mode distribution curve of the sea surface multimodal scattering spectrum, f A_n Represents the frequency center of the nth mode of the sea surface multimodal scattering spectrum.

[0046] Preferably, in step S6, the predicting of the airborne radar antenna spatial spectrum at the next moment comprises the following specific steps:

[0047] S61, calculating the airborne radar antenna spatial spectrum at the next moment according to the airborne radar system parameters at the next moment and based on the airborne radar antenna spatial spectrum parameterization model;

[0048] S62, convolve the spatial spectrum of the airborne radar antenna at the next moment with the multi-modal scattering spectrum of the sea surface to obtain the multi-modal spectrum of the sea clutter of the airborne radar at the next moment, and output the prediction result.

[0049] Therefore, the present invention adopts the above-mentioned airborne radar sea surface wave multi-modal spectrum real-time estimation and prediction method, and its technical effects are as follows:

[0050] (1) Since the multi-modal spectrum parameterized model of airborne radar sea clutter established in the present invention is represented by a weighted sum of multiple modes, the number of modes and the modal parameters of the model can be adaptively adjusted according to other factors such as the airborne radar echo signal and sea conditions, making the application scope wider.

[0051] (2) Since the present invention can continuously update the parameters of the sea clutter multimodal spectrum parameterized model according to the degree of fit between the sea clutter multimodal spectrum parameterized model and the airborne radar sea clutter measurement data, it can make a real-time estimate of the position where the spectrum peak appears and the degree of spectrum expansion in combination with the measured data, and can effectively avoid the problem of inaccurate estimation of the airborne radar sea clutter multimodal spectrum due to model mismatch.

[0052] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a flow chart of a method for real-time estimation and prediction of multi-modal spectrum of sea surface waves of airborne radar of the present invention;

[0054] Figure 2 It is the result of multi-modal spectrum estimation of sea clutter in level 3 sea condition, headwind, and 32.86° incident angle;

[0055] Figure 3 It is the result of multi-modal spectrum estimation of sea clutter in sea state level 3, headwind, and incident angle of 39.55°;

[0056] Figure 4 It is the result of multi-modal spectrum estimation of sea clutter in level 3 sea condition, headwind, and 42.63° incident angle;

[0057] Figure 5 It is the inversion result of the multi-modal scattering spectrum of the sea surface in level 3 sea state, headwind, and 32.86° incident angle;

[0058] Figure 6 It is the inversion result of multi-modal scattering spectrum of sea surface in level 3 sea condition, headwind, and 39.55° incident angle;

[0059] Figure 7 It is the inversion result of the multi-modal scattering spectrum of the sea surface in level 3 sea conditions, headwind, and 42.63° incident angle. DETAILED DESCRIPTION

[0060] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0061] Unless otherwise defined, technical or scientific terms used in the present invention shall have the common meanings understood by one having ordinary skills in the field to which the present invention belongs.

[0062] It is obvious to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the main idea or essential features of the present invention. Therefore, from all points of view, the embodiments should be regarded as exemplary and non-restrictive, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention, and any reference numerals in the claims should not be regarded as limiting the claims involved.

[0063] In addition, it should be understood that although this specification is described according to the implementation modes, not every implementation mode includes only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation modes that can be understood by those skilled in the art. These other implementation modes are also covered within the protection scope of the present invention.

[0064] It should also be understood that the specific embodiments described above are only used to explain the present invention, and the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, can make equivalent replacements or changes based on the technical solutions and inventive concepts of the present invention, which should be covered by the protection scope of the present invention / invention.

[0065] Technologies, methods, and equipment known to ordinary technicians in the relevant field may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered as part of the specification.

[0066] The disclosed contents of the prior art documents cited in the present specification are incorporated into the present invention by reference in their entirety and are therefore part of the disclosure of the present invention.

[0067] Embodiment 1

[0068] As shown in the figure, the present invention provides a method for real-time estimation and prediction of multi-modal spectrum of sea surface waves of airborne radar, comprising the following steps:

[0069] Step 1: Establish a parameterized model of the airborne radar antenna space spectrum

[0070] According to the incident angle, azimuth angle and platform moving speed of the airborne radar electromagnetic wave, the center frequency and spectrum width of the airborne radar antenna spatial spectrum are calculated; the airborne radar antenna spatial spectrum parameterization model is established by using the center frequency and spectrum width of the airborne radar antenna spatial spectrum;

[0071] The parameterized model of airborne radar antenna space spectrum is:

[0072]

[0073] Where G(f) represents the spatial spectrum parameterized model of airborne radar antenna, G 0 represents the two-way gain of the airborne radar antenna, f p represents the center frequency of the airborne radar antenna spatial spectrum, w p It represents the spectral width of the airborne radar antenna spatial spectrum;

[0074] f p The calculation method of the center frequency of the airborne radar antenna spatial spectrum is:

[0075] f p =2v a sin(θ 0 )cos(α 0 ) / λ

[0076] Among them, v a represents the speed of the airborne radar platform, θ 0 and α 0 They represent the incident angle and azimuth of the airborne radar antenna beam center respectively, and λ represents the wavelength of the airborne radar.

