An improved Omega-K imaging method suitable for squinted SAR with curved trajectory

Abstract

The application relates to an improved Omega-K imaging method suitable for curve trajectory squinted SAR and belongs to the field of radar signal processing. Two-dimensional Fourier transformation is carried out on two-dimensional time domain echo data acquired by the curve trajectory squinted SAR, and the two-dimensional time domain echo data are transformed into a two-dimensional frequency domain; after the two-dimensional frequency domain signals are multiplied by a reference function and specially Stolt mapping, the signals are transformed into a range Doppler domain by using inverse Fourier transformation in the range direction; range migration correction, azimuth compression and azimuth offset correction are carried out in the range Doppler domain; inverse Fourier transformation is carried out in the azimuth direction, the signals are transformed into two-dimensional time domain, and finally, imaging results are obtained. The application utilizes a specially designed Stolt mapping to avoid complex additional range migration caused by a traditional Stolt mapping, and then range migration correction and azimuth compression are sequentially carried out in the range Doppler domain to solve the problem that equivalent radar speed and other parameters cannot change with distance.

Description

Technical Field

[0001] This invention belongs to the field of radar signal processing, specifically relating to a method for accurate imaging of synthetic aperture radar on curved motion trajectories. Background Technology

[0002] Synthetic Aperture Radar (SAR) is an active microwave remote sensing system for Earth observation. Compared to traditional optical remote sensing, it can operate around the clock and in all weather conditions, unaffected by weather conditions, and also possesses a certain degree of penetration, making it an important means for humans to obtain information about ground features. Therefore, SAR has been widely used in military and civilian fields, such as battlefield reconnaissance, marine surveillance, land surveying, topographic mapping, environmental and disaster monitoring, crop surveys, and resource exploration.

[0003] With the development of SAR, more and more SAR systems are being installed on small aircraft, enabling them to fly on flexible, curved trajectories. Meanwhile, high-altitude spaceborne SAR systems are also attracting increasing attention. Because the integration time increases with orbital altitude, their curved trajectories can no longer be approximated as linear trajectories. The Omega-K algorithm (ωKA) is a well-known, accurate imaging algorithm for large apertures and is widely used in SAR imaging. However, this algorithm is designed for straight trajectories. In the case of curved trajectories, the variation of the equivalent radar velocity with distance limits its ability to handle wide-range mapping strips. To make the Omega-K algorithm applicable to curved trajectories, various solutions have been proposed, including motion compensation, the introduction of equivalent radar velocity, and propagation phase decomposition. However, when the curved trajectory is very curved, these algorithms cannot solve the resulting large phase errors. Therefore, there is an urgent need to study accurate imaging algorithms for wide-range mapping strips under curved trajectories. Summary of the Invention

[0004] The technical problem to be solved by this invention is:

[0005] For accurate imaging of wide-range mapping strips of oblique-looking SAR with curved motion trajectories, this invention provides an improved Omega-K imaging method suitable for oblique-looking SAR with curved trajectories.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0007] An improved Omega-K imaging method for curved trajectory squint SAR, characterized by comprising:

[0008] Calculate the spatially varying parameters: equivalent closest distance, equivalent radar velocity, equivalent beam center crossing time, and beam center crossing time;

[0009] A two-dimensional Fourier transform is performed on the two-dimensional time-domain echo data acquired by the curved trajectory squint SAR to transform it into the two-dimensional frequency domain.

[0010] After multiplying the two-dimensional frequency domain signal by a reference function and performing a special Stolt mapping, the signal is then transformed into the range-Doppler domain using an inverse Fourier transform in the range direction.

[0011] The reference function multiplication filter is designed by setting the equivalent nearest distance and equivalent radar velocity in the phase term of the two-dimensional frequency domain signal to the values ​​at the reference point at the center of the scene.

[0012] The special Stolt mapping described above: Compared to the traditional Stolt mapping, it only maps out the residual secondary range compression term caused by the equivalent nearest distance spatial variation, while retaining the residual azimuth compression term and the residual range migration correction term.

[0013] The range migration correction and the first filter are designed using the equivalent closest distance and the equivalent radar velocity. The second filter is designed using the equivalent beam center crossing time and the beam center crossing time. The range migration correction, the first filter, and the second filter are used to perform range migration correction, azimuth compression, and azimuth offset correction in the range Doppler domain, respectively.

