A Single-Step Method for Over-the-Horizon Geolocation Using Importance Sampling

This paper investigates the geolocation for an over-the-horizon (OTH) transmitter observed by widely separated arrays. We propose a maximum likelihood (ML) based direct position determination (DPD) method to directly locate the transmitter in a single step by exploiting the position information embedded in azimuth angles. The Monte Carlo importance sampling (IS) technique is employed to find an approximate global solution to this DPD problem, where the importance function analogous to Gaussian distribution is derived. This enables the transmitter to be precisely located with low complexity in a noniterative manner. Additionally, we derive the Cramér–Rao bound (CRB) expression for the investigated problem. The simulation results corroborate the superior localization performance of the proposed method with respect to the conventional two-step approaches and the iterative DPD method.

NR-UIO: NLOS-Robust UWB-Inertial Odometry Based on Interacting Multiple Model and NLOS Factor Estimation

Recently, technology utilizing ultra-wideband (UWB) sensors for robot localization in an indoor environment where the global navigation satellite system (GNSS) cannot be used has begun to be actively studied. UWB-based positioning has the advantage of being able to work even in an environment lacking feature points, which is a limitation of positioning using existing vision- or LiDAR-based sensing. However, UWB-based positioning requires the pre-installation of UWB anchors and the precise location of coordinates. In addition, when using a sensor that measures only the one-dimensional distance between the UWB anchor and the tag, there is a limitation whereby the position of the robot is solved but the orientation cannot be acquired. To overcome this, a framework based on an interacting multiple model (IMM) filter that tightly integrates an inertial measurement unit (IMU) sensor and a UWB sensor is proposed in this paper. However, UWB-based distance measurement introduces large errors in multipath environments with obstacles or walls between the anchor and the tag, which degrades positioning performance. Therefore, we propose a non-line-of-sight (NLOS) robust UWB ranging model to improve the pose estimation performance. Finally, the localization performance of the proposed framework is verified through experiments in real indoor environments.

Amount of Frequency Compression in Bimodal Cochlear Implant Users Is a Poor Predictor for Audibility and Spatial Hearing

Purpose Speech understanding in noise and horizontal sound localization is poor in most cochlear implant (CI) users with a hearing aid (bimodal stimulation). This study investigated the effect of static and less-extreme adaptive frequency compression in hearing aids on spatial hearing. By means of frequency compression, we aimed to restore high-frequency audibility, and thus improve sound localization and spatial speech recognition. Method Sound-detection thresholds, sound localization, and spatial speech recognition were measured in eight bimodal CI users, with and without frequency compression. We tested two compression algorithms: a static algorithm, which compressed frequencies beyond the compression knee point (160 or 480 Hz), and an adaptive algorithm, which aimed to compress only consonants leaving vowels unaffected (adaptive knee-point frequencies from 736 to 2946 Hz). Results Compression yielded a strong audibility benefit (high-frequency thresholds improved by 40 and 24 dB for static and adaptive compression, respectively), no meaningful improvement in localization performance (errors remained > 30 deg), and spatial speech recognition across all participants. Localization biases without compression (toward the hearing-aid and implant side for low- and high-frequency sounds, respectively) disappeared or reversed with compression. The audibility benefits provided to each bimodal user partially explained any individual improvements in localization performance; shifts in bias; and, for six out of eight participants, benefits in spatial speech recognition. Conclusions We speculate that limiting factors such as a persistent hearing asymmetry and mismatch in spectral overlap prevent compression in bimodal users from improving sound localization. Therefore, the benefit in spatial release from masking by compression is likely due to a shift of attention to the ear with the better signal-to-noise ratio facilitated by compression, rather than an improved spatial selectivity. Supplemental Material https://doi.org/10.23641/asha.16869485

Imagine Your Crossed Hands as Uncrossed: Visual Imagery Impacts the Crossed-Hands Deficit

Successful interaction with our environment requires accurate tactile localization. Although we seem to localize tactile stimuli effortlessly, the processes underlying this ability are complex. This is evidenced by the crossed-hands deficit, in which tactile localization performance suffers when the hands are crossed. The deficit results from the conflict between an internal reference frame, based in somatotopic coordinates, and an external reference frame, based in external spatial coordinates. Previous evidence in favour of the integration model employed manipulations to the external reference frame (e.g., blindfolding participants), which reduced the deficit by reducing conflict between the two reference frames. The present study extends this finding by asking blindfolded participants to visually imagine their crossed arms as uncrossed. This imagery manipulation further decreased the magnitude of the crossed-hands deficit by bringing information in the two reference frames into alignment. This imagery manipulation differentially affected males and females, which was consistent with the previously observed sex difference in this effect: females tend to show a larger crossed-hands deficit than males and females were more impacted by the imagery manipulation. Results are discussed in terms of the integration model of the crossed-hands deficit.

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.

