A method for estimating and compensating multi-component high-frequency vibration errors of a rotorborne SAR

Abstract

The application discloses a kind of multi-component high-frequency vibration error estimation and compensation method of rotor unmanned aerial vehicle-borne SAR, and the method comprises the following steps: using range-doppler algorithm to carry out coarse focusing to echo signal, to obtain the signal after coarse focusing;Extract each high-frequency vibration component contained in the signal after coarse focusing, and estimate the parameter of each high-frequency vibration component, using the parameter of each high-frequency vibration component estimated to construct the compensation function of each high-frequency vibration component, using the compensation function of high-frequency vibration component to carry out iterative compensation to the signal after coarse focusing, to obtain the echo signal after suppressing high-frequency vibration component;Residual error correction is carried out to the echo signal after suppressing high-frequency vibration component, to obtain the high-resolution imaging result after removing multi-component high-frequency vibration error.The application can effectively improve the imaging quality of rotor unmanned aerial vehicle-borne SAR.

Description

Technical Field

[0001] This invention belongs to the field of radar imaging technology, specifically relating to a method for estimating and compensating for multi-component high-frequency vibration errors in rotary-wing UAV-borne SAR. Background Technology

[0002] Airborne synthetic aperture radar (SAR) utilizes pulse compression and coherent integration techniques to generate high-resolution two-dimensional images in all weather conditions and around the clock, and has been widely applied in many fields. In recent decades, SAR systems have continuously evolved towards miniaturization and lightweight design, making them easier to install on multi-rotor unmanned aerial vehicle (UAV) platforms, thus enabling more flexible acquisition of ground information. As an important supplement to traditional airborne SAR, multi-rotor UAV-borne SAR offers advantages such as low cost, ease of operation, and high survivability, making it suitable for a wider range of applications including urban planning, emergency rescue, environmental protection, and resource exploration, and has become another research hotspot in the SAR field.

[0003] Due to their small size and light weight, the flight state of rotary-wing UAVs is affected by the rotation of their propellers, resulting in high-frequency vibration errors. Compared with traditional motion errors, high-frequency vibration errors have different forms and characteristics, and will have different effects on imaging results. Due to the mutual coupling between the multiple propeller blades of a rotary-wing UAV, high-frequency vibration errors are usually composed of multiple vibration components with different parameters, which can introduce multiple pairs of false targets around the target, seriously affecting the imaging results.

[0004] Most existing high-frequency vibration error compensation methods only consider single-component high-frequency vibration errors. Their performance drops significantly when suppressing multi-component high-frequency vibration errors, and their robustness is insufficient, which seriously affects the imaging quality. Summary of the Invention

[0005] To address the aforementioned problems in the existing technology, this invention provides a method for estimating and compensating for multi-component high-frequency vibration errors in rotary-wing unmanned aerial vehicle (UAV)-borne SAR.

[0006] The technical problem to be solved by this invention is achieved through the following technical solution:

[0007] This invention provides a method for estimating and compensating for multi-component high-frequency vibration errors in rotary-wing unmanned aerial vehicle (UAV)-borne SAR, comprising:

[0008] The range Doppler algorithm is used to coarsely focus the echo signal to obtain the coarsely focused signal.

[0009] Extract each high-frequency vibration component contained in the coarsely focused signal, estimate the parameters of each high-frequency vibration component, construct a compensation function for each high-frequency vibration component using the estimated parameters of each high-frequency vibration component, and use the compensation function of the high-frequency vibration component to iteratively compensate the coarsely focused signal to obtain the echo signal after suppressing the high-frequency vibration components.

[0010] The echo signal after suppressing the high-frequency vibration component is subjected to residual error correction to obtain a high-resolution imaging result after removing the multi-component high-frequency vibration error.

[0011] In some embodiments, the step of extracting each high-frequency vibration component contained in the coarsely focused signal, estimating the parameters of each high-frequency vibration component, constructing a compensation function for each high-frequency vibration component using the estimated parameters, and iteratively compensating the coarsely focused signal using the compensation function to obtain the echo signal after suppressing the high-frequency vibration components includes:

[0012] In the m-th iteration, the vibration component is extracted from the (m-1)-th compensation signal to obtain the m-th vibration component. It is then determined whether the m-th vibration component is a high-frequency vibration component. m is a positive integer greater than or equal to 1. When m is 1, the (m-1)-th compensation signal is the signal after coarse focusing.

[0013] When the m-th vibration component is not a high-frequency vibration component, the (m-1)-th compensation signal is used as the echo signal after suppressing the high-frequency vibration component.

[0014] When the m-th vibration component is a high-frequency vibration component, parameter estimation is performed on the m-th vibration component, and the parameters of the m-th vibration component are used to construct a compensation function for the m-th high-frequency vibration component.

