Waveform Optimization Algorithm for Synthetic Aperture Radar High Squint Imaging

Abstract

The present invention relates to the field of radar technology, and discloses a waveform optimization algorithm for synthetic aperture radar high squint imaging, including designing waveform parameters; using adaptive nonlinear frequency modulation waveform design to dynamically compensate for Doppler effect; combining wavelet transform and time-frequency adaptive window function to analyze echo signals; dynamically adjusting waveform parameters based on fuzzy logic particle swarm optimization method; real-time correction of waveform parameters to adapt to environmental changes; automatically adjusting waveform parameters to optimize imaging effects based on feedback of echo signal quality; and verifying the performance of optimized waveforms in target imaging quality, resolution and signal-to-noise ratio through simulation and actual measurement. The waveform optimization algorithm of the present invention can provide higher quality and more stable images in high squint angle SAR imaging, while enhancing the system's adaptability to dynamic environments and targets.

Description

Technical Field

[0001] The invention relates to the field of radar technology, and in particular to a waveform optimization algorithm for synthetic aperture radar high squint imaging. Background Art

[0002] With the continuous development of synthetic aperture radar (SAR) technology, the application of SAR in high squint imaging is becoming more and more widespread. Under high squint angles, SAR systems will encounter some special challenges when observing targets, especially in waveform design and signal processing. Specific problems include: frequency drift caused by the Doppler effect, reduced resolution, and signal distortion. In addition, environmental factors such as atmospheric refraction, air pressure, and temperature will also have a significant impact on the propagation characteristics of SAR signals, further exacerbating these problems.

[0003] Existing SAR waveform design and signal optimization methods mainly focus on optimization under fixed environmental conditions, but lack effective solutions for waveform optimization under complex and changing environments, different types of targets, and large squint angles. Therefore, a new waveform optimization algorithm is urgently needed to improve the resolution, stability, and adaptability of SAR in large squint imaging, while considering the impact of multiple environmental factors on the waveform. Summary of the invention

[0004] In view of this, the present invention proposes a synthetic aperture radar high squint imaging waveform optimization algorithm, aiming to solve the above problems.

[0005] The present invention proposes a synthetic aperture radar high squint imaging waveform optimization algorithm, including:

[0006] Designing waveform parameters, wherein the waveform parameters include wavelength, pulse repetition frequency, pulse width and bandwidth;

[0007] Adopting adaptive nonlinear FM waveform design, by controlling FM index and FM amplitude, the Doppler effect under large oblique viewing angle can be dynamically compensated.

[0008] The time-frequency characteristics of the echo signal are analyzed by combining wavelet transform with time-frequency adaptive window function;

[0009] Adopt particle swarm optimization method based on fuzzy logic to dynamically adjust waveform parameters;

[0010] Through dynamic atmospheric model and digital elevation model correction, waveform parameters are corrected in real time to adapt to environmental changes;

[0011] Adaptive gain control is used to automatically adjust waveform parameters based on real-time echo signal quality feedback to optimize imaging effects;

[0012] The optimization results are verified by high-resolution simulation and actual measurements.

[0013] Preferably, the relationship between the wavelength and the frequency is:

[0014] ;

[0015] Among them, λ is the wavelength, c is the speed of light, and f is the frequency.

[0016] Preferably, the frequency modulation formula of the adaptive nonlinear frequency modulation waveform is:

[0017] ;

[0018] Among them, f(t) is the change of frequency with time, f0 is the initial frequency, β1 is the frequency modulation parameter, v1 is the nonlinear index, γ1 is the dynamic frequency modulation coefficient, and ω1 is the fluctuation frequency.

[0019] Preferably, the selection formula of the adaptive window function is:

[0020] ;

[0021] Among them, w(t) is the time domain window function, α1 and δ1 are adaptive coefficients, and t0 is the center time of the window function.

[0022] Preferably, the fitness function of the fuzzy logic optimization is:

[0023] ;

[0024] Where F is the fitness function, ΔR is the range resolution error, Δθ is the azimuth resolution error, SNR is the signal-to-noise ratio, Noise is the noise, and α2, β2, and γ2 are adaptive coefficients.

[0025] Preferably, the dynamic atmospheric model is:

[0026] ;

[0027] Among them, α3 is the correction coefficient of air pressure to wave speed, ΔP is the change in air pressure, ΔT is the change in temperature, c is the speed of light, and β3 is the correction coefficient of temperature change to wave speed.

[0028] Preferably, according to the real-time echo signal quality feedback, the waveform parameters are automatically adjusted by adaptive gain control to optimize the imaging effect. The gain control formula is:

[0029] ;

[0030] Among them, G(t) is the gain at time t, G0 is the initial gain, S(t) is the echo signal strength, Smax is the maximum value of the echo signal, and δ2 is the gain adjustment coefficient.

[0031] Preferably, the digital elevation model is:

[0032] ;

[0033] Among them, β4 is the terrain correction coefficient and ΔH is the height difference.

[0034] Preferably, the waveform optimization algorithm also includes adjusting waveform parameters in real time according to feedback of the echo signal to adapt to different target types and environmental changes.

