IMPROVED SYNTHETIC APERTURE SONAR MOTION COMPENSATION COMBINED DPCA WITH SUB-APERTURE IMAGE CORRELATION

Estimation precision of Displaced Phase Center Algorithm(DPCA) is affected by the number of displaced phase center pairs,the bandwidth of transmitting signal and many other factors.Detailed analysis is made on DPCA's estimation precision.Analysis results show that the directional vector estimation precision of DPCA is low,which will produce accumulating errors when phase cen-ters' track is estimated.Because of this reason,DPCA suffers from accumulating errors seriously.To overcome this problem,a method combining DPCA with Sub Aperture Image Correlation(SAIC) is presented.Large synthetic aperture is divided into sub-apertures.Micro errors in sub-aperture are estimated by DPCA and compensated to raw echo data.Bulk errors between sub-apertures are esti-mated by SAIC and compensated directly to sub-aperture images.After that,sub-aperture images are directly used to generate ultimate SAS image.The method is applied to the lake-trial dataset of a 20 kHz SAS prototype system.Results show the method can successfully remove the accumulating error and produce a better SAS image.

Phase synchronization processing method for alternating bistatic mode in distributed SAR

In the distributed synthetic aperture radar （SAR）, the alternating bistatic mode can perform phase reference without a synchronization link between two satellites compared with the pulsed alternate synchronization method. The key of the phase synchronization processing is to extract the oscillator phase differences from the bistatic echoes. A signal model of phase synchronization in the alternating bistatic mode is presented. The phase synchronization processing method is then studied. To reduce the phase errors introduced by SAR imaging, a sub-aperture processing method is proposed. To generalize the sub-aperture processing method, an echo-domain processing method using correlation of bistatic echoes is proposed. Finally, the residual phase errors of the both proposed processing methods are analyzed. Simulation experiments validate the proposed phase synchronization processing method and its phase error analysis results.

Light field depth estimation:A comprehensive survey from principles to future

Light Field(LF)depth estimation is an important research direction in the area of computer vision and computational photography,which aims to infer the depth information of different objects in threedimensional scenes by capturing LF data.Given this new era of significance,this article introduces a survey of the key concepts,methods,novel applications,and future trends in this area.We summarize the LF depth estimation methods,which are usually based on the interaction of radiance from rays in all directions of the LF data,such as epipolar-plane,multi-view geometry,focal stack,and deep learning.We analyze the many challenges facing each of these approaches,including complex algorithms,large amounts of computation,and speed requirements.In addition,this survey summarizes most of the currently available methods,conducts some comparative experiments,discusses the results,and investigates the novel directions in LF depth estimation.

A survey for light field super-resolution

Compared to 2D imaging data,the 4D light field(LF)data retains richer scene’s structure information,which can significantly improve the computer’s perception capability,including depth estimation,semantic segmentation,and LF rendering.However,there is a contradiction between spatial and angular resolution during the LF image acquisition period.To overcome the above problem,researchers have gradually focused on the light field super-resolution(LFSR).In the traditional solutions,researchers achieved the LFSR based on various optimization frameworks,such as Bayesian and Gaussian models.Deep learning-based methods are more popular than conventional methods because they have better performance and more robust generalization capabilities.In this paper,the present approach can mainly divided into conventional methods and deep learning-based methods.We discuss these two branches in light field spatial super-resolution(LFSSR),light field angular super-resolution(LFASR),and light field spatial and angular super-resolution(LFSASR),respectively.Subsequently,this paper also introduces the primary public datasets and analyzes the performance of the prevalent approaches on these datasets.Finally,we discuss the potential innovations of the LFSR to propose the progress of our research field.