[0015] The m-th high-frequency vibration component compensation function is used to compensate the signal after the (m-1)-th compensation to obtain the m-th compensated signal. This process is repeated until the echo signal after suppressing the high-frequency vibration component is obtained.

[0016] In some embodiments, extracting the vibration component from the (m-1)th compensation signal to obtain the mth vibration component includes:

[0017] Select the N strongest distance unit signals from the (m-1)th compensation signal; N is a preset positive integer;

[0018] Determine the phase information of each of the N distance unit signals to obtain N phase information corresponding to the N distance unit signals;

[0019] Different noises are added to the N phase information to obtain N signals to be processed, each corresponding to one of the N phase information.

[0020] Based on the N signals to be processed, determine N intrinsic mode function components;

[0021] The average value of the N intrinsic mode function components is obtained by averaging the components, and the average value of the components is taken as the m-th vibration component.

[0022] In some embodiments, the parameter estimation of the m-th vibration component includes:

[0023] Based on the frequency of the m-th vibration component, a basis function is constructed to describe the m-th vibration component; the basis function contains the amplitude to be estimated and the initial phase to be estimated;

[0024] Based on the basis functions, an expression for the correlation coefficient describing the relationship between the m-th vibration component and the echo signal is determined;

[0025] Based on the expression for the correlation coefficient, the correlation coefficient is searched within a preset range of amplitude values ​​and a preset range of initial phase values ​​to obtain the maximum value of the correlation coefficient;

[0026] The amplitude and initial phase used to generate the maximum value of the correlation coefficient are used as parameters for the estimated m-th vibration component.

[0027] In some embodiments, the correlation coefficient is expressed as follows:

[0028]

[0029] in, This represents the correlation coefficient. Let S(η) represent the basis function, S(η) represent the phase of the echo signal, η represent the azimuth time, and j represent the imaginary unit sign. This indicates the magnitude to be estimated. This represents the initial phase to be estimated. Let λ represent the frequency of the m-th vibration component, λ represent the wavelength, and ∫dη represent the integration operation of the azimuth time.

[0030] In some embodiments, the parameters of the m-th vibration component include: the frequency, amplitude, and initial phase of the m-th vibration component; the expression for the compensation function of the m-th high-frequency vibration component is as follows:

[0031]

[0032] Among them, H h(η) represents the compensation function for the m-th high-frequency vibration component, where η represents the azimuth time and j represents the imaginary unit sign. This represents the amplitude of the m-th vibration component. This represents the initial phase of the m-th vibration component. Let λ represent the frequency of the m-th vibration component, and λ represent the wavelength of the signal.

[0033] In some embodiments, performing residual error correction on the echo signal after suppressing the high-frequency vibration components to obtain a high-resolution imaging result after removing multi-component high-frequency vibration errors includes:

[0034] The echo signal after suppressing the high-frequency vibration component is divided into sub-apertures to obtain multiple sub-aperture signals;

[0035] Determine the optimal phase error compensation function for each sub-aperture signal, and the linear phase offset between the sub-aperture signals;

[0036] Based on the linear phase shift between the sub-aperture signals, a sub-aperture phase error compensation function corresponding to the sub-aperture signal is constructed, and based on the sub-aperture phase error compensation function, a full-aperture phase error compensation function is constructed.

[0037] The residual error of the echo signal after suppressing the high-frequency vibration component is corrected by using the full aperture phase error compensation function to obtain a high-resolution imaging result after removing the multi-component high-frequency vibration error.

[0038] In some embodiments, determining the optimal phase error compensation function for each sub-aperture signal and the linear phase shift between the sub-aperture signals includes:

[0039] For each sub-aperture signal, range blocks are divided, and T range blocks with the strongest energy are selected from the obtained range blocks as T estimated samples of the sub-aperture signal; T is a positive integer greater than 1.

[0040] The phase error gradient of each estimated sample of the sub-aperture signal is determined using the phase gradient self-focusing algorithm, resulting in T phase error gradients of the sub-aperture signal.

[0041] By integrating the T phase error gradients of the sub-aperture signal respectively, T phase error compensation functions are obtained;

[0042] The residual error of the sub-aperture signal is pre-compensated using the T phase error compensation functions respectively, and the pre-compensation effect of the T phase error compensation functions is evaluated respectively.

[0043] Select the phase error compensation function with the best pre-compensation effect as the optimal phase error compensation function corresponding to the sub-aperture signal;

[0044] The Doppler frequency modulation offset estimation process is performed on the sub-aperture signal to obtain the linear phase offset between the sub-aperture signals.

