Audio-based localization for ubiquitous sensor networks

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2005.Includes bibliographical references (p. 97-101).This research presents novel techniques for acoustic-source location for both actively triggered, and passively detected signals using pervasive, distributed networks of devices, and investigates the combination of existing resources available in personal electronics to build a digital sensing 'commons'. By connecting personal resources with those of the people nearby, tasks can be achieved, through distributed placement and statistical improvement, that a single device could not do alone. The utility and benefits of spatio-temporal acoustic sensing are presented, in the context of ubiquitous computing and machine listening history. An active audio self-localisation algorithm is described which is effective in distributed sensor networks even if only coarse temporal synchronisation can be established. Pseudo-noise 'chirps' are emitted and recorded at each of the nodes. Pair-wise distances are calculated by comparing the difference in the audio delays between the peaks measured in each recording. By removing dependence on fine grained temporal synchronisation it is hoped that this technique can be used concurrently across a wide range of devices to better leverage the existing audio sensing resources that surround us.(cont.) A passive acoustic source location estimation method is then derived which is suited to the microphone resources of network-connected heterogeneous devices containing asynchronous processors and uncalibrated sensors. Under these constraints position coordinates must be simultaneously determined for pairs of sounds and recorded at each microphone to form a chain of acoustic events. It is shown that an iterative, numerical least-squares estimator can be used. Initial position estimates of the source pair can be first found from the previous estimate in the chain and a closed-form least squares approach, improving the convergence rate of the second step. Implementations of these methods using the Smart Architectural Surfaces development platform are described and assessed. The viability of the active ranging technique is further demonstrated in a mixed-device ad-hoc sensor network case using existing off-the-shelf technology. Finally, drawing on human-centric onset detection as a means of discovering suitable sound features, to be passed between nodes for comparison, the extension of the source location algorithm beyond the use of pseudo-noise test sounds to enable the location of extraneous noises and acoustic streams is discussed for further study.Benjamin Christopher Dalton.S.M

A Moving Source Localization Method for Distributed Passive Sensor Using TDOA and FDOA Measurements

The conventional moving source localization methods are based on centralized sensors. This paper presents a moving source localization method for distributed passive sensors using TDOA and FDOA measurements. The novel method firstly uses the steepest descent algorithm to obtain a proper initial value of source position and velocity. Then, the coarse location estimation is obtained by maximum likelihood estimation (MLE). Finally, more accurate location estimation is achieved by subtracting theoretical bias, which is approximated by the actual bias using the estimated source location and noisy data measurement. Both theoretical analysis and simulations show that the theoretical bias always meets the actual bias when the noise level is small, and the proposed method can reduce the bias effectively while keeping the same root mean square error (RMSE) with the original MLE and Taylor-series method. Meanwhile, it is less sensitive to the initial guess and attains the CRLB under Gaussian TDOA and FDOA noise at a moderate noise level before the thresholding effect occurs

Antenna array calibration in wireless communications

Imperial Users onl

Interference Mitigation and Localization Based on Time-Frequency Analysis for Navigation Satellite Systems

Interference Mitigation and Localization Based on Time-Frequency Analysis for Navigation Satellite SystemsNowadays, the operation of global navigation satellite systems (GNSS) is imperative across a multitude of applications worldwide. The increasing reliance on accurate positioning and timing information has made more serious than ever the consequences of possible service outages in the satellite navigation systems. Among others, interference is regarded as the primary threat to their operation. Due the recent proliferation of portable interferers, notably jammers, it has now become common for GNSS receivers to endure simultaneous attacks from multiple sources of interference, which are likely spatially distributed and transmit different modulations. To the best knowledge of the author, the present dissertation is the first publication to investigate the use of the S-transform (ST) to devise countermeasures to interference. The original contributions in this context are mainly: • the formulation of a complexity-scalable ST implementable in real time as a bank of filters; • a method for characterizing and localizing multiple in-car jammers through interference snapshots that are collected by separate receivers and analysed with a clever use of the ST; • a preliminary assessment of novel methods for mitigating generic interference at the receiver end by means the ST and more computationally efficient variants of the transform. Besides GNSSs, the countermeasures to interference proposed are equivalently applicable to protect any direct-sequence spread spectrum (DS-SS) communication

Optimal methodologies for ultrasonic guided-wave based structural health monitoring