[0077] Step 2: Establish a parameterized model of the sea surface multimodal scattering spectrum

[0078] Using the characteristics of sea surface multimodal scattering, a parameterized model of sea surface multimodal scattering spectrum is established as follows:

[0079]

[0080] Where S(f) represents the parameterized model of the sea surface multimodal scattering spectrum of the airborne radar, Σ represents the summation operation, n = 1, 2, 3…, N, n represents the nth mode, N represents the total number of modes of the sea surface multimodal scattering spectrum, A n represents the intensity coefficient of the nth mode of the sea surface multimodal scattering spectrum, f A_n represents the center frequency of the nth mode of the sea surface multimodal scattering spectrum, w A_n Represents the spectral width of the nth mode of the sea surface multimodal scattering spectrum.

[0081] Step 3: Establish a multi-modal spectrum parameterized model of airborne radar sea clutter

[0082] The airborne radar antenna spatial spectrum parameterized model in step 1 is convolved with the sea surface scattering spectrum parameterized model in step 2 to obtain the airborne radar sea clutter multi-modal spectrum parameterized model:

[0083]

[0084] Wherein, P(f) represents the multi-modal spectrum parameterized model of airborne radar sea clutter, Σ represents the summation operation, n=1,2,3…,N, n represents the nth mode of the multi-modal spectrum parameterized model of airborne radar sea clutter, N represents the total number of modes of the multi-modal spectrum parameterized model of airborne radar sea clutter, B n represents the intensity coefficient of the nth mode of the multi-modal spectrum parameterized model of airborne radar sea clutter, f B_n represents the center frequency of the nth mode of the multi-modal spectrum parameterized model of airborne radar sea clutter, w B_n represents the spectrum width of the nth mode of the multi-modal spectrum parameterized model of airborne radar sea clutter, σ n Represents the noise power of the receiver.

[0085] Step 4: Estimate the multi-modal spectrum of airborne radar sea clutter

[0086] The specific steps of estimating the multi-modal spectrum of airborne radar sea clutter using airborne radar sea clutter measurement data are as follows:

[0087] (1) Performing Fast Fourier Transform (FFT) on the airborne radar sea clutter measurement data after pulse compression in the pulse time domain to obtain the airborne radar sea clutter spectrum point cloud;

[0088] (2) Estimate the receiver noise power using the airborne radar internal calibration data;

[0089] (3) Initialize the number of modes, intensity coefficient, frequency center and spectrum width of each mode of the multi-mode spectrum parameterized model of airborne radar sea clutter;

[0090] (4) according to the initialization parameter setting, based on the parameterized model of the multi-modal spectrum of the airborne radar sea clutter, calculating the distribution curve of the multi-modal spectrum of the airborne radar sea clutter;

[0091] (5) The normalized root mean square error between the multimodal spectrum distribution curve of computer-borne radar sea clutter and the spectrum point cloud of airborne radar sea clutter;

[0092] (6) If the normalized root mean square error in step (5) is less than a given threshold, execute step (7); otherwise, update the number of modes and the intensity coefficient, frequency center and spectrum width parameters of each mode in the airborne radar sea clutter multi-mode spectrum parameterization model, and repeat steps (4), (5) and (6);

[0093] (7) Output the multi-modal spectrum distribution curve of the airborne radar sea clutter as the multi-modal spectrum of the airborne radar sea clutter; record the intensity coefficient, frequency center and spectrum width of each mode of the multi-modal spectrum of the airborne radar sea clutter, which are: N represents the total number of modes in the multi-modal spectrum of sea clutter of airborne radar, and the estimated receiver noise power is recorded as

[0094] Step 5: Invert the sea surface multimodal scattering spectrum

[0095] According to the intensity coefficient, frequency center and spectrum width of each mode of the multi-mode spectrum of the airborne radar sea clutter recorded in step (7), the center frequency, spectrum width and normalized intensity coefficient of each mode in the multi-mode scattering spectrum of the sea surface are calculated, and the multi-mode scattering spectrum of the sea surface is inverted according to the following steps:

[0096] The center frequency of each mode in the sea surface scattering spectrum of the onboard radar is calculated according to the following formula:

[0097]

[0098] Among them, f A_n Represents the center frequency of the nth mode of the sea surface scattering spectrum, n=1,2,3…,N, represents the frequency center of the nth mode of the airborne radar sea clutter spectrum, f p Represents the center frequency of the antenna spatial spectrum.