[0035] Preferably, adaptive gain control is used to automatically adjust waveform parameters based on real-time echo signal quality feedback. When optimizing the imaging effect, the optimization result is verified by high-resolution simulation and actual measurement. The verification criteria include PSNR (peak signal-to-noise ratio) and SSIM (structural similarity index) of the target imaging.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] 1. Improve imaging resolution: Through the adaptive nonlinear frequency modulation (ANLFM) waveform design, the Doppler effect under large oblique viewing angles can be compensated, thereby improving the resolution in the range and azimuth directions.

[0038] 2. Optimize signal-to-noise ratio (SNR): Using time-frequency domain analysis and dynamic gain control (AGC) technology, the gain can be adjusted in real time, the signal-to-noise ratio of the echo signal can be optimized, and the imaging quality can be improved.

[0039] 3. Enhance environmental adaptability: Through the dynamic atmosphere correction model and terrain correction model, the waveform parameters can be adjusted in real time according to environmental changes such as air pressure and temperature to enhance the system's environmental adaptability.

[0040] 4. Improve stability and robustness: Combine the fuzzy logic particle swarm optimization (FL-PSO) algorithm to perform multi-objective optimization of waveform parameters, so that the system can work stably in complex environments and different target types.

[0041] 5. Reduce computational complexity: The time-frequency analysis method using adaptive window function and wavelet transform effectively reduces the need for complex calculations and ensures the accuracy of signal processing.

[0042] In summary, these beneficial effects enable the waveform optimization algorithm of the present invention to provide higher quality and more stable images in large squint angle SAR imaging, while enhancing the system's adaptability to dynamic environments and targets. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0044] Figure 1 The present invention is a flow chart of the synthetic aperture radar high squint imaging waveform optimization algorithm. DETAILED DESCRIPTION

[0045] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0046] See also Figure 1 This embodiment provides a synthetic aperture radar high squint imaging waveform optimization algorithm, including:

[0047] Designing waveform parameters, wherein the waveform parameters include wavelength, pulse repetition frequency, pulse width and bandwidth;

[0048] Adopting adaptive nonlinear FM waveform design, by controlling FM index and FM amplitude, the Doppler effect under large oblique viewing angle can be dynamically compensated.

[0049] The time-frequency characteristics of the echo signal are analyzed by combining wavelet transform with time-frequency adaptive window function;

[0050] Adopt particle swarm optimization method based on fuzzy logic to dynamically adjust waveform parameters;

[0051] Through dynamic atmospheric model and digital elevation model correction, waveform parameters are corrected in real time to adapt to environmental changes;

[0052] Adaptive gain control is used to automatically adjust waveform parameters based on real-time echo signal quality feedback to optimize imaging effects;

[0053] The optimization results are verified by high-resolution simulation and actual measurements.

[0054] It can be seen that this embodiment provides a waveform optimization algorithm for synthetic aperture radar (SAR) in a high squint imaging mode. The algorithm aims to improve the imaging quality by accurately controlling the waveform parameters, especially under high squint conditions, where the Doppler effect is significant and has a greater impact on the imaging quality.

[0055] First, the design of waveform parameters is the basis of the algorithm, including wavelength, pulse repetition frequency (PRF), pulse width and bandwidth. These parameters together determine the characteristics of the radar signal, such as resolution and coverage.

[0056] Next, an adaptive nonlinear frequency modulation waveform design is used to dynamically adjust the frequency modulation index and frequency modulation amplitude to compensate for the Doppler effect produced at large oblique viewing angles. This adaptive frequency modulation technology can effectively cope with the frequency shift caused by changes in viewing angle, thereby maintaining imaging quality.

[0057] In order to further analyze the time-frequency characteristics of the echo signal, the algorithm combines wavelet transform with time-frequency adaptive window function. Wavelet transform can provide time-frequency information of the signal at different scales, while the adaptive window function dynamically adjusts according to the signal characteristics to obtain the best time-frequency resolution.

[0058] In addition, the algorithm uses a particle swarm optimization (PSO) method based on fuzzy logic to dynamically adjust the waveform parameters. Fuzzy logic provides an effective means of dealing with uncertainty and ambiguity, while particle swarm optimization is a group intelligence optimization algorithm that simulates the foraging behavior of bird flocks to find the optimal solution.

[0059] In order to adapt to environmental changes, the algorithm also combines dynamic atmospheric models and digital elevation model (DEM) corrections. These models can correct waveform parameters in real time to adapt to the impact of atmospheric conditions and terrain changes on radar wave propagation.

[0060] Finally, the algorithm uses adaptive gain control to automatically adjust waveform parameters based on real-time echo signal quality feedback to optimize imaging. This feedback mechanism ensures that the imaging process can self-adjust according to actual conditions to achieve the best imaging effect.

[0061] Through high-resolution simulation and actual measurement verification, the optimized waveform performs well in terms of target imaging quality, resolution and signal-to-noise ratio. This shows that the waveform optimization algorithm of this embodiment can effectively improve the performance of synthetic aperture radar in high squint imaging mode.

[0062] It is understandable that the waveform optimization algorithm of this embodiment is not only applicable to a specific SAR system, but also has a certain degree of versatility and can adapt to different types of SAR platforms and different imaging requirements. Through the implementation of the algorithm, high-precision imaging of targets in complex environments can be achieved, and high imaging quality and resolution can be maintained regardless of complex terrain such as cities, forests or mountains.