[0014] An inverse Fourier transform is performed in the azimuth direction to transform the signal into a two-dimensional time domain, yielding the final imaging result.

[0015] A further technical solution of the present invention: the equivalent closest distance, equivalent radar velocity, equivalent beam center crossing time, and beam center crossing time are respectively:

[0016]

[0017]

[0018]

[0019] t ar =(tanθ) ac ) / W s

[0020] θ ac =θ a (t ac )

[0021] Among them, R 0r For equivalent closest distance, V r For equivalent radar velocity, t ar For the equivalent beam center crossing time, t acThe moment the beam center crosses; t a It's about the location and time, W s R is the radar angular velocity, R0 is the closest distance between the radar and the target, and R s R is the radar's radius of motion. g Let θ be the distance from the target to the center of the circle. a The instantaneous angle between the radar and the target with O as the center is given.

[0022] A further technical solution of the present invention: the expression of the two-dimensional frequency domain signal is as follows:

[0023] S 2df (f r ,f a ) = W r (f r W a (f a -f ac )exp{jθ 2df (f r ,f a )} (1)

[0024] in,

[0025]

[0026]

[0027] Among them, f a It is the azimuth frequency, f ac It is the Doppler center frequency, f r It is the distance frequency, W r (f r ) = w r (f r / K r ) is the distance-frequency envelope, W a (f a -f ac ) is the azimuth frequency envelope, c is the speed of light, and K is the azimuth frequency envelope. r For distance frequency modulation, θ 2df (f r ,f a () represents the phase of the signal.

[0028] A further technical solution of the present invention: the expression of the reference function multiplication filter is as follows:

[0029]

[0030] Among them, R 0r_ref and V r_ref These are the reference points at the center of the scene, R. 0r and Vr The value of .

[0031] A further technical solution of the present invention: the special Stolt mapping expression is as follows:

[0032]

[0033] Wherein, D(f) a V r_ref ) is the migration factor.

[0034] A further technical solution of the present invention: the correction amount for the distance migration is:

[0035]

[0036] A further technical solution of the present invention: the first filter:

[0037]

[0038] A further technical solution of the present invention: the second filter:

[0039] H asc (t r ,f a )=exp{-2πf a (t ar -t ac )} (8).

[0040] A computer system is characterized by comprising: one or more processors, and a computer-readable storage medium for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the method described above.

[0041] A computer-readable storage medium is characterized by storing computer-executable instructions, which, when executed, are used to implement the above-described method.

[0042] The beneficial effects of this invention are as follows:

[0043] This invention provides an improved Omega-K imaging method suitable for curved trajectory squint SAR. It utilizes a specially designed Stolt mapping to avoid the complex additional range migration (RCM) caused by traditional Stolt mapping. Subsequently, in the range Doppler domain, range migration correction (RCMC) and azimuth compression are used to solve the problem that parameters such as equivalent radar velocity cannot change with range.

[0044] Besides its Stolt mapping differing from the traditional Omega-K algorithm, the improved Omega-K algorithm adds two steps: range migration correction and orientation compression in the range-Doppler domain. These two steps are what guarantee the equivalent closest distance R. 0r And equivalent radar velocity V r These two spatially variable parameters can change with distance, thereby reducing phase error, achieving more accurate imaging, expanding the width of the distance mapping zone, and providing technical theoretical support for accurate imaging of wide-distance mapping zone data under curved trajectories. Attached Figure Description

[0045] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0046] Figure 1 Flowchart of the improved Omega-K algorithm.

[0047] Figure 2 A simplified geometric model of a curved trajectory slant-look SAR.

[0048] Figure 3 SAR imaging geometry and point target distribution.

[0049] Figure 4 Improve the imaging results of the Omega-K algorithm for targets.

[0050] Figure 5 Contour maps of targets T1-T3 processed using different imaging algorithms: (a) Range Doppler algorithm; (b) Traditional Omega-K algorithm; (c) Improved Omega-K algorithm. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0052] Figure 1 This is a flowchart of the improved Omega-K algorithm during imaging. Before imaging begins, it is necessary to determine the SAR system parameters and the imaging geometric model in advance, and derive the four spatially variable parameters in the hyperbolic model distance equation (1) so that they can be used in the specific imaging process.