The assessment of structural integrity is a key issue for many industries due to its important implications in safety, maintenance cost reduction, and improved asset availability. In this context, structural health monitoring (SHM) systems using ultrasonic guided-waves are being explored for an efficient diagnosis of damage and prognosis of the remaining useful life of the monitored structure. Nonetheless, addressing this monitoring scenario is a challenge given the inherent complexities associated to each of the diagnosis steps, which encompass the optimal SHM design, the detection of damage, its localisation, and its identification. Among these complexities, uncertainties stemming from several sources such as equipment noise, manufacturing defects, and the lack of conclusive knowledge about wave propagation introduce a high variability in the response of the SHM system. The main objective of this thesis is to provide probabilistic Bayesian and fuzzy logic methodologies to manage global uncertainties for each step in the SHM process. The accuracy and reliability of an ultrasonic guided-wave based SHM system are dependent on the chosen number and location of sensors and actuators. A general framework for optimal sensor configuration based on value of information is proposed in this thesis, which trades-off information gain and cost. This approach optimally chooses the sensor position so that they render the largest information gain when inferring the damage location. The methodology is tested using different case studies in the context of ultrasonic guided waves and piezoelectric sensors. However, although this framework is mathematically rigorous, it is computationally expensive should the actuators be considered in the optimisation problem. To overcome this issue, a cost-benefit analysis is also proposed using both the Shannon's information entropy and a cost function associated to the number of sensors and actuators. The objective function is based on binary decision variables, which are relaxed into continuous variables, hence convexifying the objective function. This optimisation methodology is illustrated in several case studies considering plate-like structures with irregular geometries and different materials, providing a high computational efficiency. The first diagnosis stage requires a robust and computationally efficient damage detection approach in real-life engineering scenarios. To this end, a novel damage index for ultrasonic guided-wave measurements based on fuzzy-logic principles is proposed in this thesis. This approach assesses the time of flight mismatch between signals acquired in undamaged and non-pristine states using fuzzy sets for its evaluation. The robustness partially builds on the use of a large amount of signals stemming from two experimental procedures: the round robin configuration and the transmission beamforming technique. This new damage index is validated in several scenarios with sudden and progressive damage. Once a damage area has been detected, the next diagnosis stage requires a reliable damage localisation. To address this SHM step, a robust methodology is proposed based on two hierarchical levels: (1) a Bayesian time-frequency model class selection to obtain the time of flight of damage scattered waves; and (2) a Bayesian inverse problem of damage localisation that considers as input data the outcome of the first level. The effectiveness and robustness of the proposed methodology is illustrated using two cases studies with one and two areas of damage. Lastly, to provide a complete diagnosis of damage using ultrasonic guided-waves, the identification of damage needs to be addressed. A multi-level hybrid wave and finite element model-based Bayesian approach is proposed to identify the type of damage in composite beams based on posterior probabilities, hence accounting for different sources of uncertainty. In addition to the type of damage, this approach allows the inference of damage-related parameters and the damage location. A carbon fibre beam with two damage modes, i.e. a crack and a delamination, is used to illustrate the methodology

Optimal methodologies for ultrasonic guided-wave based structural health monitoring

The assessment of structural integrity is a key issue for many industries due to its important implications in safety, maintenance cost reduction, and improved asset availability. In this context, structural health monitoring (SHM) systems using ultrasonic guided-waves are being explored for an efficient diagnosis of damage and prognosis of the remaining useful life of the monitored structure. Nonetheless, addressing this monitoring scenario is a challenge given the inherent complexities associated to each of the diagnosis steps, which encompass the optimal SHM design, the detection of damage, its localisation, and its identification. Among these complexities, uncertainties stemming from several sources such as equipment noise, manufacturing defects, and the lack of conclusive knowledge about wave propagation introduce a high variability in the response of the SHM system. The main objective of this thesis is to provide probabilistic Bayesian and fuzzy logic methodologies to manage global uncertainties for each step in the SHM process. The accuracy and reliability of an ultrasonic guided-wave based SHM system are dependent on the chosen number and location of sensors and actuators. A general framework for optimal sensor configuration based on value of information is proposed in this thesis, which trades-off information gain and cost. This approach optimally chooses the sensor position so that they render the largest information gain when inferring the damage location. The methodology is tested using different case studies in the context of ultrasonic guided waves and piezoelectric sensors. However, although this framework is mathematically rigorous, it is computationally expensive should the actuators be considered in the optimisation problem. To overcome this issue, a cost-benefit analysis is also proposed using both the Shannon's information entropy and a cost function associated to the number of sensors and actuators. The objective function is based on binary decision variables, which are relaxed into continuous variables, hence convexifying the objective function. This optimisation methodology is illustrated in several case studies considering plate-like structures with irregular geometries and different materials, providing a high computational efficiency. The first diagnosis stage requires a robust and computationally efficient damage detection approach in real-life engineering scenarios. To this end, a novel damage index for ultrasonic guided-wave measurements based on fuzzy-logic principles is proposed in this thesis. This approach assesses the time of flight mismatch between signals acquired in undamaged and non-pristine states using fuzzy sets for its evaluation. The robustness partially builds on the use of a large amount of signals stemming from two experimental procedures: the round robin configuration and the transmission beamforming technique. This new damage index is validated in several scenarios with sudden and progressive damage. Once a damage area has been detected, the next diagnosis stage requires a reliable damage localisation. To address this SHM step, a robust methodology is proposed based on two hierarchical levels: (1) a Bayesian time-frequency model class selection to obtain the time of flight of damage scattered waves; and (2) a Bayesian inverse problem of damage localisation that considers as input data the outcome of the first level. The effectiveness and robustness of the proposed methodology is illustrated using two cases studies with one and two areas of damage. Lastly, to provide a complete diagnosis of damage using ultrasonic guided-waves, the identification of damage needs to be addressed. A multi-level hybrid wave and finite element model-based Bayesian approach is proposed to identify the type of damage in composite beams based on posterior probabilities, hence accounting for different sources of uncertainty. In addition to the type of damage, this approach allows the inference of damage-related parameters and the damage location. A carbon fibre beam with two damage modes, i.e. a crack and a delamination, is used to illustrate the methodology

