Simulation method of azimuth ambiguity ratio of sliding spotlight mode based on antenna pattern data
By using a simulation method based on antenna pattern data for the azimuth ambiguity ratio in the sliding spotting mode, the problem of simulation error in the existing technology for the sliding spotting mode is solved, and more accurate simulation results are achieved, providing a reliable evaluation basis for SAR system design.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- TIANJIN YUNYAO AEROSPACE TECH CO LTD
- Filing Date
- 2025-12-30
- Publication Date
- 2026-07-31
AI Technical Summary
Existing technologies fail to accurately simulate the azimuth blur ratio in sliding spotting mode, resulting in errors in imaging quality and system design optimization. There is a lack of methods for fitting time-varying azimuth patterns in sliding spotting mode and for weighted fusion of multiple sets of pattern data.
An azimuth ambiguity ratio (AASR) simulation method based on antenna pattern data is adopted. By calculating the platform position, azimuth beam pointing angle, oblique angle, antenna azimuth angle, and antenna pattern energy of the ambiguity signal, the azimuth ambiguity ratio (AASR) is accurately calculated, and simulation is performed using actual multi-scan angle pattern data.
It improves the accuracy of simulation results, making them closer to physical reality, providing a reliable evaluation basis for SAR system design, filling the gap in existing technology, and providing a systematic solution.
Smart Images
Figure CN121435555B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of SAR azimuth ambiguity ratio simulation, and in particular relates to a method for azimuth ambiguity ratio simulation of sliding beam pattern based on antenna pattern data. Background Technology
[0002] Azimuth Ambiguity-to-Signal Ratio (AASR), a core performance indicator of Synthetic Aperture Radar (SAR) systems, directly impacts imaging quality and system design optimization. Traditional AASR simulation methods are generally based on idealized antenna pattern models, assuming a fixed and symmetrical azimuth beam shape, suitable for static beam scanning scenarios such as stripe patterns. However, for sliding beamforming—which achieves high-resolution imaging by continuously adjusting the beam direction—the azimuth scanning angle dynamically changes over time, causing nonlinear distortion of the antenna pattern's main lobe and side lobe structures with the scanning angle. Continuing to use an idealized pattern or a single maximum scanning angle pattern for AASR calculations in this case will introduce significant errors by ignoring the real-time changes in the pattern.
[0003] Existing literature lacks simulation studies of AASR based on measured antenna patterns, and most of these studies focus on strip modes, failing to consider the coupling effect of dynamic pattern changes on fuzzy signals in sliding spotting mode. The method of using the maximum scan angle pattern as a global approximation cannot reflect the actual contribution of pattern sidelobe level fluctuations to fuzzy energy during scanning. There is a lack of azimuth time-varying pattern fitting methods for sliding spotting mode, and existing technologies have not solved the problem of weighted fusion of multiple sets of pattern data within the pulse repetition interval, resulting in systematic deviations between simulation results and physical reality. Summary of the Invention
[0004] In view of this, the present invention aims to propose a simulation method for the azimuth ambiguity ratio of the sliding beam pattern based on antenna pattern data, so as to improve the accuracy of the azimuth ambiguity ratio simulation results of SAR system and provide technical support for SAR system design.
[0005] To achieve the above objectives, the technical solution of the present invention is implemented as follows: A method for simulating the azimuth ambiguity ratio of a sliding beam-focusing mode based on antenna pattern data includes the following steps: S1. Calculate the platform position at any given time. ; S2. Calculate the azimuth beam pointing angle at any given time. ; S3. Calculate the oblique angle of the line connecting the platform and the target at any given moment. ; S4. Calculate the target's antenna azimuth angle. ; S5. Calculate the antenna azimuth angle corresponding to the m-th order ambiguity signal. ; S6. Calculate the main region energy of the antenna pattern corresponding to the antenna azimuth angle. and fuzzy region energy ; S7. Calculate the azimuth ambiguity ratio (AASR); In step S6, the calculation steps for the antenna pattern energy are as follows: S61, based on any given moment Find the scanning angle as The antenna pattern data file was obtained, and the amplitude and phase data information were extracted from it; S62. Based on the antenna azimuth angle at any given time. The phase and amplitude information extracted in step S61 are interpolated to obtain... Corresponding amplitude ; S63. The main region energy is obtained by integral calculation. and fuzzy region energy .