[0053] by Figure 2Taking the simplified geometric model as an example, the instantaneous distance R(t) of the radar reaching the target can be derived from its geometric relationship. a )as follows:

[0054]

[0055] Where, θ a (t a )=-W s t a Radar angular velocity W s =V s / R s R0, R s and R g like Figure 2 As shown. The range model frequently used in SAR imaging is the hyperbolic model. To derive the range equation applicable to the hyperbolic model, we can perform a function on the radar's position vector with respect to t. a The second-order Taylor expansion, that is, the expansion of cosθ in (24) a (t a Crossing the beam center at time t ac Perform a second-order Taylor expansion at this point:

[0056]

[0057] Where θ ac =θ a (t ac Substituting (25) into (24), we can obtain a hyperbola distance model similar to (1):

[0058]

[0059] in,

[0060]

[0061]

[0062] t ar =(tanθ) ac ) / W s (29)

[0063] t ac =θ ac / W s (30)

[0064] Thus, from (27), (28), (29), and (30), we can obtain the expressions for the following four space-varying parameters: Equivalent nearest distance R 0r Equivalent radar velocity V rEquivalent beam center crossing time t ar and the beam center crossing time t ac Ultimately, what we need to establish is the relationship between space-varying parameters and distance / time t. r The relationship between them needs to be determined, therefore it is also necessary to derive R. g , R0 and θ ac With t r The relationship between the beam center and the distance R from the radar to the target at the moment the beam center crosses the target. c =R(t) ac ) and t r The relationship between them is as follows:

[0065]

[0066] according to Figure 2 The geometric relationships can be used to derive R. g , R0 and θ ac With R c The relationship between them is:

[0067]

[0068] R0 = R s -R g (33)

[0069]

[0070] Where θ sq The angle of view is oblique. From formulas (27)-(34), the four spatially variable parameters R can finally be obtained. 0r V r t ar and t ac With distance and time t r The relationship between them.

[0071] After calculating all the parameters required for imaging, the next step is to... Figure 1 Imaging is performed using the imaging flowchart in the diagram. The specific steps are as follows:

[0072] Step 1, Two-dimensional Fourier Transform (2-D FFT). Perform a two-dimensional Fourier transform on the two-dimensional time-domain echo data acquired by the curved trajectory squint SAR to transform it into the two-dimensional frequency domain.

[0073] Taking a single point target as an example, before constructing its two-dimensional time-domain echo, it is necessary to determine the distance equation of its hyperbolic model. Under the model of the hyperbolic trajectory, the distance equation of the point target can be expressed as follows:

[0074]

[0075] Among them, ta It is the azimuth time slow, R(t) a The instantaneous distance at which the radar reaches the target is denoted as , in addition to which there are four other parameters that vary in space: equivalent closest distance R. 0r Equivalent radar velocity V r Equivalent beam center crossing time t ar and the beam center crossing time t ac In a scenario with a curved trajectory, the four equivalent parameters of a target at different radial distances from the radar will differ; therefore, they are called spatially variable parameters. Omitting complex constants and removing the carrier frequency, the two-dimensional time-domain echo signal of this point target can be expressed as follows:

[0076]

[0077] Where c is the speed of light, t r It is a distance over time, w r (·) represents the distance-time envelope, w a (·) represents the azimuth-time envelope, K r It is the distance frequency modulation, f0 is the carrier frequency, R(t) a As shown in (1), using the principle of stationary phase, the signal expression of the two-dimensional time domain signal (2) after the two-dimensional Fourier transform can be derived as follows:

[0078] S 2df (f r ,f a ) = W r (f r W a (f a -f ac )exp{jθ 2df (f r ,f a (3)

[0079] Among them, f r It is the distance frequency, W r (f r ) = w r (f r / K r ) is the distance-frequency envelope, f a It is the azimuth frequency, f ac It is the Doppler center frequency, W a (f a -f ac ) is the azimuth frequency envelope, θ 2df (f r ,f a () represents the phase of the signal, expressed as follows:

[0080]

[0081] in,

[0082]

[0083] Among them, R 0r V r t ar and t ac All are variable with distance and time t r The space-varying parameter K changes with the change. r This is explained by the increased distance and frequency modulation.