[0063] In addition, the waveform optimization algorithm of this embodiment also takes into account real-time processing capabilities to ensure the feasibility of the algorithm in practical applications. By optimizing the computational efficiency of the algorithm, it is possible to quickly respond to environmental changes and target dynamics under limited computing resources, thereby providing continuous and stable imaging services.

[0064] In practical applications, the waveform optimization algorithm of this embodiment can be integrated into the real-time processing unit of the SAR system and seamlessly connected with the existing SAR data processing flow. This not only improves the automation of the system, but also reduces the burden on operators, making SAR imaging technology more intelligent and user-friendly.

[0065] In summary, the waveform optimization algorithm provided in this embodiment provides a new solution for the application of synthetic aperture radar in high squint imaging mode, which has important practical value and broad application prospects. Through continuous optimization and improvement, the algorithm is expected to play a key role in future remote sensing detection and earth observation missions.

[0066] In some embodiments of the present application, the relationship between the wavelength and the frequency is:

[0067] ;

[0068] Among them, λ is the wavelength, c is the speed of light, and f is the frequency.

[0069] It can be seen that there is a specific mathematical relationship between the wavelength λ and the frequency f of the present embodiment, which can be described by the above formula, where c represents the speed of light, which is a constant and its value is approximately 299,792,458 meters per second. This relationship reveals the direct proportional relationship between wavelength and frequency, that is, the longer the wavelength, the lower the frequency; conversely, the shorter the wavelength, the higher the frequency.

[0070] It is understandable that the relationship between the wavelength and frequency of the present embodiment is a reflection of the basic properties of electromagnetic waves, and this relationship is widely used in various waveform designs and signal processing in electromagnetic wave theory. In a synthetic aperture radar (SAR) system, the selection of wavelength and frequency has a direct impact on the imaging quality. By precisely controlling the wavelength and frequency, the propagation characteristics of the radar wave and the processing of the echo signal can be optimized, thereby improving the imaging resolution and signal-to-noise ratio.

[0071] In SAR systems, the choice of wavelength is usually related to the operating frequency of the radar, and the choice of frequency is related to the penetration capability and resolution requirements of the radar. For example, lower frequencies (longer wavelengths) are good at penetrating clouds and vegetation, and are suitable for applications such as Ground Penetrating Radar (GPR), while higher frequencies (shorter wavelengths) can provide higher spatial resolution and are suitable for urban remote sensing and high-resolution imaging.

[0072] In some embodiments of the present application, the frequency modulation formula of the adaptive nonlinear frequency modulation waveform is:

[0073] ;

[0074] Among them, f(t) is the change of frequency with time, f0 is the initial frequency, β1 is the frequency modulation parameter, v1 is the nonlinear index, γ1 is the dynamic frequency modulation coefficient, and ω1 is the fluctuation frequency.

[0075] It can be seen that the frequency modulation formula of the adaptive nonlinear frequency modulation waveform of this embodiment is a complex modulation method, which allows the frequency to change over time to adapt to different imaging conditions and target characteristics. By adjusting the initial frequency f0, frequency modulation parameter β1, nonlinear index v1, dynamic frequency modulation coefficient γ1 and fluctuation frequency ω1, fine control of the waveform can be achieved to compensate for the Doppler effect and other imaging interference factors under large oblique viewing angles.

[0076] The frequency modulation parameter β1 determines the rate at which the frequency changes over time, while the dynamic frequency modulation coefficient γ1 allows the waveform to be dynamically adjusted according to real-time feedback during the imaging process. The introduction of the nonlinear index v1 and the fluctuation frequency ω1 makes the frequency modulation have the characteristics of nonlinear and periodic changes, which helps to further optimize the imaging quality, especially in complex and dynamically changing environments.

[0077] This adaptive nonlinear frequency modulation waveform design combines the advantages of the traditional linear frequency modulation (LFM) waveform and adds additional flexibility and adaptability through nonlinear modulation. It can better handle the Doppler shift problem in the large squint imaging mode, thereby improving imaging resolution and target detection capabilities.

[0078] In practical applications, this waveform design needs to be implemented through precise algorithms and hardware support. For example, the digital signal processor (DSP) or field programmable gate array (FPGA) in the SAR system can be used to generate and adjust this complex waveform in real time. In this way, the SAR system can dynamically optimize its waveform parameters according to different imaging requirements and environmental conditions to achieve the best imaging effect.

[0079] It is understandable that the adaptive nonlinear frequency modulation waveform design in this embodiment is not limited to the synthetic aperture radar (SAR) system, but can also be applied to other fields that require high-resolution imaging, such as medical ultrasound imaging, geological exploration, and security detection. In these applications, the adaptive adjustment capability of the waveform can also significantly improve the imaging quality, especially when facing different media and complex structures.

[0080] In addition, the waveform design of this embodiment also takes into account the feasibility of hardware implementation. When designing the waveform, it is necessary to ensure that the proposed frequency modulation formula can be effectively implemented on the existing hardware platform. This includes comprehensive consideration of factors such as the sampling rate of the digital to analog converter (DAC), the processing power of the signal processor, and the computational complexity of the waveform generation algorithm.

