Reconstruction of synthetic aperture radar raw data under analog-to-digital converter saturation distortion for large dynamic range scenes

Abstract

Digital storage and transmission are common processes in modern synthetic aperture radar systems; thus, analog-to-digital converters are indispensable. Such processes can lead to two types of error: quantization (or granular) error and saturation (or clipping) error, which cause sampling noise, and radiometric and harmonic distortions in final images. Traditionally, reasonable choices of the gain and the number of quantization bits by the analog-to-digital converter based on the echo distribution can effectively reduce these errors. However, establishing the gain control repository of a synthetic aperture radar mission is a long process. In addition, if the dynamic range of the backscattering coefficient is extremely large or if unexpected strong targets appear in a scene, then harmonics occur in the echo, which turns the variable gain amplifier into chaos based on statistic and, inevitably, results in saturation in the raw data. Once raw data saturation occurs, the SAR system can conventionally adjust only the analog-to-digital converter in the next observation, thus reducing timeliness. Power loss compensation based on a statistical model and saturation (clipping) factor on a large-scale could compensate for the energy loss in images; however, detail interference, such as harmonic distortion, cannot be effectively suppressed, which will lead to false targets in the focused data. To address this particular problem, a novel anti-saturation method for large dynamic range scenes is proposed in this paper. The log-normal distribution is used in this article to describe dynamic range scenes with strong isolated targets, which mainly cause receiver saturation. Using the statistical distribution of complex scenes as a priori information, a maximum a posteriori estimation algorithm is proposed to simultaneously compensate for the saturated values in the raw data and retain the non-saturated values. Thus, the details of the weak background are well preserved, and the isolated strong targets with sparsity are reconstructed perfectly. With Monte Carlo simulation, the proposed method can improve the radiometric accuracy by 5 to 10 dB and effectively suppress the energy of false targets. Based on TerraSAR-X, ALOS-2, and Radarsat-1 synthetic aperture radar data, the effectiveness and robustness of the proposed method are also verified by simulations.