Auditory model-based estimation of the effect of head-worn devices on frontal horizontal localisation

Auditory localisation accuracy may be degraded when a head-worn device (HWD), such as a helmet or hearing protector, is used. A computational method is proposed in this study for estimating how horizontal plane localisation is impaired by a HWD through distortions of interaural cues. Head-related impulse responses (HRIRs) of different HWDs were measured with a KEMAR and a binaural auditory model was used to compute interaural cues from HRIR-convolved noise bursts. A shallow neural network (NN) was trained with data from a subjective listening experiment, where horizontal plane localisation was assessed while wearing different HWDs. Interaural cues were used as features to estimate perceived direction and position uncertainty (standard deviation) of a sound source in the horizontal plane with the NN. The NN predicted the position uncertainty of localisation among subjects for a given HWD with an average estimation error of 1°. The obtained results suggest that it is possible to predict the degradation of localisation ability for specific HWDs in the frontal horizontal plane using the method.

Test Results of the Long Baseline Navigation Solutions under a Large a Priori Position Uncertainty

A method is proposed for long baseline navigation of autonomous underwater vehicles (AUV) to be used in the case of a large a priori position uncertainty. The new modified method is based on the iterated Kalman filter (IKF) working with different initial linearization points. The final solution is calculated by clustering and weighting the IKF results. This approach allows position estimates to be determined in accordance with the global maximum of posteriori probability density of coordinates. The test results obtained with the use of three beacons and an underwater vehicle are presented.

Uncertainty-induced instantaneous speed and acceleration of a levitated particle

Levitating nanoparticles trapped in optical potentials at low pressure open the experimental investigation of nonlinear ballistic phenomena. With engineered non-linear potentials and fast optical detection, the observation of autonomous transient mechanical effects, such as instantaneous speed and acceleration stimulated purely by initial position uncertainty, are now achievable. By using parameters of current low pressure experiments, we simulate and analyse such uncertainty-induced particle ballistics in a cubic optical potential demonstrating their evolution, faster than their standard deviations, justifying the feasibility of the experimental verification. We predict, the maxima of instantaneous speed and acceleration distributions shift alongside the potential force, while the maximum of position distribution moves opposite to it. We report that cryogenic cooling is not necessary in order to observe the transient effects, while a low uncertainty in initial particle speed is required, via cooling or post-selection, to not mask the effects. These results stimulate the discussion for both attractive stochastic thermodynamics, and extension of recently explored quantum regime.

Kalman filtering approach to particle track filtering and track uncertainty quantification for 3D PTV measurement

Three-dimensional Particle Tracking Velocimetry (3D-PTV) is a non-invasive flow measurement technique that computes the velocity field by reconstructing 3D particle positions of individual tracer particles and by subsequently tracking those positions. The particle velocity measurement accuracy depends on the faithful reconstruction of 3D particle positions. The complex measurement chain in 3D-PTV involves several steps, from calibration to 3D position reconstruction and particle position tracking, each having its own source of error. Additionally, higher seeding density increases the uncertainty in particle reconstruction and tracking, which in turn, increases the noise in the estimated tracks. A noisy track decreases the measurement accuracy and amplifies any noise in the PTV-derived quantities of interest, which includes acceleration, pressure and vorticity. Thus, track filtering techniques are critical in a 3D-PTV measurement. Track fitting using polynomial functions, filtering methods adopted from signal processing and object tracking are among the well-established techniques used to achieve smooth position, velocity estimates from reconstructed particle trajectories. The Kalman filter is one such filtering technique that is widely used in various applications. The strength of the Kalman filter lies in its ability to perform noise reduction that is informed by existing physical models and the uncertainty estimates of recorded measurements. However, the measurement uncertainty input to the Kalman filter needs to be known at priori, which in many cases may not be available or could be difficult to estimate. In the literature on Kalman filters and their variants applied to 2D-PIV/PTV, the position uncertainty data fed to the filter is either user-defined or estimated based on global noise levels in the PTV measurements. But instantaneous position and velocity uncertainty quantification for individual particle positions/tracks has been challenging in the 3D PTV community. Recent work by Bhattacharya and Vlachos (2020) provides an estimate of the uncertainty in the reconstructed particle positions for a 3D PTV measurement. This position uncertainty estimate dynamically updates the filter gain for each track and enables the evaluation of the performance of the Kalman filter in 3D PTV track filtering.

