Time Difference of Arrival

In this paper, we consider the localization of a source from quantized measurements of time of arrivals (TOA) or time difference of arrivals (TDOA). Applications include, as particular examples, acoustic source localization from a network of microphones under communication constraints, and the localization of a base station using a geolocalized mobile station using timing advance measurements. We use a maximum likelihood approach, based on an efficient implementation of the EM algorithm. Contrary to previously reported work, our technique takes into account not only the measurement noise, but also the presence of outliers (for example, non line of sight propagation) and the quantization. We illustrate our findings using simulated data and real field measurements.

Time difference of arrival (TDOA)-based methods usually achieve the most accurate source localization in passive systems. In these methods, one of the sensors is assigned as the reference and the TDOA of the others with respect to the reference is measured. The source location is estimated by processing these TDOAs. In this paper, a multireference maximum likelihood (ML)-Taylor algorithm is proposed in order to decrease the source localization error of the TDOA methods. In the proposed algorithm, TDOAs are measured assuming 2 different reference sensors, and then the ML objective function of this multireference TDOA algorithm is derived and solved using Taylor series linearization. The developed method is evaluated regarding a 2-dimensional space with some different but deterministic sensor locations. Monte Carlo simulation is used to evaluate the proposed solution. The simulation results show that the proposed method has better performance relative to the other traditional TDOA-based algorithms.

Localization of multiple targets is a challenging task due to immense complexity regarding data fusion received at the sensors. In this context, we propose an algorithm to solve the problem for an unknown number of emitters without prior knowledge to address the data fusion problem. The proposed technique combines the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurement data fusion which further uses the maximum likelihood of the measurements received at each sensor of the surveillance region. The measurement grids of the sensors are used to perform data association. The simulation results show that the proposed algorithm outperforms the multipass grid search and further effectively eliminated the ghost targets created due to the fusion of measurements received at each sensor. Moreover, the proposed algorithm reduces the computational complexity compared to other existing algorithms as it does not use repeated steps for convergence or any biological evolutions. Furthermore, the experimental testing of the proposed technique was executed successfully for tracking multiple targets in different scenarios passively.

The increasing exploitation of wireless sensor devices in Medical diagnostics and patient monitoring has attracted the researchers in the past few decades. Nowadays, the development of health care systems and infrastructure is one of the most demanding and convincing goals. The monitoring and supervision of patients which has been carried out are often executed manually. This paper provides patient monitoring, tracking and localization through wireless sensor networks. We proposed a novel hybrid localization method based on Time Difference of Arrival and Lagrangian multiplier to locate the position of sensor which provides patient localization for Health Care Support. This system performs the localization out of samples of Time Difference of Arrival, tracking and monitoring module based on accelerometer and low pass filter with oversampling algorithm. Also we have designed a hardware setup for monitoring moving target localization and tracking. The simulation results reveal that the proposed method achieved very high localization accuracy.

Time of arrival (TOA) based localization extracts the positions from the signal delay between senders and receivers in a network and puts high requirements on synchronization, in particular synchronization on the line-of-sight (LOS) signal. We propose an ultrasound based system with pure LOS synchronization and data transmission for sender identification. We use two carriers with 38 respectively 40 kHz in a baseband OFDM scheme and by comparison of the phase shift of both carriers we estimate with the Cramér-Rao lower bound a distance error between sender and receiver of 0.047 mm for a transmission range of 20 m. We transmit 8 bit in 1.5 ms where no intersymbol interferences are expected in most practical scenarios for robust localization.