[0099] The spectral width of each mode in the sea surface scattering spectrum of the onboard radar is calculated according to the following formula:

[0100]

[0101] Among them, w A_n Represents the spectrum width of the nth mode of the sea surface scattering spectrum, n = 1, 2, 3..., N, represents the spectrum width of the nth mode of the airborne radar sea clutter spectrum, w p Represents the spectral width of the antenna spatial spectrum.

[0102] The intensity coefficient of each mode in the sea surface scattering spectrum of the onboard radar is calculated according to the following formula:

[0103]

[0104] Among them, A n It represents the normalized intensity coefficient of the nth mode of the sea surface scattering spectrum, n=1,2,3…,N, N represents the total number of modes of the sea clutter spectrum of the airborne radar, represents the intensity coefficient of the nth mode of the airborne radar sea clutter spectrum, represents the spectrum width of the nth mode of the airborne radar sea clutter spectrum, w p represents the spectral width of the antenna spatial spectrum, μ represents the normalization coefficient,

[0105] The multi-mode scattering spectrum of the sea surface is inverted according to the following formula:

[0106]

[0107] Where S(f) represents the multimodal scattering spectrum of the sea surface, Σ represents the summation operation, n = 1, 2, 3…, N, n represents the nth mode, N represents the total number of modes of the multimodal scattering spectrum of the sea surface, and A n represents the intensity coefficient of the nth mode of the sea surface multimodal scattering spectrum, w A_n represents the spectrum width of the nth mode distribution curve of the sea surface multimodal scattering spectrum, f A_n Represents the frequency center of the nth mode of the sea surface multimodal scattering spectrum.

[0108] Step 6: Predict the multi-modal spectrum of sea clutter of the airborne radar at the next moment

[0109] According to the airborne radar system parameters at the next moment, based on the airborne radar antenna spatial spectrum parameterization model, the airborne radar antenna spatial spectrum at the next moment is calculated according to the following steps:

[0110] Firstly, according to the system parameters of the airborne radar at the next moment, the airborne radar antenna spatial spectrum at the next moment is calculated based on the parameterized model of the airborne radar antenna spatial spectrum;

[0111] Finally, the spatial spectrum of the airborne radar antenna at the next moment is convolved with the multimodal scattering spectrum of the sea surface to obtain the multimodal spectrum of the airborne radar sea clutter at the next moment, and the prediction result is output.

[0112] The effect of the present invention can be further illustrated by the following experiments:

[0113] 1. Experimental conditions

[0114] The present invention uses L-band HH polarization airborne radar system to measure sea clutter data for verification. The airborne radar has a carrier frequency of 1.5 GHz, a bandwidth of 20 MHz, a sampling frequency of 40 MHz, a pulse repetition frequency of 2500 Hz, a pulse number of 250, an antenna length of 1 m, a platform altitude of 3500 m, a platform moving speed of 60 m / s, an antenna pitch angle of 45°, and an incident angle range of 30-60 degrees.

[0115] 2. Experimental content and results

[0116] The measured data is combined with the parameterized distribution model of the sea clutter multimodal spectrum established by the present invention to estimate the sea clutter multimodal spectrum. The normalized root mean square error threshold is 0.005, and the condition of being less than the threshold is satisfied when the mode number is 3. Figure 2-4 It is the multi-modal spectrum estimation result of sea clutter at different incident angles under headwind and level 3 sea condition. Figure 2-4The horizontal axis represents the Doppler frequency, the vertical axis represents the normalized amplitude, the red solid points represent the sea clutter spectrum point cloud of the airborne radar measurement data, and the black curve represents the sea clutter multimodal spectrum estimation result based on the sea clutter multimodal spectrum parameterization model. According to statistics, the normalized root mean square error of the two is less than 0.005. Figure 5-7 It is the inversion result of multi-modal scattering spectrum of sea surface at different incident angles under headwind and level 3 sea condition. Figure 5-7 The solid line in the middle represents the spatial spectrum model of the airborne radar antenna, the dashed straight line represents the multimodal spectrum of sea clutter, and the solid dotted straight line represents the inversion result of the multimodal scattering spectrum of the sea surface.