[0045] In some embodiments, the step of constructing a sub-aperture phase error compensation function corresponding to the sub-aperture signal based on the linear phase shift between the sub-aperture signals, and constructing a full-aperture phase error compensation function based on the sub-aperture phase error compensation function, includes:

[0046] Based on the linear phase shift between the sub-aperture signals, an initial sub-aperture phase error compensation function corresponding to the sub-aperture signal is constructed.

[0047] Remove the linear component from the initial sub-aperture phase error compensation function corresponding to the sub-aperture signal to obtain the sub-aperture phase error compensation function;

[0048] The phase error compensation functions of the multiple sub-aperture signals, which correspond one-to-one with each other, are concatenated to obtain the full aperture phase error compensation function.

[0049] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0050] This invention first extracts multiple components of high-frequency vibration error from the echo signal sequentially through cyclic processing, then estimates and compensates for them, improving the performance of the high-frequency vibration error compensation method and effectively enhancing the imaging quality of rotary-wing UAV-borne SAR. After compensating for the high-frequency vibration error, residual phase error is also corrected, further improving the accuracy and robustness of the high-frequency vibration error compensation, broadening its applicability, and effectively expanding the application fields of rotary-wing UAV-borne SAR. Therefore, this invention enhances the performance and robustness of the high-frequency vibration error compensation method, is applicable to rotary-wing UAV platforms with multi-component high-frequency vibration errors, and effectively improves the imaging quality of rotary-wing UAV-borne SAR.

[0051] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Attached Figure Description

[0052] Figure 1 This is a flowchart illustrating a method for estimating and compensating for multi-component high-frequency vibration errors of a rotary-wing unmanned aerial vehicle (UAV)-borne SAR, as provided in an embodiment of the present invention.

[0053] Figure 2 This is another flowchart illustrating a method for estimating and compensating for multi-component high-frequency vibration errors of a rotary-wing UAV-borne SAR provided in an embodiment of the present invention;

[0054] Figure 3This is a flowchart illustrating the principle of extracting vibration components provided in an embodiment of the present invention;

[0055] Figure 4 This is a schematic diagram of an exemplary simulation imaging result provided in an embodiment of the present invention;

[0056] Figure 5 This is a schematic diagram of an exemplary actual imaging result provided by an embodiment of the present invention;

[0057] Figure 6 This is the embodiment of the present invention that provides the following: Figure 5 The diagram shows an enlarged view of the area outlined in the red dashed box. Detailed Implementation

[0058] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0059] Figure 1 This is a flowchart illustrating a method for estimating and compensating for multi-component high-frequency vibration errors of a rotary-wing unmanned aerial vehicle (UAV)-borne SAR, as provided in an embodiment of the present invention. Figure 2 This is another flowchart illustrating a method for estimating and compensating for multi-component high-frequency vibration errors in a rotary-wing UAV-borne SAR, as provided in this embodiment of the invention. Figure 2 The original data in the data is the echo signal, such as Figure 1 and Figure 2 As shown, the method includes:

[0060] S101. The range Doppler algorithm is used to coarsely focus the echo signal to obtain the coarsely focused signal.

[0061] S102. Extract each high-frequency vibration component contained in the coarsely focused signal, estimate the parameters of each high-frequency vibration component, construct a compensation function for each high-frequency vibration component using the estimated parameters of each high-frequency vibration component, and use the compensation function of the high-frequency vibration component to iteratively compensate the coarsely focused signal to obtain the echo signal after suppressing the high-frequency vibration components.

[0062] S103. Perform residual error correction on the echo signal after suppressing the high-frequency vibration component to obtain the high-resolution imaging result after removing the multi-component high-frequency vibration error.

[0063] In this invention, the aforementioned echo signal is the echo signal of a rotary-wing UAV-borne SAR, and the specific expression of the echo signal S0(τ,η) is as follows:

[0064]

[0065] Where S0(τ,η) is a two-dimensional time-domain SAR echo signal, τ represents the range time, η represents the azimuth time, and wr (τ) and w a (η) represents the envelopes in the distance time domain and the orientation time domain, respectively, R o (η) represents the ideal slant range history of the UAV platform, M represents the number of high-frequency vibration components, M is an integer greater than or equal to 1, a i f i , Let fi represent the amplitude, frequency, and phase (i.e., initial phase) of the i-th high-frequency vibration component, respectively; let c represent the distance-time; f0 represent the carrier frequency of the signal; and Ki represent the initial phase. r The frequency modulation of the received signal is expressed, where exp represents exponential operation to base e, j represents the imaginary unit sign, and... π represents the value of Pi.

[0066] In this invention, S101 is achieved through the following steps:

[0067] S1011. Perform range-dimensional Fourier transform, range-direction pulse compression, and azimuth-dimensional Fourier transform on the echo signal in sequence to obtain a two-dimensional frequency domain signal.