[0006] Furthermore, in step S1, the platform position for: ; in, Target location, unit: meters; The distance the beam covers on the ground, in meters; Platform speed, unit: meters per second; Wave foot velocity, unit: meters per second; The time for the beam to illuminate the point target, in seconds, where , For one synthesis aperture time; It represents the location of the platform at time t=0, in meters.
[0007] Furthermore, in step S2, the azimuth beam pointing angle for: ; in, The slant distance between the platform and the underground virtual point.
[0008] Furthermore, in step S3, the azimuth beam pointing angle for: ; in, This is the minimum slant distance from the beam center.
[0009] Furthermore, in step S4, the antenna azimuth angle for: .
[0010] Furthermore, in step S5, the antenna azimuth angle for: ; in, , 2×M+1 is the number of fuzzy regions, when hour, , PRF The pulse repetition frequency, For Doppler frequency, λ is the wavelength.
[0011] Furthermore, in step S63, the main region energy Energy in the fuzzy region ;in, For Doppler bandwidth, is the Doppler frequency, 2×M+1 is the number of ambiguous regions, and m refers to the corresponding ambiguous region number.
[0012] Furthermore, in step S7, AASR is calculated as follows: .
[0013] Compared with existing technologies, the azimuth ambiguity ratio simulation method for sliding beamforming mode based on antenna pattern data described in this invention has the following advantages: The azimuth ambiguity ratio simulation method for sliding spotting mode based on antenna pattern data described in this invention provides simulation results that more closely approximate physical reality for antenna pattern data with different scanning angles. This simulation method, based on actual antenna pattern data, is closer to engineering practice and provides a more reliable evaluation basis for system design and parameter optimization. It fills the gap in existing technologies for accurate simulation methods of sliding spotting mode AASR and provides a systematic solution. Attached Figure Description
[0014] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a schematic diagram of the geometric cross-section of the sliding beam-gathering mode according to an embodiment of the present invention; Figure 2 This is a schematic diagram of antenna radiation pattern data according to an embodiment of the present invention; Figure 3 This is a simulation diagram of AASR under different oversampling factors as described in the embodiments of the present invention; Figure 4This is a schematic diagram of the simulation method described in an embodiment of the present invention. Detailed Implementation
[0015] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.
[0016] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.
[0017] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.
[0018] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.
[0019] like Figures 1 to 4 As shown, the simulation method for azimuth ambiguity ratio in sliding beam-focusing mode based on antenna pattern data includes the following steps: S1. Calculate the platform position at any given time. ; S2. Calculate the azimuth beam pointing angle at any given time. ; S3. Calculate the oblique angle of the line connecting the platform and the target at any given moment. ; S4. Calculate the target's antenna azimuth angle. ; S5. Calculate the antenna azimuth angle corresponding to the m-th order ambiguity signal. ; S6. Calculate the main region energy of the antenna pattern corresponding to the antenna azimuth angle. and fuzzy region energy ; S7. Calculate the azimuth ambiguity ratio (AASR).
[0020] Compared with the traditional ideal sinc function model, this invention uses actual multi-scan angle pattern data, which can more realistically reflect the physical reality and accurately capture characteristics such as grating lobe and side lobe changes, providing a reliable evaluation basis for the design and parameter optimization of sliding spotlight SAR systems.
