Error analysis and correction of atmospheric disturbance for interferometric imaging radar altimeter

Abstract

The interferometric imaging radar altimeter (InIRA) is a newly developed microwave remote sensing system in recent years. It is of great significance to analyze the main sources of its height measurement error through simulation, and to develop the corresponding image phase error correction methods. This can provide basis and reference for the subsequent correction of the real images phase error. In this study, according to the interaction principle between atmospheric interference and InIRA radar electromagnetic waves, the influence of background ionosphere and background troposphere on height measurement error is analyzed by introducing ionosphere and troposphere delays into echo signals respectively. The impact of ionospheric scintillation and atmospheric turbulence on the height measurement error is analyzed by building multi-layer phase screen models. Then according to the analysis results, a phase error correction method for InIRA images combined by the improved fruit fly optimization algorithm (IFOA) and the joint minimum entropy criterion (JMEC) is proposed. The image is divided into several sub-images with negligible spatial variation of phase error according to the principle of first range and then azimuth. IFOA is used to search the optimal phase compensation values iteratively to compensate the phase of all sub-images. When the joint entropy of the sub-images reaches the minimum value, the phase error correction of the whole InIRA image is completed. Experimental results show that the proposed method can correct the phase of InIRA images with inconspicuous features and polluted by weak ionospheric scintillation and atmospheric turbulence relatively quickly. After correction, the height measurement error can be reduced from decimeter level to centimeter level. The correction accuracy and efficiency of the proposed method are superior to those of the method combined by particle swarm optimization algorithm and joint minimum entropy criterion (PSO-JMEC), the method combined by genetic algorithm and joint minimum entropy criterion (GA-JMEC), the method combined by phase gradient autofocus and Map Drift (PGA-MD), and improved PGA-MD algorithm.