[0068] Specifically, by first performing a range-dimensional Fourier transform on the echo signal, the range-frequency domain azimuth-time domain signal S1(f) can be obtained. τ After that, the range pulse compression function is compared with the range-frequency domain azimuth-time domain signal S1(f τ Multiplying by η, we obtain the range-frequency domain azimuth-time domain signal S2(f) after range pulse compression. τ Next, the range-frequency domain azimuth-time domain signal S2(f) after range pulse compression is processed. τ Perform an azimuth-dimensional Fourier transform on η) to obtain the two-dimensional frequency domain signal S3(f) τ ,f η ).

[0069] Specifically, S1(f τ The specific expression for S1(f,η) is: τ ,η)=∫S0(τ,η)exp(-j2πf τ τ)dτ,f τ Let ∫dτ represent the range frequency, and ∫dτ represent the integration operation over the range time. The specific expression for the range pulse compression function is: Among them, H1(f τ η) represents the distance pulse compression function. S3(f τ ,f η The specific expression for S3(f) is: τ ,f η )=∫S2(f τ ,η)exp(-j2πf η η)dη,f η∫dη represents the azimuth frequency, and ∫dη represents the integration operation on the azimuth time.

[0070] S1012. Perform range migration correction and range-dimensional inverse Fourier transform on the two-dimensional frequency domain signal in sequence to obtain the range-time-domain azimuth-frequency domain signal.

[0071] Specifically, the two-dimensional frequency domain signal is first multiplied by the range migration correction function to obtain the range-migration-corrected two-dimensional frequency domain signal S4(f). τ ,f η Then, a range-dimensional inverse Fourier transform is performed on the two-dimensional frequency domain signal after range migration correction to obtain the range-time azimuth frequency domain signal S5(τ,f). η ).

[0072] Specifically, the expression for the distance migration correction function is as follows: H2(f τ ,f η ) represents the distance migration correction function, λ represents the wavelength of the signal, and R p S5(τ,f) represents the nearest slant distance to any target point P in the scene, and v represents the velocity of the platform (i.e., the rotary-wing drone). η The specific expression for ) is: ∫df τ This indicates that the distance frequency is being integrated.

[0073] S1013. Perform azimuth pulse compression and azimuth inverse Fourier transform on the range-time-domain and azimuth-frequency-domain signals in sequence to obtain the coarsely focused signal.

[0074] Specifically, first convert the distance-time-domain azimuth-frequency domain signal S5(τ,f) into a frequency domain signal. η Multiplying this by the azimuth pulse compression function yields the azimuth pulse-compressed range-time domain azimuth frequency domain signal S6(τ,f) η Next, the range-time domain azimuth-frequency domain signal S6(τ,f) after azimuth pulse compression is... η Perform an inverse Fourier transform in the azimuth dimension to obtain a two-dimensional time-domain signal S7(τ,η), which is the signal after coarse focusing.

[0075] Specifically, the specific expression for the azimuth pulse compression function is as follows: H3(τ,f η S7(τ,η) represents the azimuth pulse compression function. The expression for S7(τ,η) is: After formula rearrangement, the specific expression for S7(τ,η) is: X p This indicates the location of any target point P in the scene.

[0076] In this invention, S102 is achieved through the following steps:

[0077] S1021. In the m-th iteration, extract the vibration component from the compensation signal of the (m-1)-th iteration to obtain the m-th vibration component, and determine whether the m-th vibration component is a high-frequency vibration component; m is a positive integer greater than or equal to 1; when m is 1, the compensation signal of the (m-1)-th iteration is the signal after coarse focusing.

[0078] Specifically, after extracting the m-th vibration component, time-frequency analysis can be performed on the m-th vibration component to obtain its frequency. Then, by determining whether the frequency of the m-th vibration component belongs to the frequency range corresponding to the high-frequency vibration component, it can be determined whether the m-th vibration component is a high-frequency vibration component. If the frequency of the m-th vibration component belongs to the frequency range corresponding to the high-frequency vibration component, then the m-th vibration component is a high-frequency vibration component; otherwise, the m-th vibration component is not a high-frequency vibration component.

[0079] S1022. When the m-th vibration component is not a high-frequency vibration component, the compensation signal of the (m-1)th vibration component is used as the echo signal after suppressing the high-frequency vibration component.

[0080] S1023. When the m-th vibration component is a high-frequency vibration component, perform parameter estimation on the m-th vibration component and construct the compensation function for the m-th high-frequency vibration component using the parameters of the m-th vibration component.

[0081] S1024. The m-th high-frequency vibration component compensation function is used to compensate the signal after the (m-1)-th compensation to obtain the m-th compensation signal. This process is repeated until the echo signal after suppressing the high-frequency vibration component is obtained.