We propose an ultrasound communication system designed for time difference of arrival (TDOA) based indoor localization. The concept involves an infrastructure of stationary and independent senders tracking mobile receivers. The main goal is pure line-of-sight (LOS) communication for correct localization. When ignoring the reception energy of multipaths the transmission range is reduced to 20 meters and we need more devices to cover the same area (0.03 devices/m 2 ). Thus, for cost-effectiveness and easy installation, we focus in the sender design on low power consumption for long battery or even energy independent operation. Moreover, we use the energy efficient π/4-DQPSK modulation technique to send 8 data bits in 3.5 ms. An identifier in each message along with the reception time can be used for TDOA localization. The frame synchronization error for a distance of 20 m at 3 dB SNR is 11.2 ns. Thus, for speed of sound the distance measurement error is 3.7 µm.

Traduction : Francis Bismans et Eulalia Damaso e phénomène des ondes longues a été discuté de longue date par les sciences économiques, sociales et politiques. Une telle notion repose sur l'hypothèse que le développement capitaliste suit une dynamique de fluctuations longues, qui constituent un élément inhérent à ce type de développement. Ces fluctuations ont fait jusqu'à présent l'objet d'intenses dis-L Revue française d'économie, n° 4/vol XXIV Revue française d'économie, n° 4/vol XXIV

Research in passive acoustic detection and localization of marine mammals has largely focused on isolated cases of single or few individuals. However, many marine mammals, such as dolphins, characteristically aggregate in large pods. In addition, dolphins vocalize frequently, emitting broadband clicks and frequency-modulated whistles, which, coupled with a large number of individuals, further complicates the detection and localization problem. Using acoustic data collected on a high-frequency horizontal line array deployed in the Southern California Offshore Range (SCORE), automated tracking of individual dolphins within large pods is achieved by exploiting time differences of arrival and frequency-domain beamforming. Visual observations are used to corroborate acoustic localization results and to describe dolphin vocalization and group behavior patterns.

Electromagnetic (EM) source localization has become a vital issue lately to improve civilian safety, increase the military security and mitigate the disaster effect. The localization is embedded in battle-field surveillance, traffic alert, emergency call 911 (E911), resource allocation and mitigation of disaster effect. The location of the emitter can be determined by bestowing its transmitted signal measured at an array of spatially separated receivers. Several methods have been developed for estimating the EM source location. These methods include Time of Arrival (TOA), Time Difference of Arrival (TDOA), Frequency Difference of arrival (FDOA), Angle of Arrival (AOA) and Received Signal Strength (RSS) of the transmitted emitter signal. Comparing all localization techniques, TDOA and FDOA localization techniques (hyperbolic) are one of the simplest and most cost effective. The sensors situated on an axis in two-dimensional scenario measuring the TDOA and FDOA of the emitting signal from a moving source can estimate its position and velocity from the intersection point of hyperbola, which is created from TDOA and FDOA non-linear equations set. However, the hyperbolas may not be intersected at a single point due to the non-linear localization equations set and measurement noise in wireless sensor network (WSN). It is therefore important to estimate a source position that minimizes its deviations from the actual position. In this paper, a hybrid method combined with maximum likelihood (ML) and genetic algorithm (GA) are proposed to determine the instantaneous position of the moving source by estimating the position and velocity based on hyperbolic techniques (TDOA and FDOA). Firstly ML is applied in the position and velocity localization data. Additionally, GA is implemented to acquire the globally best solution of localization parameters from non-linear equations set of ML solution. The results obtained confirmed that the proposed method achieved the theoretical lower bound for near to far-field with same and different velocity and different baseline of sensors in low to high Gaussian noise level. In this study, explicit solutions are provided by the proposed methods that are not achievable through the established methods in all cases.

Wireless source localization has found a number of applications in wireless sensor networks. In this work, we investigate source localization based on the practical time of arrival (TOA) measurement model. Unlike most existing works that transform TOA measurement into time differences before processing, we consider the original measurement model and investigate three methods for direct source localization. We also derive the Cramér-Rao lower bound (CRLB) under the TOA model and establish its connection with the CRLB under the more commonly used time-difference of arrival (TDOA) signal model. We present results that illustrate the performance advantage of source localization based on the original TOA model over the commonly used TDOA pre-processing.