[0117] The experimental results show that the multimodal spectrum of sea clutter estimated by the method of the present invention is consistent with the sea clutter spectrum point cloud of airborne radar measurement data, indicating that the estimation accuracy is high. At the same time, the multimodal scattering spectrum of the sea surface can be inverted, thereby realizing the prediction of the multimodal spectrum of sea clutter of airborne radar.

[0118] Therefore, the present invention adopts the above-mentioned airborne radar sea surface wave multimodal spectrum real-time estimation and prediction method, which can accurately estimate the airborne radar sea clutter multimodal spectrum in real time and accurately predict the airborne radar sea clutter multimodal spectrum at the next moment.

[0119] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A real-time estimation and prediction method for multi-modal spectrum of sea surface waves using airborne radar. It is characterized in that The specific steps include: S1. Establishment of spatial spectrum parameterized model of airborne radar antenna According to the incident angle, azimuth angle and platform moving speed of the airborne radar electromagnetic wave, the center frequency and spectrum width of the airborne radar antenna spatial spectrum are calculated; the airborne radar antenna spatial spectrum parameterization model is established by using the center frequency and spectrum width of the airborne radar antenna spatial spectrum; S2. Using the characteristics of sea surface multimodal scattering, a parameterized model of sea surface multimodal scattering spectrum is established; S3, convolving the airborne radar antenna spatial spectrum parameterized model in step S1 with the sea surface scattering spectrum parameterized model in step S2 to establish an airborne radar sea clutter multi-modal spectrum parameterized model; S4. Using the airborne radar sea clutter measurement data, estimate the airborne radar sea clutter multimodal spectrum. The specific steps of estimating the airborne radar sea clutter multimodal spectrum are as follows: S41, performing Fast Fourier Transform (FFT) on the airborne radar sea clutter measurement data after pulse compression in the pulse time domain to obtain a frequency spectrum point cloud of the airborne radar sea clutter; S42, estimating the noise power of the receiver using the calibration data within the airborne radar; S43, initializing parameter settings for the mode number of the airborne radar sea clutter multi-mode spectrum parameterized model and the intensity coefficient, frequency center and spectrum width of each mode; S44, according to the initialization parameter setting, based on the airborne radar sea clutter multi-modal spectrum parameterization model, calculating the airborne radar sea clutter multi-modal spectrum distribution curve; S45, normalized root mean square error between computer-borne radar sea clutter multimodal spectrum distribution curve and airborne radar sea clutter spectrum point cloud; S46, if the normalized root mean square error in step S45 is less than a given threshold, execute step S47, otherwise, update the number of modes and the intensity coefficient, frequency center and spectrum width parameters of each mode in the airborne radar sea clutter multi-modal spectrum parameterized model, and repeat steps S45, S46 and S47; S47, output the airborne radar sea clutter multi-modal spectrum distribution curve as the airborne radar sea clutter multi-modal spectrum; record the intensity coefficient, frequency center and spectrum width of each mode of the airborne radar sea clutter multi-modal spectrum, which are n=1,2,3…,N, where N represents the total number of modes in the multi-modal spectrum of sea clutter of airborne radar. The estimated receiver noise power is recorded as S5, using the airborne radar sea clutter multimodal spectrum obtained in step 3, calculating the center frequency, spectrum width and normalized intensity coefficient of each mode in the sea surface multimodal scattering spectrum, and inverting the sea surface multimodal scattering spectrum; S6. According to the system parameters of the airborne radar at the next moment and based on the parameterized model of the airborne radar antenna spatial spectrum, predict the multi-modal spectrum of the sea clutter of the airborne radar at the next moment.

2. The method for real-time estimation and prediction of multi-modal spectrum of sea surface waves by airborne radar according to claim 1, It is characterized in that In step S1, the airborne radar antenna spatial spectrum parameterized model is: Where G(f) represents the spatial spectrum parameterized model of airborne radar antenna, G 0 represents the two-way gain of the airborne radar antenna, f p represents the center frequency of the airborne radar antenna spatial spectrum, w p Represents the spectral width of the airborne radar antenna spatial spectrum.

3. The method for real-time estimation and prediction of multi-modal spectrum of sea surface waves by airborne radar according to claim 2, It is characterized in that In step S1, the f p The calculation method of the center frequency of the airborne radar antenna spatial spectrum is: f p =2v a sin(θ 0 )cos(α 0 ) / min Among them, v a represents the speed of the airborne radar platform, θ 0 and α 0 They represent the incident angle and azimuth of the airborne radar antenna beam center respectively, and λ represents the wavelength of the airborne radar.

