Abstract
The combination of synthetic aperture radar (SAR) with unmanned aerial vehicles (UAVs) is attractive for acquiring high-resolution images without the restrictions of time and weather. However, because UAVs are lightweight and small in size, the UAV SAR system is very sensitive to turbulence, resulting in serious trajectory deviations and remaining residual motion errors (RMEs), such as residual range cell migration (RCM) and aperture phase errors (APEs). In this paper, a novel motion compensation (MoCo) strategy is developed based on the symmetric triangle linear frequency modulated continuous wave (STLFMCW) signal. The STLFMCW signal model is first presented, which reveals the relevance between the positive-negative frequency modulations and motion errors. The residual RCMs and relative phase errors are estimated directly through the phase differential interferometry of up-ramp and down-ramp chirp signals, instead of the traditional approach of relying on the range-shift gradient of adjacent range profiles. The proposed approach is independent of conventional prominent point processing (PPP) and calculates the range deviation to avoid the accumulation of errors, thereby achieving high precision and efficiency. Experiments based on simulated and measured data sets validate the proposed approach.
Highlights
Unmanned aerial vehicle synthetic aperture radar (UAV SAR) is an advanced remote sensing technique that can obtain high-resolution images with high efficiency and low energy consumption for many applications, such as search and rescue operations and civil infrastructure inspection missions [1]–[3]
Two basic methods are employed for the removal of residual range cell migration (RCM): one is to apply conventional algorithms, e.g., phase gradient autofocus (PGA), to obtain the aperture phase errors (APEs) at a coarser resolution after downsampling; the other is to determine the range shift gradient through range profile cross-correlation followed by interpolation [17]–[19]
The results indicate that the imaging quality is very close to the ideal quality, and the focusing damage caused by large residual motion errors (RMEs) is overcome by our method
Summary
Unmanned aerial vehicle synthetic aperture radar (UAV SAR) is an advanced remote sensing technique that can obtain high-resolution images with high efficiency and low energy consumption for many applications, such as search and rescue operations and civil infrastructure inspection missions [1]–[3]. Two basic methods are employed for the removal of residual RCM: one is to apply conventional algorithms, e.g., PGA, to obtain the APEs at a coarser resolution after downsampling; the other is to determine the range shift gradient through range profile cross-correlation followed by interpolation [17]–[19] The former method is based on ideal prominent point targets and is greatly dependent on the scattered field. The RCM correction method in the imaging algorithm may couple the RMEs to the azimuth direction, resulting in an unknown phase This mathematical model is rational and natural for an FMCW SAR system that receives echoes in de-chirp mode. The results indicate that the imaging quality is very close to the ideal quality, and the focusing damage caused by large RMEs is overcome by our method
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