[0021] In a preferred embodiment of the present invention, in step S1, the platform position... for: ; in, Target location, unit: meters; The distance the beam covers on the ground, in meters; Platform speed, unit: meters per second; Wave foot velocity, unit: meters per second; The time for the beam to illuminate the point target, in seconds, where , For one synthesis aperture time; This represents the platform's location at time t=0, in meters. Platform location in step S1. The formula establishes the relationship between platform motion parameters and imaging geometry, forming the basis for the design of a sliding spotlight SAR system. By controlling the ratio of wave foot velocity to platform velocity, azimuth resolution can be flexibly adjusted. The platform position... This provides an accurate geometric basis for subsequent azimuth ambiguity ratio (AASR) calculations, ensuring consistency between simulation results and physical reality.
[0022] In a preferred embodiment of the present invention, in step S2, the azimuth beam pointing angle for: ; in, The slant distance between the platform and the underground virtual point It refers to the straight-line distance from the radar platform to the underground virtual point (or target point). In the sliding beamforming mode, the slant range is the basic parameter for calculating the azimuth beam pointing angle; different slant ranges correspond to different beam pointing angles, affecting the imaging geometry.
[0023] In a preferred embodiment of the present invention, in step S3, the azimuth beam pointing angle for: ; in, This represents the minimum slant range of the beam center. In the sliding beamforming mode, different slant ranges correspond to different beam pointing angles, directly affecting the accuracy of the imaging geometry. It provides an accurate geometric basis for calculating the azimuth ambiguity ratio (AASR). Compared to an ideal antenna pattern model, simulation using actual multi-scan angle pattern data can more realistically reflect the physical reality and provide a more reliable evaluation basis for system design and parameter optimization.
[0024] In a preferred embodiment of the present invention, in step S4, the antenna azimuth angle for: .
[0025] In a preferred embodiment of the present invention, in step S5, the antenna azimuth angle for: ; in, , 2×M+1 represents the number of fuzzy regions, where m refers to the corresponding fuzzy region number. hour, , PRF The pulse repetition frequency determines the system's time sampling rate and affects its ambiguity suppression capability. For Doppler frequency, Wavelength, antenna azimuth angle Compared to ideal antenna pattern models, using actual multi-scan angle pattern data can more realistically reflect the physical reality.
[0026] In a preferred embodiment of the present invention, the detailed calculation steps for the antenna pattern energy in step S6 are as follows: S61, based on any given moment Find the scanning angle as The antenna pattern data file was obtained, and the amplitude and phase data information were extracted from it; S62. Based on the antenna azimuth angle at any given time. The phase and amplitude information extracted in step S61 are interpolated to obtain... Corresponding amplitude ; S63. The main region energy is obtained by integral calculation. and fuzzy region energy ;in For Doppler bandwidth, is the Doppler frequency, 2×M+1 is the number of ambiguous regions, and m refers to the corresponding ambiguous region number.
[0027] Compared to traditional ideal sinc function or fixed pattern models, this method uses real data from multiple scanning angles, which can more accurately capture real characteristics such as changes in grating lobes and side lobes, providing a reliable evaluation basis for the design and parameter optimization of sliding spotlight SAR systems.
[0028] In a preferred embodiment of the present invention, in step S7, AASR is calculated as follows: .
[0029] The azimuth ambiguity ratio simulation method for sliding spotting mode provided by this invention can make the simulation results closer to the physical reality for antenna pattern data with different scanning angles. The simulation method based on actual antenna pattern data is closer to engineering practice and can provide a more reliable evaluation basis for system design and parameter optimization. It fills the gap in existing technologies for accurate simulation methods of sliding spotting mode AASR and provides a systematic solution.
[0030] Example 1 The input parameters are the sliding spotting mode parameters of a certain spaceborne SAR system, as shown in Table 1 below: Table 1 Simulation parameters for sliding beam convergence mode Center frequency 9.8GHz Azimuth antenna size 3.6m orbital altitude 522km resolution 1m Orientation to scene size 20km The input data consists of multiple sets of antenna pattern data (see...). Figure 2 The azimuth antenna pattern, with a scanning angle ranging from -1.5° to +1.5°, is shown, where the horizontal axis represents angle and the vertical axis represents gain. For ease of comparison, the horizontal axis of the data was shifted to ensure the main lobe is at 0 degrees. It can be seen that compared to the ideal sinc antenna pattern, the azimuth antenna pattern data contains a large number of grating lobes, and the side lobes gradually rise and widen as the scanning angle increases. This will affect AASR (Advanced Antenna Reflection and Surface Gain).
