Incorporation of acoustic sensors in the regulation of a mobile robot

This article introduces the incorporation of acoustic sensors for the localization of a mobile robot. The robot is considered as a sound source and its position is located applying a Time Delay of Arrival (TDOA) method. Since the accuracy of this method varies with the microphone array, a navigation acoustic map that indicates the location errors is built. This map also provides the robot with navigation trajectories point-to-point and the control is capable to drive the robot through these trajectories to a desired configuration. The proposed localization method is thoroughly tested using both a 900 Hz square signal and the natural sound of the robot, which is driven near the desired point with an average error of 0:067 m.This is an Accepted Manuscript of an article published by Taylor & Francis in Advanced Robotics on 01/01/2019, available online: http://www.tandfonline.com/10.1080/01691864.2019.1573703.”Peer ReviewedPostprint (author's final draft

Source localization via time difference of arrival

Accurate localization of a signal source, based on the signals collected by a number of receiving sensors deployed in the source surrounding area is a problem of interest in various fields. This dissertation aims at exploring different techniques to improve the localization accuracy of non-cooperative sources, i.e., sources for which the specific transmitted symbols and the time of the transmitted signal are unknown to the receiving sensors. With the localization of non-cooperative sources, time difference of arrival (TDOA) of the signals received at pairs of sensors is typically employed. A two-stage localization method in multipath environments is proposed. During the first stage, TDOA of the signals received at pairs of sensors is estimated. In the second stage, the actual location is computed from the TDOA estimates. This later stage is referred to as hyperbolic localization and it generally involves a non-convex optimization. For the first stage, a TDOA estimation method that exploits the sparsity of multipath channels is proposed. This is formulated as an f1-regularization problem, where the f1-norm is used as channel sparsity constraint. For the second stage, three methods are proposed to offer high accuracy at different computational costs. The first method takes a semi-definite relaxation (SDR) approach to relax the hyperbolic localization to a convex optimization. The second method follows a linearized formulation of the problem and seeks a biased estimate of improved accuracy. A third method is proposed to exploit the source sparsity. With this, the hyperbolic localization is formulated as an an f1-regularization problem, where the f1-norm is used as source sparsity constraint. The proposed methods compare favorably to other existing methods, each of them having its own advantages. The SDR method has the advantage of simplicity and low computational cost. The second method may perform better than the SDR approach in some situations, but at the price of higher computational cost. The l1-regularization may outperform the first two methods, but is sensitive to the choice of a regularization parameter. The proposed two-stage localization approach is shown to deliver higher accuracy and robustness to noise, compared to existing TDOA localization methods. A single-stage source localization method is explored. The approach is coherent in the sense that, in addition to the TDOA information, it utilizes the relative carrier phases of the received signals among pairs of sensors. A location estimator is constructed based on a maximum likelihood metric. The potential of accuracy improvement by the coherent approach is shown through the Cramer Rao lower bound (CRB). However, the technique has to contend with high peak sidelobes in the localization metric, especially at low signal-to-noise ratio (SNR). Employing a small antenna array at each sensor is shown to lower the sidelobes level in the localization metric. Finally, the performance of time delay and amplitude estimation from samples of the received signal taken at rates lower than the conventional Nyquist rate is evaluated. To this end, a CRB is developed and its variation with system parameters is analyzed. It is shown that while with noiseless low rate sampling there is no estimation accuracy loss compared to Nyquist sampling, in the presence of additive noise the performance degrades significantly. However, increasing the low sampling rate by a small factor leads to significant performance improvement, especially for time delay estimation

Online Audio-Visual Multi-Source Tracking and Separation: A Labeled Random Finite Set Approach

The dissertation proposes an online solution for separating an unknown and time-varying number of moving sources using audio and visual data. The random finite set framework is used for the modeling and fusion of audio and visual data. This enables an online tracking algorithm to estimate the source positions and identities for each time point. With this information, a set of beamformers can be designed to separate each desired source and suppress the interfering sources

Sound Based Positioning

With a growing interest in non-GPS positioning, navigation, and timing (PNT), sound based positioning provides a precise way to locate both sound sources and microphones through audible signals of opportunity (SoOPs). Exploiting SoOPs allows for passive location estimation. But, attributing each signal to a specific source location when signals are simultaneously emitting proves problematic. Using an array of microphones, unique SoOPs are identified and located through steered response beamforming. Sound source signals are then isolated through time-frequency masking to provide clear reference stations by which to estimate the location of a separate microphone through time difference of arrival measurements. Results are shown for real data

Introduction to indoor networking concepts and challenges in LiFi

LiFi is networked, bidirectional wireless communication with light. It is used to connect fixed and mobile devices at very high data rates by harnessing the visible light and infrared spectrum. Combined, these spectral resources are 2600 times larger than the entire radio frequency (RF) spectrum. This paper provides the motivation behind why LiFi is a very timely technology, especially for 6th generation (6G) cellular communications. It discusses and reviews essential networking technologies, such as interference mitigation and hybrid LiFi/Wi-Fi networking topologies. We also consider the seamless integration of LiFi into existing wireless networks to form heterogeneous networks across the optical and RF domains and discuss implications and solutions in terms of load balancing. Finally, we provide the results of a real-world hybrid LiFi/Wi-Fi network deployment in a software defined networking testbed. In addition, results from a LiFi deployment in a school classroom are provided, which show that Wi-Fi network performance can be improved significantly by offloading traffic to the LiFi

Audio for Virtual, Augmented and Mixed Realities: Proceedings of ICSA 2019 ; 5th International Conference on Spatial Audio ; September 26th to 28th, 2019, Ilmenau, Germany

The ICSA 2019 focuses on a multidisciplinary bringing together of developers, scientists, users, and content creators of and for spatial audio systems and services. A special focus is on audio for so-called virtual, augmented, and mixed realities. The fields of ICSA 2019 are: - Development and scientific investigation of technical systems and services for spatial audio recording, processing and reproduction / - Creation of content for reproduction via spatial audio systems and services / - Use and application of spatial audio systems and content presentation services / - Media impact of content and spatial audio systems and services from the point of view of media science. The ICSA 2019 is organized by VDT and TU Ilmenau with support of Fraunhofer Institute for Digital Media Technology IDMT