[0081] In some embodiments of the present application, the selection formula of the adaptive window function is:

[0082] ;

[0083] Among them, w(t) is the time domain window function, α1 and δ1 are adaptive coefficients, and t0 is the center time of the window function.

[0084] It can be seen that the adaptive window function selection formula of this embodiment provides a flexible way to adjust the shape and width of the time domain window function to adapt to different signal processing requirements. By adjusting the adaptive coefficients α1 and δ1, fine control of the window function shape can be achieved, thereby optimizing the time domain resolution and frequency selectivity of the signal.

[0085] The adaptive coefficient α1 affects the width of the window function. A larger α1 value will make the window function narrower, thereby improving the time domain resolution, but may reduce the frequency selectivity. On the contrary, a smaller α1 value will make the window function wider, improve the frequency selectivity, but reduce the time domain resolution. The adaptive coefficient δ1 is used to adjust the shape of the window function to adapt to specific signal characteristics or suppress specific interference.

[0086] The setting of the window function center time t0 allows the window function to be moved on the time axis to more accurately process the signal within a specific time range. This flexibility is especially important for processing signals with time-varying characteristics, such as imaging or monitoring in dynamically changing environments.

[0087] Combined with the selection of adaptive nonlinear frequency modulation waveform and adaptive window function, comprehensive optimization processing of signals can be achieved. This optimization is not limited to improving imaging quality, but also includes improving the sensitivity and accuracy of signal detection, and enhancing the system's adaptability to complex environments.

[0088] In practical applications, the selection and adjustment of adaptive window functions also require precise algorithm support and hardware implementation. Hardware platforms such as digital signal processors (DSPs) or field programmable gate arrays (FPGAs) can be used to calculate and apply window functions in real time to ensure the real-time and accuracy of signal processing.

[0089] In summary, the adaptive nonlinear frequency modulation waveform and adaptive window function selection method proposed in this embodiment provide new possibilities for signal processing and imaging technology, especially in application scenarios requiring high resolution and high sensitivity. Through this comprehensive signal processing strategy, the performance of the imaging system can be significantly improved, making it more adaptable to complex and changeable practical application requirements.

[0090] It is understandable that the adaptive window function selection method in this embodiment is not limited to theoretical discussion, but its effectiveness and practicality have been verified by experiments. The experimental results show that by properly selecting and adjusting the adaptive coefficients α1, δ1 and the window function center time t0, the performance of signal processing can be significantly improved. For example, in the field of medical imaging, the method in this embodiment can obtain clearer images, thereby helping doctors to diagnose diseases more accurately. In radar and sonar systems, this method can improve the accuracy and resolution of target detection and enhance the anti-interference ability of the system.

[0091] In addition, the present embodiment also takes into account the computational complexity and resource consumption in practical applications. By optimizing the algorithm and hardware design, the feasibility of the adaptive window function selection method in practical applications is ensured. For example, by using an efficient algorithm and an optimized hardware architecture, power consumption and cost can be reduced while ensuring processing speed, so that the technology can be widely used in various portable and mobile devices.

[0092] In summary, the adaptive window function selection method proposed in this embodiment has shown its powerful advantages in theory and practice. It can not only provide flexible signal processing means to meet the needs of different application scenarios, but also ensure its efficiency and reliability in practical applications through precise algorithms and hardware implementations. With the continuous advancement and optimization of technology, it is expected that this method will play a more important role in future signal processing and imaging technologies.

[0093] In some embodiments of the present application, the fitness function of the fuzzy logic optimization is:

[0094] ;

[0095] Where F is the fitness function, ΔR is the range resolution error, Δθ is the azimuth resolution error, SNR is the signal-to-noise ratio, Noise is the noise, and α2, β2, and γ2 are adaptive coefficients.

[0096] It can be seen that the fitness function selection formula of the fuzzy logic optimization of this embodiment provides an effective method to evaluate and optimize the performance of the signal processing system. By considering the range resolution error ΔR, the azimuth resolution error Δθ and the signal-to-noise ratio SNR, the fitness function can comprehensively reflect the spatial resolution and signal quality of the system. The introduction of noise Noise further ensures that the fitness function can evaluate the system performance in an actual noise environment.

[0097] The adaptive coefficients α2, β2, and γ2 allow weighting of each item in the fitness function to suit different optimization goals and application scenarios. For example, if improving range resolution is a priority, the value of α2 can be increased to increase the influence of ΔR in the fitness function. Correspondingly, if improving azimuth resolution is more important, the value of β2 can be adjusted. γ2 is used to adjust the weight of the signal-to-noise ratio SNR in the fitness function to ensure that the system has good signal quality while maintaining high resolution.

[0098] The introduction of fuzzy logic provides flexibility and robustness for the optimization of fitness functions. Through fuzzification, precise numerical values ​​can be converted into fuzzy sets, so that uncertainty and ambiguity can be taken into account in the evaluation and decision-making process. This processing method is particularly suitable for signal processing systems, because signals in real environments are often affected by multiple factors and have certain uncertainties and complexities.

[0099] In practical applications, the fitness function of fuzzy logic optimization can be used to guide the parameter adjustment and optimization of signal processing algorithms. For example, in radar systems, the fitness function can be used to guide the parameter setting of beamforming algorithms to achieve the best detection performance. In communication systems, the fitness function can be used to optimize the modulation and demodulation scheme of signals to adapt to different channel conditions and noise levels.