[0084] Step 2: Multiply the reference functions.

[0085] R in phase (4) 0r and V r By setting the parameters to their values ​​at the reference point at the center of the scene, the expression for the reference function multiplication filter can be obtained as follows:

[0086]

[0087] Where R 0r_ref and V r_ref These are the reference points at the center of the scene, R. 0r and V r The value of . After filtering by multiplying the reference function, we can obtain the expression for the filtered residual phase from (4) and (6):

[0088]

[0089] Step 3, Specially Designed Stolt Mapping. To avoid the complex additional distance migration caused by the Stolt mapping in the traditional Omega-K algorithm, we will redesign a new Stolt mapping.

[0090] From (5), it can be deduced that U(f) r ,f a V r ) for f r The result after Taylor expansion is as follows:

[0091]

[0092] Among them, o(f r D(f) is a higher-order infinitesimal in a Taylor expansion. a V r This can be called the migration factor, expressed as follows:

[0093]

[0094] Based on (8), we designed a special Stolt mapping as follows:

[0095]

[0096] Where f r ' is the new distance frequency obtained after mapping. After this mapping, U(f) can be... r ,f a V r_ref ) item (8) regarding f r higher-order infinitesimal term o(f) r Mapping it out, this step can achieve the mapping from R 0r Residual quadratic distance compression caused by spatial variation. Due to o(f r The value is very small, thus greatly reducing the migration of the mapping.

[0097] To obtain the phase expression of the signal after the phase of signal (7) is mapped by (10), its derivation is performed below. Combining (5), formula (10) can be transformed into:

[0098]

[0099] Then, substitute the above equation into U(f) r ,f a V r From (7), we can obtain U(f) in (7). r ,f a V r After mapping (10), it becomes:

[0100]

[0101] For N(f) r ',f a V r ) to conduct research on f r Taylor expansion of ':

[0102]

[0103] Finally, substituting (10) and (12) into (7), we can obtain the signal phase after mapping (7) through (10):

[0104]

[0105] Among them, o'(f) in (13) r The ') is ignored, which indicates that it is caused by V r The residual secondary distance compression (SRC) caused by the spatial variation can be ignored.

[0106] Step 4, Inverse Fourier Transform in the Range Direction. After multiplication with the reference function and a specially designed Stolt mapping in the two-dimensional frequency domain, the signal is transformed to the range-Doppler domain using the inverse Fourier transform in the range direction. The signal phase (14) becomes the following after the inverse Fourier transform in the range direction:

[0107]

[0108] in,

[0109]

[0110] Step 5, Distance Migration Correction (RCMC). As can be seen from (15), the distance migration correction amount is:

[0111]

[0112] As can be seen from the above equation, distance migration correction in the distance-Doppler domain can guarantee the equivalent closest distance R. 0r And equivalent radar velocity V r These two spatially variable parameters can vary with distance and time t r The phase error can be reduced by changing the phase of the phase. Because D(f) changes... ac V r ) = R 0r / R c , where R c =R(t) ac Therefore, signal (15) after distance migration correction is:

[0113] S rcmc (t r ,f a ) = p r {t r -2(R c -R c_ref ) / c}W a (f a -f ac )exp{jθ rd (t r ,f a )}, (18)

[0114] Where R c_ref It is R c The value of θ at the reference point at the center of the scene. rd (t r ,f a (16) is given.

[0115] Step 6, azimuth compression and azimuth offset correction. Azimuth compression and azimuth offset correction are performed using the following two filters (19) and (20) to compensate for θ in (18). rd (t r ,f a Similar to the range migration correction in the previous step, azimuth compression and azimuth offset correction are also performed in the range-Doppler domain, thus ensuring the preservation of the spatially varying parameter R. 0r V r t ar and t ac With distance and time t r The phase error is reduced by the change in phase.

[0116]

[0117] H asc (t r ,f a )=exp{-2πf a (t ar -t ac (20)

[0118] Step 7: Inverse Fourier Transform in the Azimuth Direction. Finally, an inverse Fourier transform is performed in the azimuth direction to transform the signal into the two-dimensional time domain, obtaining the final imaging result.