[0082] For example, when the coarsely focused signal has M high-frequency vibration components, the value of m is from 1 to M, so M iterations of compensation are required to suppress the high-frequency vibration components in the coarsely focused signal.

[0083] Here, the compensation function of the m-th high-frequency vibration component is multiplied by the signal after the (m-1)-th compensation to obtain the compensation signal of the m-th compensation.

[0084] In this invention, combined with Figure 3 As shown, the step of "extracting the vibration component from the (m-1)th compensation signal to obtain the mth vibration component" in S1021 above can be achieved through steps S1 to S5, wherein, Figure 2 The original data in the data is the compensation signal of the (m-1)th iteration.

[0085] S1. Select the N range unit signals with the strongest energy from the (m-1)th compensation signal; N is a preset positive integer.

[0086] Here, the energy of the signal of each distance unit in the (m-1)th compensation signal is calculated. Based on the calculated energy, the N distance unit signals with the strongest energy are selected from the (m-1)th compensation signal. The value of N can be set according to actual needs, and this invention does not limit it.

[0087] S2. Determine the phase information of each of the N range cell signals to obtain N phase information corresponding to each of the N range cell signals.

[0088] Here, phase extraction and dewinding are performed on each of the selected N range cell signals to obtain the phase information P of the selected N range cell signals. n (η), where n = 1, ..., N represents the sequence of selected distance units.

[0089] S3. Add different noises to each of the N phase information to obtain N signals to be processed, each corresponding to one of the N phase information.

[0090] Here, P represents the phase information of the selected N range cell signals. n (η) Random white noise with different amplitudes is added to each signal to obtain N signals I to be processed. n (η).

[0091] S4. Based on the N signals to be processed, determine the N intrinsic mode function components.

[0092] Here, (2d) the average envelope of each of the N signals to be processed is calculated, resulting in N average envelopes; (2e) the nth average envelope is subtracted from the corresponding nth signal to be processed to obtain the nth intermediate signal. Thus, N intermediate signals M are obtained. n (η); (2f) sequentially determine whether the N intermediate signals satisfy the following conditions: (i) the number of extreme points and zero-crossing points are equal or differ by one; (ii) the upper envelope and lower envelope are locally symmetrical with respect to the time axis; the intermediate signals that satisfy the conditions are regarded as intrinsic mode function components, and the intermediate signals that do not satisfy the conditions are regarded as new signals to be processed. Repeat steps (2d) to (2f) until N intrinsic mode function components (IMF) are obtained. n (η).

[0093] S5. Average the N intrinsic mode function components to obtain the average value of the components, and take the average value of the components as the m-th vibration component.

[0094] Specifically, the m-th vibration component S h The expression for (η) is:

[0095] In this invention, the "parameter estimation of the m-th vibration component" in S1023 above can be achieved through the following steps:

[0096] S11. Based on the frequency of the m-th vibration component, construct a basis function to describe the m-th vibration component; the basis function contains the amplitude to be estimated and the initial phase to be estimated.

[0097] Here, the expression for the basis function of the m-th vibration component is:

[0098]

[0099] in, Let m be the basis function representing the m-th vibration component. This represents the frequency of the m-th vibration component. and This represents the amplitude and initial phase to be estimated for the m-th vibration component.

[0100] S12. Based on the basis functions, determine the expression for the correlation coefficient used to describe the relationship between the m-th vibration component and the echo signal.

[0101] Here, the expression for the correlation coefficient is as follows:

[0102]

[0103] in, Represents the correlation coefficient. Let S(η) represent the basis function, S(η) represent the phase of the echo signal, η represent the azimuth time, and j represent the imaginary unit sign. This indicates the magnitude to be estimated. This indicates the initial phase to be estimated. Let λ represent the frequency of the m-th vibration component, λ represent the wavelength, and ∫dη represent the integration operation of the direction and time.

[0104] S13. Based on the expression for the correlation coefficient, search for the correlation coefficient within the preset amplitude range and the preset initial phase range to obtain the maximum value of the correlation coefficient.

[0105] S14. The amplitude and initial phase used to generate the maximum value of the correlation coefficient are used as parameters for the estimated m-th vibration component.

[0106] Specifically, the magnitude and initial phase of the maximum value of the correlation coefficient are expressed as follows: `max` represents the maximum value operation. This indicates the magnitude of the maximum value of the generated correlation coefficient. This represents the initial phase of the magnitude of the maximum value of the correlation coefficient.

[0107] In this invention, the parameters of the m-th vibration component include: the frequency, amplitude, and initial phase of the m-th vibration component; the expression of the compensation function for the m-th high-frequency vibration component constructed through S1023 is as follows:

[0108]

[0109] Among them, H h (η) represents the compensation function for the m-th high-frequency vibration component. This represents the amplitude of the m-th vibration component. This represents the initial phase of the m-th vibration component. This represents the frequency of the m-th vibration component.