WiFi Fingerprinting Indoor Localization Based on Dynamic Mode Decomposition Feature Selection with Hidden Markov Model

Over the years, WiFi received signal strength indicator (RSSI) measurements have been widely implemented for determining the location of a user’s position in an indoor environment, where the GPS signal might not be received. This method utilizes a huge RSSI dataset collected from numerous access points (APs). The WiFi RSSI measurements are nonlinear with distance and are largely influenced by interference in the indoor environment. Therefore, machine learning (ML) techniques such as a hidden Markov model (HMM) are generally utilized to efficiently identify a trend of RSSI values, which corresponds to locations around a region of interest. Similar to other ML tools, the performance and computing cost of the HMM are dependent on the feature dimension since a large quantity of RSSI measurements are required for the learning process. Hence, this article introduces a feature extraction method based on dynamic mode decomposition (DMD) for the HMM to effectively model WiFi fingerprint indoor localization. The DMD is adopted since it decomposes RSSIs to meaningful spatial and temporal forms over a given time. Here, the mode forms are analytically reconstructed to produce low-dimensional feature vectors, which are used with the HMM. The localization performance of the proposed HMM-DMD is compared with other well-known ML algorithms for WiFi fingerprinting localization using simulations. The results show that the HMM-DMD algorithm yields a significant localization performance improvement, accuracy, and reasonable processing time in comparison with the state-of-the-art algorithms.

Proficiency in Using Level Cue for Sound Localization Is Related to the Auditory Cortical Structure in Patients With Single-Sided Deafness

Spatial hearing, which largely relies on binaural time/level cues, is a challenge for patients with asymmetric hearing. The degree of the deficit is largely variable, and better sound localization performance is frequently reported. Studies on the compensatory mechanism revealed that monaural level cues and monoaural spectral cues contribute to variable behavior in those patients who lack binaural spatial cues. However, changes in the monaural level cues have not yet been separately investigated. In this study, the use of the level cue in sound localization was measured using stimuli of 1 kHz at a fixed level in patients with single-sided deafness (SSD), the most severe form of asymmetric hearing. The mean absolute error (MAE) was calculated and related to the duration/age onset of SSD. To elucidate the biological correlate of this variable behavior, sound localization ability was compared with the cortical volume of the parcellated auditory cortex. In both SSD patients (n = 26) and normal controls with one ear acutely plugged (n = 23), localization performance was best on the intact ear side; otherwise, there was wide interindividual variability. In the SSD group, the MAE on the intact ear side was worse than that of the acutely plugged controls, and it deteriorated with longer duration/younger age at SSD onset. On the impaired ear side, MAE improved with longer duration/younger age at SSD onset. Performance asymmetry across lateral hemifields decreased in the SSD group, and the maximum decrease was observed with the most extended duration/youngest age at SSD onset. The decreased functional asymmetry in patients with right SSD was related to greater cortical volumes in the right posterior superior temporal gyrus and the left planum temporale, which are typically involved in auditory spatial processing. The study results suggest that structural plasticity in the auditory cortex is related to behavioral changes in sound localization when utilizing monaural level cues in patients with SSD.

A Hybrid AOA and TDOA-Based Localization Method Using Only Two Stations

Target localization plays an important role in the application of radar, sonar, and wireless sensor networks. In order to improve the localization performance using only two stations, a hybrid localization method based on angle of arrival (AOA) and time difference of arrival (TDOA) measurements is proposed in this paper. Firstly, the optimization model for localization based on AOA and TDOA are built, respectively, in the sensor network. Secondly,the majorization-minimization (MM) method is employed to create surrogate functions for solving the multiple objective optimization problem. Next, the hybrid localization problem is solved by the projected gradient decent (PGD) method. Finally, the Cramer–Rao lower bound (CRLB) for the joint AOA and TDOA method is derived for the comparison. Simulations proved that the proposed method has improved localization performance using AOA and TDOA measurements from only two base stations.

A Scalable Framework for Map Matching Based Cooperative Localization

Localization based on scalar field map matching (e.g., using gravity anomaly, magnetic anomaly, topographics, or olfaction maps) is a potential solution for navigating in Global Navigation Satellite System (GNSS)-denied environments. In this paper, a scalable framework is presented for cooperatively localizing a group of agents based on map matching given a prior map modeling the scalar field. In order to satisfy the communication constraints, each agent in the group is assigned to different subgroups. A locally centralized cooperative localization method is performed in each subgroup to estimate the poses and covariances of all agents inside the subgroup. Each agent in the group, at the same time, could belong to multiple subgroups, which means multiple pose and covariance estimates from different subgroups exist for each agent. The improved pose estimate for each agent at each time step is then solved through an information fusion algorithm. The proposed algorithm is evaluated with two different types of scalar field based simulations. The simulation results show that the proposed algorithm is able to deal with large group sizes (e.g., 128 agents), achieve 10-m level localization performance with 180 km traveling distance, while under restrictive communication constraints.