[0031] Figure 3 The figures show the azimuth ambiguity ratio (AASR) simulation curves under different oversampling factors. The blue curve represents the AASR variation curve using an ideal antenna pattern model, the yellow curve represents the AASR variation curve using fixed antenna pattern data at the maximum scan angle, and the red curve represents the AASR variation curve using pattern data from multiple scan angles. As can be seen from the figures, the presence of grating lobes in the antenna pattern data affects the AASR, making it unsuitable to simulate the AASR using an ideal antenna pattern function or fixed single data. Simulation using pattern data from various scan angles is necessary, and only through the design of a reasonable oversampling factor can the system design specifications be met.
[0032] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A method for simulating the azimuthal ambiguity ratio of a sliding beam mode based on antenna pattern data, characterized in that: Includes the following steps: S1. Calculate the platform position at any given time. ; S2. Calculate the azimuth beam pointing angle at any given time. ; S3. Calculate the oblique angle of the line connecting the platform and the target at any given moment. ; S4. Calculate the target's antenna azimuth angle. ; S5. Calculate the antenna azimuth angle corresponding to the m-th order ambiguity signal. ; S6. Calculate the main region energy of the antenna pattern corresponding to the antenna azimuth angle. and fuzzy region energy ; S7. Calculate the azimuth ambiguity ratio (AASR); In step S6, the calculation steps for the antenna pattern energy are as follows: S61, based on any given moment Find the azimuth beam pointing angle as The antenna pattern data file was obtained, and the amplitude and phase data information were extracted from it; S62. Based on the antenna azimuth angle at any given time. The phase and amplitude information extracted in step S61 are interpolated to obtain... Corresponding amplitude ; S63. The main region energy is obtained by integral calculation. and fuzzy region energy ; In step S63, the main region energy Energy in the fuzzy region ;in, For Doppler bandwidth, is the Doppler frequency, and 2×M+1 is the number of ambiguous regions.
2. The method for simulating the azimuth ambiguity ratio of a sliding spotting mode based on antenna pattern data according to claim 1, characterized in that: In step S1, the platform position for: ; in, Target location, unit: meters; The distance the beam covers on the ground, in meters; Platform speed, unit: meters per second; Wave foot velocity, unit: meters per second; The time for the beam to illuminate the point target, in seconds, where , For one synthesis aperture time; It represents the location of the platform at time t=0, in meters.
3. The method for simulating the azimuth ambiguity ratio of a sliding spotting mode based on antenna pattern data according to claim 2, characterized in that: In step S2, the azimuth beam pointing angle for: ; in, The slant distance between the platform and the underground virtual point.
4. The azimuth ambiguity ratio simulation method for sliding beamforming mode based on antenna pattern data according to claim 2, characterized in that: In step S3, oblique angle for: ; in, This is the minimum slant distance from the beam center.
5. The azimuth ambiguity ratio simulation method for sliding beamforming mode based on antenna pattern data according to claim 3 or 4, characterized in that: In step S4, the antenna azimuth angle for: .
6. The method for simulating the azimuth ambiguity ratio of a sliding spotting mode based on antenna pattern data according to claim 3, characterized in that: In step S5, the antenna azimuth angle for: ; in, , 2×M+1 represents the number of fuzzy regions, where m refers to the corresponding fuzzy region number. hour, , PRF The pulse repetition frequency, For Doppler frequency, λ is the wavelength.
7. The method for simulating the azimuth ambiguity ratio of a sliding spotting mode based on antenna pattern data according to claim 1, characterized in that: In step S7, AASR is calculated as follows: .