[0110] In this invention, S103 is achieved through the following steps:

[0111] S1031. Divide the echo signal after suppressing the high-frequency vibration component into sub-apertures to obtain multiple sub-aperture signals.

[0112] Specifically, the number of sub-aperture signals can be set according to actual needs, and this invention does not limit this.

[0113] S1032. Determine the optimal phase error compensation function for each sub-aperture signal, and the linear phase offset between the sub-aperture signals.

[0114] Specifically, S1032 is implemented through the following steps:

[0115] S10. Divide each sub-aperture signal into range blocks, and select the T range blocks with the strongest energy from the multiple range blocks obtained as the T estimated samples of the sub-aperture signal; T is a positive integer greater than 1.

[0116] Here, the specific value of T can be set according to actual needs, and this invention does not limit it.

[0117] S20. Use the phase gradient self-focusing algorithm to determine the phase error gradient of each estimated sample of the sub-aperture signal, and obtain the T phase error gradients of the sub-aperture signal.

[0118] Here, the expression for the phase error gradient of the t-th estimated sample of each sub-aperture signal is:

[0119]

[0120] Where, φ t (η) represents the phase error gradient of the t-th estimated sample of each sub-aperture signal, G t (η) represents the t-th estimated sample of the sub-aperture signal. G represents tThe conjugate transpose of (η).

[0121] S30. Integrate the T phase error gradients of the sub-aperture signal respectively to obtain T phase error compensation functions.

[0122] S40. Use T phase error compensation functions to pre-compensate the residual error of the sub-aperture signal respectively, and evaluate the pre-compensation effect of the T phase error compensation functions respectively.

[0123] Here, T phase error compensation functions can be used to pre-compensate the residual error of the sub-aperture signal, thereby obtaining T pre-compensated sub-aperture signals. These T pre-compensated sub-aperture signals are then used for imaging, resulting in T images. The entropy of these T images is then calculated, yielding T entropy values, which represent the pre-compensation effect of the corresponding T phase error compensation functions.

[0124] S50. Select the phase error compensation function with the best pre-compensation effect as the optimal phase error compensation function corresponding to the sub-aperture signal.

[0125] S60. Perform Doppler frequency modulation offset estimation processing on the sub-aperture signal to obtain the linear phase offset between the sub-aperture signals.

[0126] S1033. Based on the linear phase shift between the sub-aperture signals, construct the sub-aperture phase error compensation function corresponding to the sub-aperture signal, and based on the sub-aperture phase error compensation function, construct the full aperture phase error compensation function.

[0127] Here, based on the linear phase shift between the sub-aperture signals, an initial sub-aperture phase error compensation function corresponding to the sub-aperture signal is constructed; the linear component in the initial sub-aperture phase error compensation function corresponding to the sub-aperture signal is removed to obtain the sub-aperture phase error compensation function; the multiple sub-aperture phase error compensation functions corresponding to multiple sub-aperture signals are concatenated to obtain the full aperture phase error compensation function.

[0128] S1034. The full aperture phase error compensation function is used to correct the residual error of the echo signal after suppressing the high-frequency vibration component, so as to obtain the high-resolution imaging result after removing the multi-component high-frequency vibration error.

[0129] Here, the full aperture phase error compensation function is multiplied with the echo signal after suppressing the high-frequency vibration component to complete the correction of the residual error, thus obtaining the high-resolution imaging result after completely removing the multi-component high-frequency vibration error.

[0130] This invention first extracts multiple components of high-frequency vibration error from the echo signal sequentially through cyclic processing, then estimates and compensates for them, improving the performance of the high-frequency vibration error compensation method and effectively enhancing the imaging quality of rotorcraft UAV-borne SAR. After compensating for the high-frequency vibration error, an improved phase error gradient self-focusing method is used to correct the residual phase error, further improving the accuracy and robustness of the high-frequency vibration error compensation, broadening its applicability, and effectively expanding the application fields of rotorcraft UAV-borne SAR. Therefore, this invention enhances the performance and robustness of the high-frequency vibration error compensation method, is applicable to rotorcraft UAV platforms with multi-component high-frequency vibration errors, and effectively improves the imaging quality of rotorcraft UAV-borne SAR.

[0131] The following simulation experiments further illustrate the technical effects achievable by this invention.

[0132] The simulation parameters are shown in Table 1, and the simulation imaging results are as follows: Figure 4 As shown, the present invention clearly has a good focusing effect on point targets in the simulation scene, thus verifying the effectiveness of the present invention.

