Range-segment Motion Compensation based on Integrated INS for Wide-swath Synthetic Aperture Sonar

2019 International Conference on Advanced Electronic Materials, Computers and Materials Engineering

无线电电子学;计算机科学;材料科学

Zhang, Yu^1^2^3 ; Zhong, Rongxing^1^2^3 ; Liu, Jiyuan^1^2 ; Wei, Linzhe^1^2 ; Wang, Peng^1^2 ; Zhang, Pengfei^1^2

Institute of Acoustics, Chinese Academy of Sciences, Beijing, China^1

Key Laboratory of Science and Technology on Advanced Underwater Acoustic Signal Processing, Chinese Academy of Sciences, Beijing, China^2

University of Chinese Academy of Sciences, Beijing, China^3

关键词: Conventional methods;    Displaced phase center antenna;    Doppler velocity logs;    Global position systems;    Inertial navigation systems (INS);    Multisensor data fusion;    Synthetic aperture sonar (SAS);    Vehicle instability;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/563/4/042089/pdf
DOI  :  10.1088/1757-899X/563/4/042089

来源: IOP

【 摘 要 】

Motion errors have severe effect on synthetic aperture sonar (SAS) imagery if worse than a fraction of a wavelength along the entire aperture. Not only currents, but vehicle instability randomly causes periodic motion errors in the synthetic aperture and grating lobes in the SAS images as well. Displaced phase center antenna referred to as micro-navigation has been developed with multiple-receiver SAS for a long time, but still has challenges against larger motion errors. The integrated inertial navigation system comprising an inertial navigation system (INS) and other aided sensors such as a Doppler Velocity Log (DVL) and a surface ship's Global Position System (GPS) is an unavoidable and trade-off solution to the problem of motion estimate, which is definitely more robust and reliable with the application of the multi-sensor data fusion. Besides, the motion error is variant along the slant range. For a better resolution of the wide-swath SAS, a range-segment motion compensation method based on the integrated inertial navigation system is presented in the paper. This method relies on the multi-sensor data fusion of the integrated inertial navigation system via Kalman filter and is found to significantly outperform the conventional method.