合成孔径雷达(SAR)由于其良好的特性被广泛应用于高分辨成像,但成像所需的庞大数据导致其难以在资源受限的平台推广应用.单比特SAR通过将回波采样点表征为 1 比特二进制数字信号,可以达到降低数据量、缓解平台负担的目的,但二值数据跳变产生的高阶谐波将导致成像质量下降.为提升单比特 SAR 成像质量,提出基于卷积去量化(CDQOB)网络的无人机载条带SAR成像方法,通过单比特子孔径数据实现运动误差估计与智能化距离-多普勒二维谱重构,进而实现低数据量下的高质量条带SAR成像.通过实测数据的处理分析,验证了所提单比特成像方法的有效性.
One-bit UAV SAR Imaging Based on CDQOB Network Sub-aperture Reconstruction
Synthetic aperture radar(SAR)has been widely applied for high-resolution imaging due to its excellent characteristics.However,the large amount of data required for imaging makes it difficult to apply on resource-constrained platforms.One-bit SAR re-duces data volume and alleviates the platform burden by representing echo samples as one-bit binary digital signals.Nevertheless,the high-order harmonics generated by the binary data transitions result in a degradation in imaging quality.To improve the imaging qual-ity of one-bit SAR,this paper proposes a UAV-borne stripmap SAR imaging method based on the convolutional de-quantization one-bit(CDQOB)network.The proposed method utilizes one-bit sub-aperture data to achieve motion error estimation and intelligent Range-Doppler two-dimensional spectrum reconstruction,thereby achieving high-quality stripmap SAR imaging with reduced data vol-ume.The effectiveness of the proposed one-bit imaging method has been validated through the analysis of measured data.
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