Limited by the detection principle, an electromagnetic log (EML) can only output a velocity measurement relative to the ocean current in marine applications, which thus leads to a negative effect on a strapdown inertial navigation system (SINS)/EML integrated navigation system. The current velocity usually can be modeled as a first-order Gaussian Markov process, while the Markov time correlation constant should be determined in advance and used as a constant during the navigation process. The integrated navigation system will suffer performance degradation if an incorrect time correlation constant is applied. In order to tackle the problem, an ocean current coefficient estimation method is proposed in this manuscript. According to the Markov nature, the current velocity at the present time epoch is determined by that at the previous time epoch. Consequently, the current velocity corrected by the Kalman filter (KF) at the previous time epoch is utilized as the measurement information at the present time epoch, where the current coefficient is introduced into the state vector. The observability analysis shows that the current coefficient is totally observable. Simulation and field test results indicate that the proposed method can accurately estimate the current coefficient. Compared to the SINS/EML integrated navigation system without considering the current effects, the system with an estimated current coefficient minimizes the effect caused by ocean currents.

The hull deformation can be estimated by comparing the angular velocity outputs from two inertial navigation systems (INSs), whose estimation accuracy affects the large ship transfer alignment accuracy greatly. However, due to the existence of the cross-correlation error, which originates from the cross-correlation effect between the ship and dynamic deformation angular velocities, the estimated static hull deformation angle will be biased. Based on the inertial matching equation, an analysis of how the cross-correlation error to the hull deformation angle measurement is provided in this manuscript. It is demonstrated that a linear correlation coefficient between the ship and deformation angular velocities will be coupled into the hull deformation angle estimation as a biased deformation angle error. In order to mitigate the biased error, an error estimation method is further presented to compensate the cross-correlation error, based on the least square estimation and correlation theory. The simulation and experimental results indicate that the proposed method can accurately compensate the cross-correlation error and effectively reduce biased deformation angle error, which is helpful in improving the larger ship transfer alignment accuracy.

We propose a motion redirection system for virtual reality and augmented reality, automatically detecting a person's action in a source scene and redirecting it to a new target scene. The redirected motion is augmented in the target scene based on the understanding of the target scene's semantics and layout. The results show that the system may facilitate telecommunication across scenes, improving user interaction experiences.

In order to address the issue of rapidly divergent positioning errors of a single-platform inertial navigation system (INS) in GNSS-denied environments, this paper proposes a cross-platform cooperative navigation method based on relative range and angle measurements. The observability of the cooperative navigation system under different motion strategies is investigated using Fisher information matrix (FIM) right null-space analysis combined with singular value decomposition (SVD). The results show that with relative range and angle measurement constraints, all inertial sensor biases can be effectively estimated by two strapdown inertial navigation systems (SINSs) moving along a simple trajectory, thereby improving the navigation accuracy. Experimental results demonstrate that compared to the autonomous navigation mode, the average positioning accuracy of the two SINSs improves by 77.4% and 68.4% respectively after 3 h of cooperative navigation along the prescribed trajectory. Using relative range and angle measurements, the proposed method requires only two SINSs and relatively simple planar motion, without the need for high-precision reference benchmarks, complex three-dimensional excitation trajectories, or turntable modulation. It reduces system complexity and motion requirements, providing an effective and easy-to-implement solution for ground vehicular positioning and orientation and other cross-platform cooperative navigation tasks in GNSS-denied environments.