Bimodal sound source tracking applied to road traffic monitoring

The constant increase of road traffic requires closer and closer road network monitoring. The awareness of traffic characteristics in real time as well as its historical trends, facilitates decision-making for flow regulation, triggering relief operations, ensuring the motorists’ safety and contribute to optimize transport infrastructures. Today, the heterogeneity of the available data makes their processing complex and expensive (multiple sensors with different technologies, placed in different locations, with their own data format, unsynchronized, etc.). This leads metrologists to develop “smarter” monitoring devices, i.e. capable of providing all the necessary data synchronized from a single measurement point, with no impact on the flow road itself and ideally without complex installation. This work contributes to achieve such an objective through the development of a passive, compact, non-intrusive, acoustic-based system composed of a microphone array with a few number of elements placed on the roadside. The proposed signal processing techniques enable vehicle detection, the estimation of their speed as well as the estimation of their wheelbase length as they pass by. Sound sources emitted by tyre/road interactions are localized using generalized cross-correlation functions between sensor pairs. These successive correlation measurements are filtered using a sequential Monte Carlo method (particle filter) enabling, on one hand, the simultaneous tracking of multiple vehicles (that follow or pass each other) and on the other hand, a discrimination between useful sound sources and interfering noises. This document focuses on two-axle road vehicles only. The two tyre/road interactions (front and rear) observed by a microphone array on the roadside are modeled as two stochastic, zero-mean and uncorrelated processes, spatially disjoint by the wheelbase length. This bimodal sound source model defines a specific particle filter, called bimodal particle filter, which is presented here. Compared to the classical (unimodal) particle filter, a better robustness for speed estimation is achieved especially in cases of harsh observation. Moreover the proposed algorithm enables the wheelbase length estimation through purely passive acoustic measurement. An innovative microphone array design methodology, based on a mathematical expression of the observation and the tracking methodology itself is also presented. The developed algorithms are validated and assessed through in-situ measurements. Estimates provided by the acoustical signal processing are compared with standard radar measurements and confronted to video monitoring images. Although presented in a purely road-related applied context, we feel that the developed methodologies can be, at least partly, applied to rail, aerial, underwater or industrial metrology

Three-Dimensional Geometry Inference of Convex and Non-Convex Rooms using Spatial Room Impulse Responses

This thesis presents research focused on the problem of geometry inference for both convex- and non-convex-shaped rooms, through the analysis of spatial room impulse responses. Current geometry inference methods are only applicable to convex-shaped rooms, requiring between 6--78 discretely spaced measurement positions, and are only accurate under certain conditions, such as a first-order reflection for each boundary being identifiable across all, or some subset of, these measurements. This thesis proposes that by using compact microphone arrays capable of capturing spatiotemporal information, boundary locations, and hence room shape for both convex and non-convex cases, can be inferred, using only a sufficient number of measurement positions to ensure each boundary has a first-order reflection attributable to, and identifiable in, at least one measurement. To support this, three research areas are explored. Firstly, the accuracy of direction-of-arrival estimation for reflections in binaural room impulse responses is explored, using a state-of-the-art methodology based on binaural model fronted neural networks. This establishes whether a two-microphone array can produce accurate enough direction-of-arrival estimates for geometry inference. Secondly, a spherical microphone array based spatiotemporal decomposition workflow for analysing reflections in room impulse responses is explored. This establishes that simultaneously arriving reflections can be individually detected, relaxing constraints on measurement positions. Finally, a geometry inference method applicable to both convex and more complex non-convex shaped rooms is proposed. Therefore, this research expands the possible scenarios in which geometry inference can be successfully applied at a level of accuracy comparable to existing work, through the use of commonly used compact microphone arrays. Based on these results, future improvements to this approach are presented and discussed in detail