[0100] In summary, the fuzzy logic optimized fitness function proposed in this embodiment provides a new tool for performance evaluation and optimization of signal processing systems. By flexibly adjusting the adaptive coefficient and using fuzzy logic to process uncertain information, this method can effectively improve the overall performance of the system and meet complex and changeable application requirements. Experimental and practical application results show that the fitness function is not only reasonable in theory, but also shows good performance and practicality in practical applications. With the continuous development of signal processing technology, it is expected that this method will play a more critical role in future system optimization and performance improvement.

[0101] It can be understood that the fuzzy logic optimized fitness function proposed in this embodiment is not only applicable to radar and communication systems, but can also be extended to other signal processing fields, such as sonar systems, medical imaging, and wireless sensor networks. In these applications, the fitness function can also be adjusted according to specific application requirements and environmental conditions to achieve optimal performance.

[0102] For example, in sonar systems, the fitness function can be used to optimize the parameters of signal transmission and reception to improve the detection capability and imaging quality of underwater targets. In the field of medical imaging, the function can be used to improve the image reconstruction algorithm, thereby reducing noise while improving the resolution and contrast of the image. In wireless sensor networks, the fitness function can guide the signal processing strategy of the node to improve the coverage of the network and the reliability of data transmission.

[0103] In addition, with the development of artificial intelligence and machine learning technologies, the fitness function of fuzzy logic optimization can also be combined with these technologies to further enhance the intelligence level of signal processing systems. Through machine learning algorithms, the system can automatically learn and adjust the parameters in the fitness function to adapt to changing environments and task requirements.

[0104] In summary, the fuzzy logic optimized fitness function proposed in this embodiment has broad application prospects and important practical significance. It can not only improve the performance of existing signal processing systems, but also provide new ideas and methods for the development of future technologies. With the continuous advancement of related technologies, the fitness function is expected to be applied in more fields and make greater contributions to the innovation and optimization of signal processing technology.

[0105] In some embodiments of the present application, the dynamic atmospheric model is:

[0106] ;

[0107] Among them, α3 is the correction coefficient of air pressure to wave speed, ΔP is the change in air pressure, ΔT is the change in temperature, c is the speed of light, and β3 is the correction coefficient of temperature change to wave speed.

[0108] It can be seen that the atmospheric correction formula of this embodiment provides an effective method to correct the wave velocity error caused by changes in atmospheric conditions. In practical applications, changes in atmospheric conditions such as air pressure and temperature will affect the wave velocity, thereby affecting the propagation time and positioning accuracy of the signal. By considering the pressure change ΔP and the temperature change ΔT, the formula can correct the wave velocity in real time, ensuring that the signal processing system can measure distance and position more accurately.

[0109] α3 and β3 act as correction factors that allow for quantitative adjustments to be made for the effects of changes in air pressure and temperature on wave velocity. Under different environmental conditions, these factors can be adjusted to reflect actual atmospheric propagation characteristics. For example, at high altitudes, where air pressure is lower, the value of α3 may need to be adjusted to reflect the effect of this change on wave velocity. Similarly, the value of β3 may be adjusted based on the effect of temperature changes on wave velocity to ensure accurate wave velocity corrections are obtained under different temperature conditions.

[0110] The atmospheric correction formula can be integrated into various signal processing systems, such as radar, sonar, and wireless communication systems, to improve their performance under complex atmospheric conditions. For example, in a weather radar system, the formula can be used to correct the propagation speed of radar waves in the atmosphere, thereby improving the accuracy of weather monitoring and forecasting. In a wireless communication system, the formula can be used to correct the signal propagation time to reduce communication delays and errors caused by changes in atmospheric conditions.

[0111] In summary, the atmospheric correction formula proposed in this embodiment provides an effective tool for improving the performance of the signal processing system under complex atmospheric conditions. By accurately correcting the wave velocity error, this method can improve the measurement accuracy and reliability of the system and meet the application requirements under various environmental conditions. With the continuous development of signal processing technology, it is expected that this method will play a more critical role in future system optimization and performance improvement.

[0112] It is understandable that the atmospheric correction formula proposed in this embodiment is not only applicable to the above-mentioned systems, but can also be extended to other fields that need to consider the influence of atmospheric conditions, such as satellite communications, remote sensing detection, and navigation systems. In these applications, the atmospheric correction formula can also be adjusted according to specific application requirements and environmental conditions to achieve optimal performance.

[0113] For example, in the field of satellite communications, this formula can be used to correct the propagation delay of signals in the atmosphere, thereby improving the synchronization of communications and the accuracy of data transmission. In remote sensing detection, the atmospheric correction formula can be used to improve the accuracy of remote sensing data, especially when detecting and analyzing the atmosphere. In navigation systems, this formula can be used to improve positioning accuracy, especially in the Global Positioning System (GPS), where atmospheric delay is an important factor affecting positioning accuracy.

[0114] In addition, with the development of space technology, the atmospheric correction formula can also be combined with space exploration missions. For example, in the exploration missions of Mars or other planets, the signal propagation speed can be corrected taking into account the characteristics of the atmospheres of different planets to ensure the accuracy of communication and detection data.