4. The method for real-time estimation and prediction of multi-modal spectrum of sea surface waves by airborne radar according to claim 1, It is characterized in that In step S2, the sea surface multimodal scattering spectrum parameterization model is: Where S(f) represents the parameterized model of the sea surface multimodal scattering spectrum of the airborne radar, Σ represents the summation operation, n = 1, 2, 3…, N, n represents the nth mode, N represents the total number of modes of the sea surface multimodal scattering spectrum, A n represents the intensity coefficient of the nth mode of the sea surface multimodal scattering spectrum, f A_n represents the center frequency of the nth mode of the sea surface multimodal scattering spectrum, w A_n Represents the spectral width of the nth mode of the sea surface multimodal scattering spectrum.

5. The method for real-time estimation and prediction of multi-modal spectrum of sea surface waves by airborne radar according to claim 1, It is characterized in that In step S3, the multi-modal spectrum parameterized model of airborne radar sea clutter is: Wherein, P(f) represents the multi-modal spectrum parameterized model of airborne radar sea clutter, Σ represents the summation operation, n=1,2,3…,N, n represents the nth mode of the multi-modal spectrum parameterized model of airborne radar sea clutter, N represents the total number of modes of the multi-modal spectrum parameterized model of airborne radar sea clutter, B n represents the intensity coefficient of the nth mode of the multi-modal spectrum parameterized model of airborne radar sea clutter, f B_n represents the center frequency of the nth mode of the multi-modal spectrum parameterized model of airborne radar sea clutter, w B_n represents the spectrum width of the nth mode of the multi-modal spectrum parameterized model of airborne radar sea clutter, σ n Represents the noise power of the receiver.

6. The method for real-time estimation and prediction of multi-modal spectrum of sea surface waves by airborne radar according to claim 1, It is characterized in that In step S5, the central frequency of each mode in the sea surface multimodal scattering spectrum is calculated as follows: Among them, f A_n Represents the center frequency of the nth mode of the sea surface scattering spectrum, n=1,2,3…,N, represents the frequency center of the nth mode of the airborne radar sea clutter spectrum, f p Represents the center frequency of the antenna spatial spectrum; The calculation method of the spectrum width of each mode in the airborne radar sea surface scattering spectrum is: Among them, w A_n Represents the spectrum width of the nth mode of the sea surface scattering spectrum, n = 1, 2, 3..., N, represents the spectrum width of the nth mode of the airborne radar sea clutter spectrum, w p Represents the spectral width of the antenna spatial spectrum; The calculation method of the intensity coefficient of each mode in the airborne radar sea surface scattering spectrum is: Among them, A n It represents the normalized intensity coefficient of the nth mode of the sea surface scattering spectrum, n=1,2,3…,N, N represents the total number of modes of the sea clutter spectrum of the airborne radar, represents the intensity coefficient of the nth mode of the airborne radar sea clutter spectrum, represents the spectrum width of the nth mode of the airborne radar sea clutter spectrum, w p represents the spectral width of the antenna spatial spectrum, μ represents the normalization coefficient, 7. The method for real-time estimation and prediction of multi-modal spectrum of sea surface waves by airborne radar according to claim 6, It is characterized in that In step S5, the method of inverting the sea surface multimodal scattering spectrum is: Where S(f) represents the multimodal scattering spectrum of the sea surface, Σ represents the summation operation, n = 1, 2, 3…, N, n represents the nth mode, N represents the total number of modes of the multimodal scattering spectrum of the sea surface, and A n represents the intensity coefficient of the nth mode of the sea surface multimodal scattering spectrum, w A_n represents the spectrum width of the nth mode distribution curve of the sea surface multimodal scattering spectrum, f A_n Represents the frequency center of the nth mode of the sea surface multimodal scattering spectrum.

8. The method for real-time estimation and prediction of multi-modal spectrum of sea surface waves by airborne radar according to claim 1, It is characterized in that In step S6, the multi-modal spectrum of sea clutter of the airborne radar at the next moment is predicted, and the specific steps are as follows: S61, calculating the airborne radar antenna spatial spectrum at the next moment according to the airborne radar system parameters at the next moment and based on the airborne radar antenna spatial spectrum parameterization model; S62, convolve the spatial spectrum of the airborne radar antenna at the next moment with the multi-modal scattering spectrum of the sea surface to obtain the multi-modal spectrum of the sea clutter of the airborne radar at the next moment, and output the prediction result.

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