In source localization, one estimates the location of a source using a variety of relative position information. Many algorithms use certain powers of distances to effect localization. In practice, exact distance measurement is not directly available and must be estimated from information such as received signal strength (RSS), time of arrival, or time difference of arrival. This letter considers bias and variance issues in estimating powers of distances from RSS affected by practical log-normal shadowing. We show that the underlying estimation problem is inefficient and that the maximum likelihood estimate yields a bias and a meansquare error (MSE) that both increase exponentially with the noise power. We then characterize the class of unbiased estimates and show that there is only one estimator in this class, but that its MSE also grows exponentially with the noise power. Finally, we provide the linear minimum mean-square error (MMSE) estimate and show that its bias and MSE are both bounded in the noise power.

Traditionally, Blind Speech Separation techniques are computationally expensive as they update the demixing matrix at every time frame index, making them impractical to use in many Real-Time applications. In this paper, a robust data driven twomicrophone sound source localization method is used as a criterion to reduce the computational complexity of the Independent Vector Analysis (IVA) Blind Speech Separation (BSS) method. IVA is used to separate convolutedly mixed speech and noise sources. The practical feasibility of the proposed method is proved by implementing it on a smartphone device to separate speech and noise in Real-World scenarios for Hearing-Aid applications. The experimental results with objective and subjective tests reveal the practical usability of the developed method in many real-world applications.

The LTE (Long-Term Evolution) eMBMS (evolved Multimedia Broadcast Multicast Service) technology employed in MBSFNs (Multicast Broadcast Single Frequency Networks) has been shown to be capable of considerably increasing the capacity of serving public safety users under group communication scenarios. However, due to its own limitations, a more fine-tuned scheduling approach is needed in order to fully utilize the strengths of eMBMS multicast. In this work, we first identify and analyze several overlooked challenges for scheduling multicast traffic in MBSFNs. Then we develop an effective and efficient dynamic scheduling algorithm for eMBMS multicast in time and frequency varying channel. The proposed algorithm leverages the advantages of both multicast and unicast schemes via user grouping; and through extensive simulations, is shown to be capable of significantly enhancing the mission critical performances under both best effort and guaranteed bit rate delivery models. We also show the resiliency of our proposed algorithm by applying it onto various network and user deployment scenarios. Our consistent results further prove that LTE eMBMS in MBSFN is a key solution in overcoming limitations in near future public safety networks.

Background: Recent improvements in fixed acoustic monitoring receivers allow the tracking of individual aquatic animals over long periods of time with regular fine-scale positions. The VEMCO Positioning System (VPS) is now widely used, but various methodological issues remain to be clarified. The aim of this study was to analyze the spatial distribution of the probability of location and the positioning error over the entire surface of a hydropower reservoir, prior to analyzing fish behavior. Findings: Filtering the data set by the horizontal position error (HPE) significantly reduced the positioning error. Retaining only the positions with an HPE less than 15 retained 79% of VPS positions and decreased the positioning error by 33% (mean = 3.3 m, SD = 3.3 m). A higher probability of location was observed inside than outside the receiver array (44% and 36%, respectively). Moreover, the positioning error significantly differed inside (n = 243, mean = 2.4 m, SD = 2.1 m) and outside (n = 253, mean = 4.2 m, SD = 4.0 m) the receiver array (P < 0.001). Finally, the lowest positioning errors were detected in the area with the highest receiver density. Conclusions: The VPS measures fish positioning in a reservoir, under suitable conditions, with satisfactory accuracy. We showed that the probability of location and the positioning error differed spatially in accordance with previous results in other conditions. Consequently, these analyses are recommended as a prerequisite to further spatial analyses using VPS-derived data.