[0119] In the improved Omega-K algorithm of this invention, the specially designed Stolt mapping, compared to the traditional Stolt mapping, has a smaller migration amount and therefore does not distort the data support domain (DSR) of the signal in the two-dimensional frequency domain, easily maximizing the use of DSR. More importantly, it does not introduce additional complex range migration correction in the range-frequency envelope as the traditional Stolt mapping, making subsequent complete correction of range migration possible. The traditional Stolt mapping is as follows:

[0120] U(f r ,f a V r_ref )=f0+f r ', (twenty one)

[0121] Where f r 'This is the new range frequency obtained after mapping. The range frequency envelope W before mapping. r (f r After mapping through equation (21), it becomes:

[0122]

[0123] It can be seen that the mapped distance-frequency envelope (22) is not only fr The function of ' is also f a This will result in additional range migration. In other words, the range migration is embedded not only in the mapped phase but also in the mapped range-frequency envelope. This makes it difficult to fully correct the range migration after a traditional Stolt mapping. However, if a specially designed Stolt mapping is used instead, the range-frequency envelope W... r (f r It will become:

[0124]

[0125] Because o(f) r ) is very small, while D(f) a V r_ref Since ) < 1, after the specially designed Stolt mapping, the above distance-frequency envelope remains essentially unchanged. At this point, it is not f a The function, but only f r The function is so that it does not cause additional range migration embedded in the range frequency envelope. Therefore, after using the special Stolt mapping, the range migration exists only in the mapped phase and can be easily corrected in the range-Doppler domain.

[0126] The improved Omega-K algorithm in this invention, besides differing from the traditional Omega-K algorithm in its Stolt mapping, adds two additional steps: range migration correction and azimuth compression in the range-Doppler domain. These two steps are what ensure the equivalent radar velocity V. r This spatially variable parameter can change with distance, thereby reducing phase error, achieving more accurate imaging, expanding the width of the distance mapping zone, and providing technical theoretical support for accurate imaging of wide-distance mapping zone data under curved trajectories.

[0127] In the above imaging process, it can be seen from (17) and (19) that both the range migration correction and azimuth compression steps require the use of the equivalent closest distance R. 0r Equivalent radar velocity V r These two spatially variable parameters enable the spatially variable parameters to change with distance, thereby reducing phase error; as can be seen from (20), the azimuth offset correction step requires the equivalent beam center crossing time t. ar and the beam center crossing time t acThese two spatially variable parameters enable precise positioning of the target after focusing. After imaging is completed, the two-dimensional coordinates of the image are obtained as azimuth time and range time. The azimuth time and the actual azimuth position of the target can be converted according to the radar speed, while the range time and the actual range position of the target need to be converted according to the parameter instantaneous distance (31).

[0128] The effects of this invention are further illustrated by the following simulation experiments.

[0129] The simulation parameters are listed in Table 1. The simulated point targets are distributed in the beam illumination area in the form of a 3×3 matrix, as shown below. Figure 3 As shown, the three leftmost targets and the three rightmost targets are evenly distributed along the left and right edges of the range strip, respectively. The width of the range strip, ΔR0, is approximately 8 km. Figure 4 The imaging results obtained by the proposed algorithm are shown.

[0130] Table I Simulation Parameters

[0131]

[0132] To demonstrate the superiority of the improved Omega-K algorithm of this invention, we analyzed... Figure 4 Imaging results of midpoint targets T1, T2, and T3 are presented, and the proposed algorithm is compared with the range-Doppler algorithm and the traditional Omega-K algorithm. Since the range migration correction and azimuth compression in the improved Omega-K algorithm are similar to those in the range-Doppler algorithm, the proposed algorithm is also compared with the range-Doppler algorithm.

[0133] The contour maps of the focused point targets obtained using these three algorithms are as follows: Figure 5 As shown. Since point target T2 is located at a reference distance, all three algorithms can accurately focus on the target. Furthermore, from... Figure 5 (a) It can be seen that, for the range-Doppler algorithm, point targets T1 and T3 exhibit range defocus. This is because the range-Doppler algorithm can only compensate for V. r The space variation cannot compensate for R in the secondary distance compression. 0r The space change. Furthermore, from Figure 5 (b) It can be seen that although the traditional Omega-K algorithm can compensate for R in the second distance compression 0r The space variation, but it ignores V. r Due to the spatial variation, point targets T1 and T3 exhibit azimuth defocus. Finally, from Figure 5 (c) It can be seen that the improved Omega-K algorithm of the present invention can make all point targets fully focused in both directions.