[0133] Table 1 Simulation Parameters

[0134]

[0135] Figure 5 The figure shows the measured results obtained using the method of the present invention, wherein... Figure 6 It is Figure 5 The diagram shows an enlarged view of the area outlined in the red dashed box. (See diagram below.) Figure 5 and Figure 6 As shown, the present invention has a good focusing effect on strong scattering points in real-world scenarios, thus verifying the effectiveness of the present invention.

[0136] This invention can be applied to the field of SAR imaging on future rotary-wing UAVs, solving the problem of multi-component high-frequency vibration errors in rotary-wing UAV platforms, improving the imaging quality of SAR on rotary-wing UAVs, and enabling more flexible and rapid perception of regions of interest, thus possessing great application potential. As an important supplement to traditional airborne SAR, multi-rotor UAV-borne SAR has advantages such as low cost, simple operation, strong survivability, and low requirements for the operating environment and take-off and landing sites, which can improve convenience for many fields such as environmental reconnaissance, environmental protection, agricultural production, and emergency rescue. In environmental reconnaissance, this invention can efficiently detect areas that need to be reconnoitered, thereby obtaining effective information; in environmental protection, this invention can monitor sewage discharge, marine oil spills, etc. in real time, improving the work efficiency of relevant personnel and contributing to the construction of green mountains and clear waters; in agricultural production, this invention can monitor the growth and planting of crops, vegetation cover, etc., helping farmers to implement scientific agricultural production and increase crop yields; in emergency rescue, this invention can accurately, safely, and efficiently obtain first-hand information on the rescue area, helping rescuers plan rescue strategies, improving rescue efficiency, and saving lives and property.

[0137] It should be noted that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0138] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

[0139] In this specification, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. While different embodiments may describe certain measures, this does not mean that these measures cannot be combined to produce a good effect.

[0140] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A method for estimating and compensating for multi-component high-frequency vibration errors in rotary-wing UAV-borne SAR, characterized in that, include: The range Doppler algorithm is used to coarsely focus the echo signal to obtain the coarsely focused signal. Extract each high-frequency vibration component contained in the coarsely focused signal, estimate the parameters of each high-frequency vibration component, construct a compensation function for each high-frequency vibration component using the estimated parameters of each high-frequency vibration component, and use the compensation function of the high-frequency vibration component to iteratively compensate the coarsely focused signal to obtain the echo signal after suppressing the high-frequency vibration components. The echo signal after suppressing the high-frequency vibration component is subjected to residual error correction to obtain a high-resolution imaging result after removing the multi-component high-frequency vibration error.

2. The method for estimating and compensating for multi-component high-frequency vibration errors of SAR on a rotary-wing unmanned aerial vehicle according to claim 1, characterized in that, The process involves extracting each high-frequency vibration component from the coarsely focused signal, estimating the parameters of each high-frequency vibration component, constructing a compensation function for each high-frequency vibration component using the estimated parameters, and iteratively compensating the coarsely focused signal using the compensation function to obtain the echo signal after suppressing the high-frequency vibration components. This includes: In the m-th iteration, the vibration component is extracted from the (m-1)-th compensation signal to obtain the m-th vibration component. It is then determined whether the m-th vibration component is a high-frequency vibration component. m is a positive integer greater than or equal to 1. When m is 1, the (m-1)-th compensation signal is the signal after coarse focusing. When the m-th vibration component is not a high-frequency vibration component, the (m-1)-th compensation signal is used as the echo signal after suppressing the high-frequency vibration component. When the m-th vibration component is a high-frequency vibration component, parameter estimation is performed on the m-th vibration component, and the parameters of the m-th vibration component are used to construct a compensation function for the m-th high-frequency vibration component. The m-th high-frequency vibration component compensation function is used to compensate the signal after the (m-1)-th compensation to obtain the m-th compensated signal. This process is repeated until the echo signal after suppressing the high-frequency vibration component is obtained.

3. The method for estimating and compensating for multi-component high-frequency vibration errors of SAR on a rotary-wing unmanned aerial vehicle according to claim 2, characterized in that, The extraction of vibration components from the (m-1)th compensation signal to obtain the m-th vibration component includes: Select the N strongest distance unit signals from the (m-1)th compensation signal; N is a preset positive integer; Determine the phase information of each of the N distance unit signals to obtain N phase information corresponding to the N distance unit signals; Different noises are added to the N phase information to obtain N signals to be processed, each corresponding to one of the N phase information. Based on the N signals to be processed, determine N intrinsic mode function components; The average value of the N intrinsic mode function components is obtained by averaging the components, and the average value of the components is taken as the m-th vibration component.