In Source Separation research, "cocktail party problem" is a challenging problem that research into source separation aims to solve. Many attempts have been made to solve this complex problem. A logical approach would be to break down this complex problem into several smaller problems which are solved in different stages -each considering various aspects. In this paper, we are providing a robust solution to a part of the problem by localizing and tracking multiple moving speech sources in a room environment. Here we study the separation problem for unknown number of moving sources. The DUET-CBMeMBer method we outline is capable of estimating the number of sound sources as well as tracking and labelling them. This paper proposes a track management technique that identifies sound sources based on their trajectory as an extension to the DUET-CBMeMBer technique.

We propose an optical wireless indoor localization using light emitting diodes (LEDs) and demonstrate it via simulation. Unique frequency addresses are assigned to each LED lamp, and transmitted through the light radiated by the LED. Using the phase difference, time difference of arrival (TDOA) localization algorithm is employed. Because the proposed localization method used pre-installed LED ceiling lamps, no additional infrastructure for localization is required to install and therefore, inexpensive system can be realized. The performance of the proposed localization method is evaluated by computer simulation, and the indoor location accuracy is less than 1 cm in the space of 5m ⅹ 5 m ⅹ 3 m. 1

The precise position of the mobile station is critical for the ever increasing number of applications based on location . In this paper a new approach for mobile positioning in WCDMA (Wideband Code Division Multiple Access ) cellular network is proposed . Radio Received Signal Strength Indication (RSSI) can be measured from a number of nearby base stations . Three proposed approaches (3-BS's , 5-BS's and 7-BS's) are simulated using three propagation models and the rms error is calculated for each model. As a simulation study case, the University of Mosul area is choosed to find the position of a mobile user in the university campus .

This paper introduces a new method for single-channel denoising that sheds new light on classical early developments on this topic that occurred in the 70's and 80's with Wiener filtering and spectral subtraction. Operating both in the short-time Fourier transform domain, these methods consist in estimating the power spectral density (PSD) of the noise without speech. Then, the clean speech signal is obtained by manipulating the corrupted time-frequency bins thanks to these noise PSD estimates. Theoretically grounded when using power spectra, these methods were subsequently generalized to magnitude spectra, or shown to yield better performance by weighting the PSDs in the so-called parameterized Wiener filter. Both these strategies were long considered ad-hoc. To the best of our knowledge, while we recently proposed an interpretation of magnitude processing, there is still no theoretical result that would justify the better performance of parameterized Wiener filters. Here, we show how the α-stable probabilistic model for waveforms naturally leads to these weighted filters and we provide a grounded and fast algorithm to enhance corrupted audio that compares favorably with classical denoising methods.

In this paper we present an integrated system for multimodal 3D tracking of a performer during an artistic event. This system integrates two tracking algorithms: one audio and one optical. DEI-CSC, University of Padova, has developed the audio tracking algorithm. The optical tracking algorithm has been developed by DIST-InfoMus Lab University of Genova. The two algorithms are integrated using the EyesWeb software platform (www.eyesweb.org), which is responsible of the synchronisation and combination of data flow.

Dans le cadre du CND ultrasonore par capteurs multiéléments, on utilise de plus en plus couramment des simulations d'inspection pour mettre au point de nouveaux contrôles ou de nouveaux capteurs, pour qualifier des méthodes, pour interpréter des résultats complexes, ou pour la formation. Dans ce contexte, la demande de temps de calcul réduits et si possible interactifs est forte. Or aujourd'hui la puissance des architectures massivement parallèles (manycore et multicore sur architecturesGPP, general purpose processors, et GPU, graphic processing unit) permet de l'envisager. Au sein de la plateforme CIVA, a été développé un outils de simulation de champ interactive reposant sur un modèle de pinceaux basé sur une résolution numérique de formulations analytiques des trajets optique de la propagation de l'onde ultrasonore à travers la pièce inspectée. Ce poster présente, dans un premier temps, les modèles utilisés pour les simulations interactives, leur domaine d'application, ainsi que les spécificités de leur implémentation sur architectures GPP. Puis sont donnés quelques résultats de validité et performance sur différents cas d'applications industriels.