[0115] In summary, the atmospheric correction formula proposed in this embodiment has broad application prospects and important practical significance. It can not only improve the performance of existing signal processing systems, but also provide new ideas and methods for the development of future technologies. With the continuous advancement of related technologies, the correction formula is expected to be applied in more fields and make greater contributions to the innovation and optimization of signal processing technology.

[0116] It is understandable that the determination of the α3 and β3 coefficients proposed in this embodiment needs to be based on a large amount of experimental data and theoretical calculations. In actual operation, these coefficients can be optimized by establishing a relationship model between atmospheric parameters and wave velocity changes, using historical meteorological data and experimental measurement results. For example, a machine learning algorithm can be used to automatically adjust the values ​​of α3 and β3 by analyzing wave velocity change data under different air pressure and temperature conditions to achieve the best correction effect.

[0117] In practical applications, the implementation of the atmospheric correction formula also needs to rely on advanced sensor technology to monitor changes in atmospheric conditions in real time. For example, air pressure sensors and temperature sensors can be used to obtain real-time data, and these data can be input into the atmospheric correction formula to achieve dynamic wave speed correction.

[0118] In summary, the atmospheric correction formula proposed in this embodiment not only provides an effective wave velocity correction method for the signal processing system, but also, through continuous optimization and expansion, is expected to play an important role in more fields and more complex environments, and provide strong support for the development and application of signal processing technology.

[0119] In some embodiments of the present application, adaptive gain control is used to automatically adjust waveform parameters according to real-time echo signal quality feedback to optimize imaging effects. The gain control formula is:

[0120] ;

[0121] Among them, G(t) is the gain at time t, G0 is the initial gain, S(t) is the echo signal strength, Smax is the maximum value of the echo signal, and δ2 is the gain adjustment coefficient.

[0122] It can be seen that the gain control formula of this embodiment provides a method for dynamically adjusting the signal gain to adapt to changes in signal strength. In a signal processing system, the strength of the echo signal may fluctuate due to a variety of factors, such as the distance of the target object, reflectivity, and environmental noise. By monitoring the echo signal strength S(t) in real time and comparing it with the maximum value Smax, the formula can automatically adjust the gain G(t) to maintain the stability and clarity of the signal output.

[0123] G0, as the initial gain, provides a starting point for the system, while the gain adjustment coefficient δ2 determines the sensitivity of the gain adjustment. In practical applications, the value of δ2 can be adjusted according to the specific needs of the system to achieve the best signal processing effect. For example, in a noisy environment, a larger δ2 value may be required to quickly respond to changes in signal strength, while in an environment where the signal is relatively stable, a smaller δ2 value may be required to avoid over-adjustment.

[0124] The gain control formula can be integrated into various signal processing systems, such as radar, sonar, and wireless communication systems, to improve their performance in complex environments. For example, in radar systems, the formula can be used to dynamically adjust the gain of the received signal to ensure a clear echo signal even when the distance to the target object changes or the reflection conditions change. In wireless communication systems, the formula can be used to adjust the gain of the received signal to adapt to fluctuations in signal strength, thereby reducing data transmission errors and improving communication quality.

[0125] In summary, the gain control formula proposed in this embodiment provides an effective tool for improving the performance of the signal processing system in a complex environment. By dynamically adjusting the gain, the method can improve the signal processing capability and reliability of the system and meet the application requirements under various environmental conditions. With the continuous development of signal processing technology, it is expected that this method will play a more critical role in future system optimization and performance improvement.

[0126] It is understandable that the gain control formula proposed in this embodiment is not only applicable to the above-mentioned system, but can also be extended to other fields that need to consider signal strength changes, such as audio processing, video monitoring, and medical imaging. In these applications, the gain control formula can also be adjusted according to specific application requirements and environmental conditions to achieve optimal performance.

[0127] For example, in audio processing, the formula can be used to automatically adjust the gain of a microphone or speaker to adapt to changes in sound intensity in different environments, thereby providing clearer audio output. In video surveillance, the gain control formula can be used to adjust the signal gain of a camera to adapt to changes in light conditions and ensure the quality of video images. In medical imaging, the formula can be used to adjust the signal gain of an imaging device to adapt to the imaging characteristics of different tissues or organs and improve the clarity and contrast of the image.

[0128] In addition, with the development of artificial intelligence technology, the gain control formula can also be combined with intelligent algorithms. For example, in the automatic adjustment system, historical data can be analyzed through machine learning algorithms to automatically optimize the gain control parameters to achieve the best signal processing effect.

[0129] In summary, the gain control formula proposed in this embodiment has broad application prospects and important practical significance. It can not only improve the performance of existing signal processing systems, but also provide new ideas and methods for the development of future technologies. With the continuous advancement of related technologies, the gain control formula is expected to be applied in more fields and make greater contributions to the innovation and optimization of signal processing technology.

[0130] In some embodiments of the present application, the digital elevation model is:

[0131] ;

[0132] Among them, β4 is the terrain correction coefficient and ΔH is the height difference.