Impact of Transmitter Positioning and Orientation Uncertainty on RSS-Based Visible Light Positioning Accuracy

This paper present simulation-based results on the impact of transmitter (Tx) position and orientation uncertainty on the accuracy of the visible light positioning (VLP) system based on the received signal strength (RSS). There are several constraining factors for RSS-based algorithms, particularly due to multipath channel characteristics and set-up uncertainties. The impact of Tx uncertainties on positioning error performance is studied, assuming a statistical modelling of the uncertainties. Simulation results show that the Tx uncertainties have a severe impact on the positioning error, which can be leveraged through the usage of more transmitters. Concerning a smaller Tx’s position uncertainty of 5 cm, the average positioning errors are 23.3, 15.1, and 13.2 cm with the standard deviation values of 6.4, 4.1, and 2.7 cm for 4-, 9-, and 16-Tx cases, respectively. While for a smaller Tx’ orientation uncertainty of 5°, the average positioning errors are 31.9, 20.6, and 17 cm with standard deviation values of 9.2, 6.3, and 3.9 cm for 4-, 9-, and 16-Tx cases, respectively.

High-Precision Drill Bit Tracking

The lateral well position uncertainty of magnetic/gyro MWD measurements can often exceed the requirements regarding anti-collision, for optimal placement of infill wells between existing producers, or for hitting targets with limited geological extent. The positional uncertainty can be significantly reduced by implementing high-precision drill-bit localization using passive seismic data. Consequently, not only drilling risks can be reduced, but optimal reservoir drainage is ensured as well. By utilizing passive seismic recordings from the seafloor, we can "listen" to the noise generated by the BHA while drilling. Despite various noise sources in the vicinity (e.g. vessels and rigs), advanced data processing and the combination of hundreds of seafloor receivers spread above the ongoing drilling, enable us to detect the drilling signal and locate the drill bit. Whereas the magnetic and gyro MWD tools have errors that accumulate with measured depth, each bit position derived from seismic (usually every 90 seconds) is completely independent. For horizontal sections, the error does not increase with measured depth, and hence can provide improved lateral accuracy. No additional BHA tool is required and the measurements are neither dependent on the magnetic nor gravitational field. Moreover, the passive seismic measurements can be used to obtain an improved lateral well position estimate. This is done by optimizing the azimuth information of the well trajectory in the minimum curvature method. A lateral uncertainty measure can be derived from the residuals between the passive measurements and the updated well path. Since 2018, we have used the continuous stream of passive data from permanent seafloor sensors at the Grane field with its reservoir depth of around 1800 m TVDSS to follow all wells with this drill bit tracking scheme. Lateral deviations from the magnetic/gyro measurements of up to 20m have been observed. The lateral position uncertainty can be as low as a couple of meters under optimal conditions.

Position uncertainties of AGATA pulse-shape analysis estimated via the bootstrapping method

The unprecedented capabilities of state-of-the-art segmented germanium-detector arrays, such as AGATA and GRETA, derive from the possibility of performing pulse-shape analysis. The comparison of the net- and transient-charge signals with databases via grid-search methods allows the identification of the $$\gamma $$ γ -ray interaction points within the segment volume. Their precise determination is crucial for the subsequent reconstruction of the $$\gamma $$ γ -ray paths within the array via tracking algorithms, and hence the performance of the spectrometer. In this paper the position uncertainty of the deduced interaction point is investigated using the bootstrapping technique applied to $$^{60}$$ 60 Co radioactive-source data. General features of the extracted position uncertainty are discussed as well as its dependence on various quantities, e.g. the deposited energy, the number of firing segments and the segment geometry.