Real-time single frequency precise point positioning (PPP) is a promising technique for high-precision navigation with sub-meter or even centimeter-level accuracy because of its convenience and low cost. The navigation performance of single frequency PPP heavily depends on the real-time availability and quality of correction products for satellite orbits and satellite clocks. Satellite-based augmentation system (SBAS) provides the correction products in real-time, but they are intended to be used for wide area differential positioning at 1 meter level precision. By imposing the constraints for ionosphere error, we have developed a real-time single frequency PPP method by sufficiently utilizing SBAS correction products. The proposed PPP method are tested with static and kinematic data, respectively. The static experimental results show that the position accuracy of the proposed PPP method can reach decimeter level, and achieve an improvement of at least 30% when compared with the traditional SBAS method. The positioning convergence of the proposed PPP method can be achieved in 636 epochs at most in static mode. In the kinematic experiment, the position accuracy of the proposed PPP method can be improved by at least 20 cm relative to the SBAS method. Furthermore, it has revealed that the proposed PPP method can achieve decimeter level convergence within 500 s in the kinematic mode.

The noise interference of side-scan sonar images is stronger than that of optical images, and the gray level is uneven. To solve this problem, we propose a side-scan sonar image enhancement method based on multi-stage repairing image fusion. Firstly, to remove the environmental noise in the sonar image, we perform adaptive Gaussian smoothing on the original image and the weighted average grayscale image. Then, the smoothed images are all processed through multi-stage image repair. The multi-stage repair network consists of three stages. The first two stages consist of a novel encoder–decoder architecture to extract multi-scale contextual features, and the third stage uses a network based on the resolution of the original inputs to generate spatially accurate outputs. Each phase is not a simple stack. Between each phase, the supervised attention module (SAM) improves the repair results of the previous phase and passes them to the next phase. At the same time, the multi-scale cross-stage feature fusion mechanism (MCFF) is used to complete the information lost in the repair process. Finally, to correct the gray level, we propose a pixel-weighted fusion method based on the unsupervised color correction method (UCM), which performs weighted pixel fusion between the RGB image processed by the UCM algorithm and the gray-level image. Compared with the algorithm with the SOTA methods on datasets, our method shows that the peak signal-to-noise ratio (PSNR) is increased by 26.58%, the structural similarity (SSIM) is increased by 0.68%, and the mean square error (MSE) is decreased by 65.02% on average. In addition, the processed image is balanced in terms of image chromaticity, image contrast, and saturation, and the grayscale is balanced to match human visual perception.

Ultra-short baseline (USBL) underwater positioning systems are widely used in marine scientific research and ocean engineering. Angle misalignment is a main error that reduces the accuracy of USBL underwater positioning. The conventional angle-calibration method assumes that the transponder position obtained by USBL positioning is an errorless coefficient matrix. However, errors inevitably exist in the estimation of the transponder’s position via USBL positioning, and the precision varies at different epochs. Ignoring the error in the transponder’s position will significantly reduce the precision of the angle misalignment estimation. In this paper, a new angle-calibration method is proposed for precise USBL underwater positioning. The angle alignment model is derived by treating the transponder’s position obtained by USBL positioning as an observation, and the stochastic model is then established according to the bearing angles. Robust estimation is likewise applied to further improve the precision of the angle misalignment estimation. To verify the performance of the proposed method, a sea experiment was performed. The results show that the new method has high calibration accuracy and robustness. The estimation precision of this method is improved by 0.0457°~0.6896° in heading, 0.0125°~0.8072° in roll, and 0.0077°~0.9436° in pitch, compared with that of the conventional angle alignment method.