[0133] It can be seen that the terrain correction formula of this embodiment provides a method for considering the influence of terrain factors on signal propagation. In a signal processing system, terrain has a significant influence on the propagation path and strength of a signal. For example, in a radar or wireless communication system, the propagation of a signal may be blocked or reflected by mountains, buildings or other terrain features, thereby affecting the quality and coverage of the signal.

[0134] By using the terrain correction formula, the system can adjust the signal processing parameters according to the terrain elevation data to compensate for the signal attenuation or enhancement caused by the terrain. The introduction of the terrain correction coefficient β4 allows the system to correct the signal propagation characteristics under different terrain conditions. In practical applications, the value of β4 can be adjusted according to the complexity of the terrain and the specific needs of signal propagation to ensure the accuracy of signal processing.

[0135] For example, in radar systems, terrain correction can be used to optimize target detection and tracking algorithms to ensure accurate identification and positioning of targets even in complex terrain conditions. In wireless communication systems, terrain correction can be used to adjust signal transmission power and receiving sensitivity to adapt to the impact of terrain changes on signal coverage, thereby improving communication quality and network performance.

[0136] Terrain correction formulas can be integrated into various signal processing systems to improve their performance in complex terrain environments. With the development of geographic information systems (GIS) and digital elevation models (DEM) technology, the acquisition and processing of terrain data have become more accurate and efficient, which provides solid data support for terrain correction.

[0137] In summary, the terrain correction formula proposed in this embodiment provides an effective tool for improving the performance of the signal processing system in a complex terrain environment. By considering the influence of terrain factors on signal propagation, this method can improve the signal processing capability and reliability of the system and meet the application requirements under various terrain conditions. With the continuous development of signal processing and geographic information system technology, it is expected that this method will play a more critical role in future system optimization and performance improvement.

[0138] It is understandable that the terrain correction formula proposed in this embodiment is not only applicable to the above-mentioned systems, but can also be extended to other fields that need to consider the influence of terrain, such as navigation, remote sensing, and environmental monitoring. In these applications, the terrain correction formula can also be adjusted according to specific application requirements and environmental conditions to achieve optimal performance.

[0139] For example, in navigation systems, terrain correction can be used to improve positioning accuracy, especially in complex terrains such as mountainous areas or urban canyons. In remote sensing applications, terrain correction can be used to correct remote sensing images to reduce the impact of terrain undulations on image quality. In environmental monitoring, terrain correction can be used to optimize the layout of sensor networks to ensure the accuracy and reliability of monitoring data.

[0140] In addition, with the development of artificial intelligence technology, terrain correction formulas can also be combined with intelligent algorithms. For example, in the automatic adjustment system, terrain data can be analyzed through machine learning algorithms to automatically optimize terrain correction parameters to achieve the best signal processing effect.

[0141] In summary, the terrain correction formula proposed in this embodiment has broad application prospects and important practical significance. It can not only improve the performance of existing signal processing systems, but also provide new ideas and methods for the development of future technologies. With the continuous advancement of related technologies, the terrain correction formula is expected to be applied in more fields and make greater contributions to the innovation and optimization of signal processing technology.

[0142] In some embodiments of the present application, the waveform optimization algorithm further includes adjusting waveform parameters in real time according to feedback of the echo signal to adapt to different target types and environmental changes.

[0143] It can be seen that the waveform optimization algorithm of this embodiment provides a dynamic adaptability, which enables the signal processing system to adjust itself according to the actual feedback information, thereby maintaining the best performance in various complex and changing environments. By adjusting the waveform parameters in real time, the system can respond quickly to the characteristics of different targets and changes in environmental conditions, further improving the accuracy of target detection and the efficiency of signal processing.

[0144] For example, in a military radar system, target types may include various aircraft, ships, or ground vehicles, each with its own unique echo characteristics. By analyzing the echo signal in real time, the waveform optimization algorithm can adjust the radar waveform's pulse width, frequency, phase and other parameters to improve the detection capability of specific targets. In the civilian field, such as wireless communication systems, environmental factors such as weather changes and building obstructions may affect signal quality. The waveform optimization algorithm can adjust the transmission waveform in real time according to these changes to ensure the stability and reliability of communication.

[0145] In addition, the real-time adjustment capability of the waveform optimization algorithm also means that the system can automatically adapt to long-term environmental changes, such as changes in propagation conditions caused by seasonal changes, or the increase in building density during urbanization. Through continuous self-optimization, the signal processing system can maintain efficiency and adaptability in the long term, providing users with stable and reliable services.

[0146] In summary, the waveform optimization algorithm proposed in this embodiment can not only verify the optimization effect through high-resolution simulation and actual measurement, but also adjust the waveform parameters in real time according to the feedback of the echo signal, so as to maintain the best performance in various complex and changing environments. This dynamic adaptability is an indispensable part of the signal processing system in modern applications and helps to improve the overall performance of the system.

[0147] In some embodiments of the present application, adaptive gain control is used to automatically adjust waveform parameters based on real-time echo signal quality feedback to optimize imaging effects. The optimization results are verified through high-resolution simulation and actual measurement, and the verification criteria include PSNR and SSIM of target imaging.

[0148] It can be seen that the optimization result verification method of this embodiment provides a reliable means to evaluate the performance improvement effect of the signal processing system. The peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) of target imaging are two important indicators for measuring image quality. PSNR reflects the intensity change of the image signal, while SSIM measures the degree of preservation of the structural information of the image.

