基于多传感器的圆周合成孔径声呐运动补偿方法OA

Circular synthetic aperture sonar(CSAS)is a high-resolution underwater imaging sonar system mounted on a mobile platform,whose imaging quality is highly sensitive to the precision of platform position and attitude.However,in practical survey operations,platforms are often affected by environmental disturbances such as ocean currents and waves,causing deviations from the ideal trajectory and resulting in position and attitude errors,which significantly degrade imaging quality.Existing CSAS motion compensation methods mainly rely on underwater acoustic positioning systems to obtain the platform position.However,such methods usually require the deployment of additional underwater acoustic base stations,with a strong dependence on external equipment.To address this issue,this paper investigated the influence of CSAS platform motion errors on imaging results and the corresponding geometric compensation methods.A CSAS platform motion compensation method based on multi-sensor fusion of the global positioning system(GPS)and the inertial navigation system(INS)was proposed.By integrating the position information obtained from GPS with the attitude data provided by the INS,the spatial pose of the sonar platform could be jointly estimated,and the spatial position of the sonar array could be corrected accordingly,thereby reducing the influence of platform motion errors on the imaging results.The influence of platform motion errors on imaging results was simulated and analyzed,and the effectiveness of the proposed method was verified through lake trial data.The experimental results demonstrate that the proposed approach can effectively compensate for motion errors caused by platform motion and significantly improve the imaging quality of CSAS.The proposed method does not rely on external underwater acoustic positioning systems and instead utilizes only the onboard navigation sensors of the platform to achieve effective motion compensation.This provides a simple and practical solution for high-resolution CSAS imaging in complex environments.