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Most navigation systems of unmanned surface vehicles (USVs) are based on global position system (GPS)/inertial navigation system (INS) integrated methods to improve the accuracy of the navigation system. But in some places on the Earth's surface, the GPS signal suffers from outages, interferences, and weakness, which affect the performance of the whole USV navigation system. This paper provides a continuous and accurate navigation solution via integrated GPS with micro-electro-mechanical (MEMS)-INS smartphone sensors and reduced-aided visual odometry (RAVO) using centralized Kalman filter (CKF) data fusion. The proposed RAVO is used to correct USV navigation system errors during GPS outages. It is based on integrated visual odometry (VO) with MEMS-INS smartphone sensors, the Doppler velocity log (DVL), depth and compass sensors. The DVL/depth/compass/MEMS-INS smartphone integrated solution is used to improve the performance of a VO system before data fusion processing using a CKF. The CKF is used as data fusion processing to estimate and correct USV navigation system errors.The efficiency of the USV navigation system is tested on a surface reference trajectory called Kur-Mukalla in Mukalla City, Yemen. During GPS outage for 83 s, the proposed method based on the GPS/RAVO/MEMS-INS smartphone integrated CKF method could provide a reliable USV navigation solution; it reduced the position error to 82.32% compared to the traditional GPS/MEMS-INS CKF method, and to about 70.72% compared to the GPS/Pure-VO/MEMS-INS CKF integrated method.

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