[0134] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the scope of the technology disclosed in the present invention, and such modifications or substitutions should all be covered within the scope of protection of the present invention.

Claims

1. An improved Omega-K imaging method suitable for curved trajectory squint SAR, characterized in that, include: Calculate the spatially varying parameters: equivalent closest distance, equivalent radar velocity, equivalent beam center crossing time, and beam center crossing time; A two-dimensional Fourier transform is performed on the two-dimensional time-domain echo data acquired by the curved trajectory squint SAR to transform it into the two-dimensional frequency domain. After multiplying the two-dimensional frequency domain signal by a reference function and performing a special Stolt mapping, the signal is then transformed into the range-Doppler domain using an inverse Fourier transform in the range direction. The reference function multiplication filter is designed by setting the equivalent nearest distance and equivalent radar velocity in the phase term of the two-dimensional frequency domain signal to the values ​​at the reference point at the center of the scene. The special Stolt mapping described above: Compared to the traditional Stolt mapping, it only maps out the residual secondary range compression term caused by the equivalent nearest distance spatial variation, while retaining the residual azimuth compression term and the residual range migration correction term. The range migration correction and the first filter are designed using the equivalent closest distance and the equivalent radar velocity. The second filter is designed using the equivalent beam center crossing time and the beam center crossing time. The range migration correction, the first filter, and the second filter are used to perform range migration correction, azimuth compression, and azimuth offset correction in the range Doppler domain, respectively. An inverse Fourier transform is performed in the azimuth direction to transform the signal into a two-dimensional time domain, thus obtaining the final imaging result.

2. The improved Omega-K imaging method for curved trajectory squint SAR according to claim 1, characterized in that, The equivalent closest distance, equivalent radar velocity, equivalent beam center crossing time, and beam center crossing time are respectively: θ ac = θ a ( t ac ) in, R 0r For equivalent closest distance, V r For equivalent radar velocity, t ar For the equivalent beam center crossing time, t ac The moment the beam center crosses; t a It's about the location and the time. W s For radar angular velocity, R 0 represents the closest distance between the radar and the target. R s The radius of motion of the radar. R g The distance from the target to the center of the circle. θ a The instantaneous angle between the radar and the target with O as the center is given.

3. The improved Omega-K imaging method for curved trajectory squint SAR according to claim 2, characterized in that, The expression for the two-dimensional frequency domain signal is as follows: (1) in, (2) (3) in, f a It is the azimuth frequency. f ac It is the Doppler center frequency. f r It is distance frequency. W r ( f r ) = w r ( f r / K r () is the distance-frequency envelope. W a ( f a f ac ) is the azimuth frequency envelope, and c is the speed of light. For distance frequency tuning, The phase of the signal.

4. The improved Omega-K imaging method for curved trajectory squint SAR according to claim 3, characterized in that, The expression for the reference function multiplication filter is as follows: (4) in, R 0r_ref and V r_ref These are the reference points at the center of the scene. R 0r and V r The value of .

5. An improved Omega-K imaging method for curved trajectory squint SAR according to claim 4, characterized in that, The special Stolt mapping expression is as follows: (5) in, As a migration factor, , It is the new distance frequency obtained after mapping.

6. An improved Omega-K imaging method for curved trajectory squint SAR according to claim 5, characterized in that, The correction amount for the distance migration is: (6)。 7. An improved Omega-K imaging method for curved trajectory squint SAR according to claim 6, characterized in that, The first filter: (7)。 8. An improved Omega-K imaging method for curved trajectory squint SAR according to claim 7, characterized in that, The second filter mentioned above: (8)。 9. A computer system, characterized in that... include: One or more processors, a computer-readable storage medium for storing one or more programs, wherein, when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to perform the method of any one of claims 1-8.

10. A computer-readable storage medium, characterized in that... The device stores computer-executable instructions, which, when executed, are used to implement the method described in any one of claims 1-8.

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