[0149] In practical applications, high-resolution simulation can simulate the performance of the signal processing system under ideal conditions, while actual measurement can reflect the working conditions of the system in a real environment. Comparing the simulation results with the actual measurement results can verify whether the optimization measures are effective and whether the optimization results meet expectations.

[0150] The higher the PSNR value, the better the signal quality of the image and the lower the noise level; while the closer the SSIM value is to 1, the better the structural information of the image is preserved and the closer the visual effect is to the original image. Therefore, by comparing the PSNR and SSIM values ​​before and after optimization, the performance improvement of the signal processing system can be intuitively evaluated.

[0151] For example, in radar systems, by optimizing signal processing algorithms, the accuracy of target detection can be improved, false positives and false negatives can be reduced, and the PSNR and SSIM values ​​can be improved. In wireless communication systems, optimizing transmit power and receive sensitivity can improve signal coverage and quality, which is also reflected in the improvement of PSNR and SSIM values.

[0152] In summary, the optimization result verification method proposed in this embodiment provides a comprehensive and accurate means for the performance evaluation of the signal processing system. By comparing the PSNR and SSIM values ​​before and after optimization, the performance improvement effect of the signal processing system can be objectively evaluated to ensure that the optimization measures achieve the expected goals. With the continuous advancement of signal processing technology, this verification method will help promote the continuous optimization and innovation of related technologies.

[0153] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0154] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0155] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0156] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0157] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A synthetic aperture radar high squint imaging waveform optimization algorithm, characterized in that: include: Designing waveform parameters, wherein the waveform parameters include wavelength, pulse repetition frequency, pulse width and bandwidth; Adopting adaptive nonlinear FM waveform design, by controlling FM index and FM amplitude, the Doppler effect under large oblique viewing angle can be dynamically compensated. The time-frequency characteristics of the echo signal are analyzed by combining wavelet transform with time-frequency adaptive window function; Adopt particle swarm optimization method based on fuzzy logic to dynamically adjust waveform parameters; Through dynamic atmospheric model and digital elevation model correction, waveform parameters are corrected in real time to adapt to environmental changes; Adaptive gain control is used to automatically adjust waveform parameters based on real-time echo signal quality feedback to optimize imaging effects; The optimization results are verified by high-resolution simulation and actual measurements.

2. The synthetic aperture radar high squint imaging waveform optimization algorithm according to claim 1, characterized in that: The relationship between wavelength and frequency is: ； Among them, λ is the wavelength, c is the speed of light, and f is the frequency.

3. The synthetic aperture radar high squint imaging waveform optimization algorithm according to claim 1, characterized in that: The frequency modulation formula of the adaptive nonlinear frequency modulation waveform is: ； Among them, f(t) is the change of frequency with time, f0 is the initial frequency, β1 is the frequency modulation parameter, v1 is the nonlinear index, γ1 is the dynamic frequency modulation coefficient, and ω1 is the fluctuation frequency.

4. The synthetic aperture radar high squint imaging waveform optimization algorithm according to claim 1, characterized in that: The selection formula of the adaptive window function is: ； Among them, w(t) is the time domain window function, α1 and δ1 are adaptive coefficients, and t0 is the center time of the window function.

5. The synthetic aperture radar high squint imaging waveform optimization algorithm according to claim 1, characterized in that: The fitness function of the fuzzy logic optimization is: ； Where F is the fitness function, ΔR is the range resolution error, Δθ is the azimuth resolution error, SNR is the signal-to-noise ratio, Noise is the noise, and α2, β2, and γ2 are adaptive coefficients.

6. The synthetic aperture radar high squint imaging waveform optimization algorithm according to claim 1, characterized in that: The dynamic atmospheric model is: ； Among them, α3 is the correction coefficient of air pressure to wave speed, ΔP is the change in air pressure, ΔT is the change in temperature, c is the speed of light, and β3 is the correction coefficient of temperature change to wave speed.

7. The synthetic aperture radar high squint imaging waveform optimization algorithm according to claim 1, characterized in that: According to the real-time feedback of echo signal quality, adaptive gain control is used to automatically adjust the waveform parameters to optimize the imaging effect. The gain control formula is: ； Among them, G(t) is the gain at time t, G0 is the initial gain, S(t) is the echo signal strength, Smax is the maximum value of the echo signal, and δ2 is the gain adjustment coefficient.

8. The synthetic aperture radar high squint imaging waveform optimization algorithm according to claim 1, characterized in that: The digital elevation model is: ； Among them, β4 is the terrain correction coefficient and ΔH is the height difference.

9. The synthetic aperture radar high squint imaging waveform optimization algorithm according to claim 1, characterized in that: The waveform optimization algorithm also includes adjusting waveform parameters in real time according to the feedback of the echo signal to adapt to different target types and environmental changes.

10. The synthetic aperture radar high squint imaging waveform optimization algorithm according to claim 1, characterized in that: Based on real-time echo signal quality feedback, adaptive gain control is used to automatically adjust waveform parameters to optimize imaging effects. The optimization results are verified through high-resolution simulation and actual measurement. The verification criteria include the peak signal-to-noise ratio and structural similarity index of target imaging.

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