Phase-Compensated Adaptive Filtering Method for UAV SAR Echo Enhancement.
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| Title: | Phase-Compensated Adaptive Filtering Method for UAV SAR Echo Enhancement. |
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| Authors: | Wang, Lele1 (AUTHOR), Chen, Leping1 (AUTHOR) lepingchen@nudt.edu.cn, An, Daoxiang1 (AUTHOR) |
| Source: | Remote Sensing. Mar2026, Vol. 18 Issue 6, p862. 24p. |
| Subjects: | Synthetic aperture radar, Radar signal processing, Phase distortion (Electronics), Adaptive filters, Radar antennas, Data reduction, Electric filters |
| Abstract: | Highlights: What are the main findings? This paper proposes a parameter-adjusted Chebyshev filtering algorithm for UAV SAR echo signal enhancement based on phase compensation. This algorithm fully utilizes the high repetition rate characteristic of UAV SAR systems, improving the pulse SNR while reducing the amount of data. The SNR gain during SAR processing and its relationship with SNR and PRF were analyzed. What are the implications of the main findings? This method effectively overcomes the problem that traditional SAR echo azimuth processing cannot compensate for phase changes between adjacent pulses, and makes full use of the energy of each pulse, thereby reducing the SNR loss caused by filtering and downsampling. This method can significantly reduce the amount of echo data, decrease memory usage, and improve the efficiency of SAR echo signal processing. Unmanned aerial vehicle Synthetic Aperture Radar (UAV SAR) is inevitably affected by hardware performance and complex electromagnetic environments, resulting in noise in the radar echo signal. This causes image blurring and loss of detail, severely limiting the detection performance and imaging quality of UAV SAR. High-repetition-rate UAV SAR can achieve high signal-to-noise ratio (SNR), but the SAR data volume grows exponentially, posing a challenge for large-scale data processing. Furthermore, in the case of high repetition rate, downsampling methods are needed to reduce the amount of raw data, which leads to a decrease in the echo SNR, thus significantly affecting SAR image details. Existing SAR signal processing methods typically involve a series of processing steps on the raw echo data, such as azimuth and range direction processing. However, these traditional methods still have limitations in improving the SNR, especially in complex environments or when the target signal is weak, where their effectiveness is often unsatisfactory. To address these issues, this paper first analyzes the SNR gain in SAR echo data processing and proposes a phase-compensated parameter-adjusted Chebyshev filtering algorithm to improve the SNR of SAR echoes. The algorithm first utilizes azimuth Chebyshev filtering to avoid spectral aliasing during downsampling and fully leverages navigation information provided by the airborne platform to accurately compensate for phase changes between pulses. Then, it employs parameter-adjusted Chebyshev filtering and coherent superposition techniques to combine multiple adjacent pulses into a single pulse with a higher SNR. Finally, the enhanced pulses are combined into a new two-dimensional matrix for subsequent pulse compression and imaging processing. This method can improve the echo SNR while reducing the amount of echo data, minimizing the loss of the original echo SNR and reducing the memory footprint of subsequent imaging processing, thus effectively improving data processing efficiency. The effectiveness of the algorithm is verified through simulation and actual measurement data. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? This paper proposes a parameter-adjusted Chebyshev filtering algorithm for UAV SAR echo signal enhancement based on phase compensation. This algorithm fully utilizes the high repetition rate characteristic of UAV SAR systems, improving the pulse SNR while reducing the amount of data. The SNR gain during SAR processing and its relationship with SNR and PRF were analyzed. What are the implications of the main findings? This method effectively overcomes the problem that traditional SAR echo azimuth processing cannot compensate for phase changes between adjacent pulses, and makes full use of the energy of each pulse, thereby reducing the SNR loss caused by filtering and downsampling. This method can significantly reduce the amount of echo data, decrease memory usage, and improve the efficiency of SAR echo signal processing. Unmanned aerial vehicle Synthetic Aperture Radar (UAV SAR) is inevitably affected by hardware performance and complex electromagnetic environments, resulting in noise in the radar echo signal. This causes image blurring and loss of detail, severely limiting the detection performance and imaging quality of UAV SAR. High-repetition-rate UAV SAR can achieve high signal-to-noise ratio (SNR), but the SAR data volume grows exponentially, posing a challenge for large-scale data processing. Furthermore, in the case of high repetition rate, downsampling methods are needed to reduce the amount of raw data, which leads to a decrease in the echo SNR, thus significantly affecting SAR image details. Existing SAR signal processing methods typically involve a series of processing steps on the raw echo data, such as azimuth and range direction processing. However, these traditional methods still have limitations in improving the SNR, especially in complex environments or when the target signal is weak, where their effectiveness is often unsatisfactory. To address these issues, this paper first analyzes the SNR gain in SAR echo data processing and proposes a phase-compensated parameter-adjusted Chebyshev filtering algorithm to improve the SNR of SAR echoes. The algorithm first utilizes azimuth Chebyshev filtering to avoid spectral aliasing during downsampling and fully leverages navigation information provided by the airborne platform to accurately compensate for phase changes between pulses. Then, it employs parameter-adjusted Chebyshev filtering and coherent superposition techniques to combine multiple adjacent pulses into a single pulse with a higher SNR. Finally, the enhanced pulses are combined into a new two-dimensional matrix for subsequent pulse compression and imaging processing. This method can improve the echo SNR while reducing the amount of echo data, minimizing the loss of the original echo SNR and reducing the memory footprint of subsequent imaging processing, thus effectively improving data processing efficiency. The effectiveness of the algorithm is verified through simulation and actual measurement data. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18060862 |
