Ambisonics Sound Source Localization With Varying Amount of Visual Information in Virtual Reality

To reproduce realistic audio-visual scenarios in the laboratory, Ambisonics is often used to reproduce a sound field over loudspeakers and virtual reality (VR) glasses are used to present visual information. Both technologies have been shown to be suitable for research. However, the combination of both technologies, Ambisonics and VR glasses, might affect the spatial cues for auditory localization and thus, the localization percept. Here, we investigated how VR glasses affect the localization of virtual sound sources on the horizontal plane produced using either 1st-, 3rd-, 5th- or 11th-order Ambisonics with and without visual information. Results showed that with 1st-order Ambisonics the localization error is larger than with the higher orders, while the differences across the higher orders were small. The physical presence of the VR glasses without visual information increased the perceived lateralization of the auditory stimuli by on average about 2°, especially in the right hemisphere. Presenting visual information about the environment and potential sound sources did reduce this HMD-induced shift, however it could not fully compensate for it. While the localization performance itself was affected by the Ambisonics order, there was no interaction between the Ambisonics order and the effect of the HMD. Thus, the presence of VR glasses can alter acoustic localization when using Ambisonics sound reproduction, but visual information can compensate for most of the effects. As such, most use cases for VR will be unaffected by these shifts in the perceived location of the auditory stimuli.

Tracking Control of Autonomous Underwater Vehicles with Acoustic Localization and Extended Kalman Filter

The absence of global positioning system (GPS) signals and the influence of ocean currents are two of the main challenges facing the autonomy of autonomous underwater vehicles (AUVs). This paper proposes an acoustic localization-based tracking control method for AUVs. Particularly, three buoys that emit acoustic signals periodically are deployed over the surface. Times of arrivals of these acoustic signals at the AUV are then obtained and used to calculate an estimated position of the AUV. Moreover, the uncertainties involved in the localization and ocean currents are handled together in the framework of the extended Kalman filter. To deal with system physical constraints, model predictive control relying on online repetitive optimizations is applied in the tracking controller design. Furthermore, due to the different sampling times between localization and control, the dead-reckoning technique is utilized considering detailed AUV dynamics. To avoid using the highly nonlinear and complicated AUV dynamics in the online optimizations, successive linearizations are employed to achieve a trade-off between computational complexity and control performance. Simulation results show that the proposed algorithms are effective and can achieve the AUV tracking control goals.