4. The method for estimating and compensating for multi-component high-frequency vibration errors of rotary-wing UAV-borne SAR according to claim 2, characterized in that, The parameter estimation for the m-th vibration component includes: Based on the frequency of the m-th vibration component, a basis function is constructed to describe the m-th vibration component; the basis function contains the amplitude to be estimated and the initial phase to be estimated; Based on the basis functions, an expression for the correlation coefficient describing the relationship between the m-th vibration component and the echo signal is determined; Based on the expression for the correlation coefficient, the correlation coefficient is searched within a preset range of amplitude values ​​and a preset range of initial phase values ​​to obtain the maximum value of the correlation coefficient; The amplitude and initial phase used to generate the maximum value of the correlation coefficient are used as parameters for the estimated m-th vibration component.

5. The method for estimating and compensating for multi-component high-frequency vibration errors of rotary-wing UAV-borne SAR according to claim 4, characterized in that, The expression for the correlation coefficient is as follows: in, This represents the correlation coefficient. Let S(η) represent the basis function, S(η) represent the phase of the echo signal, η represent the azimuth time, and j represent the imaginary unit sign. This indicates the magnitude to be estimated. This represents the initial phase to be estimated. Let λ represent the frequency of the m-th vibration component, λ represent the wavelength, and ∫dη represent the integration operation of the azimuth time.

6. The method for estimating and compensating for multi-component high-frequency vibration errors of SAR on a rotary-wing unmanned aerial vehicle according to claim 2, characterized in that, The parameters of the m-th vibration component include: the frequency, amplitude, and initial phase of the m-th vibration component; the expression of the compensation function for the m-th high-frequency vibration component is as follows: Among them, H h (η) represents the compensation function for the m-th high-frequency vibration component, where η represents the azimuth time and j represents the imaginary unit sign. This represents the amplitude of the m-th vibration component. This represents the initial phase of the m-th vibration component. Let λ represent the frequency of the m-th vibration component, and λ represent the wavelength of the signal.

7. The method for estimating and compensating for multi-component high-frequency vibration errors of SAR on a rotary-wing unmanned aerial vehicle according to claim 1, characterized in that, The residual error correction of the echo signal after suppressing the high-frequency vibration components to obtain a high-resolution imaging result after removing multi-component high-frequency vibration errors includes: The echo signal after suppressing the high-frequency vibration component is divided into sub-apertures to obtain multiple sub-aperture signals; Determine the optimal phase error compensation function for each sub-aperture signal, and the linear phase offset between the sub-aperture signals; Based on the linear phase shift between the sub-aperture signals, a sub-aperture phase error compensation function corresponding to the sub-aperture signal is constructed, and based on the sub-aperture phase error compensation function, a full-aperture phase error compensation function is constructed. The residual error of the echo signal after suppressing the high-frequency vibration component is corrected by using the full aperture phase error compensation function to obtain a high-resolution imaging result after removing the multi-component high-frequency vibration error.

8. The method for estimating and compensating for multi-component high-frequency vibration errors of SAR on a rotary-wing unmanned aerial vehicle according to claim 7, characterized in that, The step of determining the optimal phase error compensation function for each sub-aperture signal and the linear phase shift between the sub-aperture signals includes: For each sub-aperture signal, range blocks are divided, and T range blocks with the strongest energy are selected from the obtained range blocks as T estimated samples of the sub-aperture signal; T is a positive integer greater than 1. The phase error gradient of each estimated sample of the sub-aperture signal is determined by using the phase gradient self-focusing algorithm, thus obtaining T phase error gradients of the sub-aperture signal; By integrating the T phase error gradients of the sub-aperture signal respectively, T phase error compensation functions are obtained; The residual error of the sub-aperture signal is pre-compensated using the T phase error compensation functions respectively, and the pre-compensation effect of the T phase error compensation functions is evaluated respectively. Select the phase error compensation function with the best pre-compensation effect as the optimal phase error compensation function corresponding to the sub-aperture signal; The Doppler frequency modulation offset estimation process is performed on the sub-aperture signal to obtain the linear phase offset between the sub-aperture signals.

9. A method for estimating and compensating for multi-component high-frequency vibration errors of SAR on a rotary-wing unmanned aerial vehicle (UAV) according to claim 7, characterized in that, The step of constructing a sub-aperture phase error compensation function corresponding to the sub-aperture signal based on the linear phase shift between the sub-aperture signals, and constructing a full-aperture phase error compensation function based on the sub-aperture phase error compensation function, includes: Based on the linear phase shift between the sub-aperture signals, an initial sub-aperture phase error compensation function corresponding to the sub-aperture signal is constructed. Remove the linear component from the initial sub-aperture phase error compensation function corresponding to the sub-aperture signal to obtain the sub-aperture phase error compensation function; The phase error compensation functions of the multiple sub-aperture signals, which correspond one-to-one with each other, are concatenated to obtain the full aperture phase error compensation function.

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