Time Difference Of Arrival Information Research Articles

UHF Sensors Based Localization of Partial Discharge Sources in Air-Insulated Electrical Substation Using TDOA and Relative RSS Information

Time-difference of arrival (TDOA) based localization of partial discharge (PD) sources using Ultra High Frequency (UHF) technique often provides uncertain accuracy because of unavoidable errors in TDOA estimation between PD pulses. This paper presents a novel localization approach utilizing TDOA and relative received signal strength (RRSS) information together for improving the localization accuracy of PD sources within air-insulated substations (AIS). A complete PD localization system governed by this approach has been developed using multiple UHF PD sensors. The effectiveness of the proposed PD source localization approach has been validated through laboratory and field-based experiments in real-life AIS.

Dir-MUSIC Algorithm for DOA Estimation of Partial Discharge Based on Signal Strength Represented by Antenna Gain Array Manifold.

Inspection robots are widely used in the field of smart grid monitoring in substations, and partial discharge (PD) is an important sign of the insulation state of equipment. PD direction of arrival (DOA) algorithms using conventional beam forming and time difference of arrival (TDOA) require large-scale antenna arrays and high computational complexity, making them difficult to implement on inspection robots. To address this problem, a novel directional multiple signal classification (Dir-MUSIC) algorithm for PD direction finding based on signal strength is proposed, and a miniaturized directional spiral antenna circular array is designed in this paper. First, the Dir-MUSIC algorithm is derived based on the array manifold characteristics. This method uses strength intensity information rather than the TDOA information, which could reduce the computational difficulty and the requirement of array size. Second, the effects of signal-to-noise ratio (SNR) and array manifold error on the performance of the algorithm are discussed through simulations in detail. Then, according to the positioning requirements, the antenna array and its arrangement are developed and optimized. Simulation results suggested that the algorithm has reliable direction-finding performance in the form of six elements. Finally, the effectiveness of the algorithm is tested by using the designed spiral circular array in real scenarios. The experimental results show that the PD direction-finding error is , which meets the need for partial discharge DOA estimation using inspection robots in substations.

Method for determining of statistical characteristics of evaluated distance obtained from measurements of Doppler frequency offsets

This paper presents a technique for obtaining of the main statistical characteristics of the distance estimation between the flying object and a hyperbolic navigation system using two Doppler frequency shifts of the navigation signal. This problem arises when the guidance of the object at a target point along a hyperbola using a bistatic hyperbolic navigation system is considered. Despite the fact that the object guidance loop is closed according to the time difference of arrival information, it is necessary to enter the distance between the object and the center of the navigation system into the algorithm for generating control commands in order to stabilize the frequency response of the control loop and eliminate dynamic errors. This article discusses the statistical characteristics of the estimated range based on the measurements of three values of the object’s speed: its own speed and two projections of this speed on the lines connecting the object with the navigation positions.

High-Precision Underwater Acoustical Localization of the Black Box Based on an Improved TDOA Algorithm

Underwater acoustic localization (UWAL) of the black box is a significant technique for transportation safety. The traditional UWAL algorithm based on the time difference of arrival (TDOA) suffers from large localization error due to the signal period drift in the scenario of high pressure and low temperature under sea. To solve this problem, an improved TDOA algorithm is proposed in this letter. We define the second-order time difference of arrival, which eliminates the drifted signal period. Utilizing the second-order time difference of arrival information, we establish the equations to solve the position of the black box. Precision analysis is presented to theoretically calculate the localization precision of the proposed algorithm and test the robustness with respect to the input errors. Experiments in different scenes validate the effectiveness of the algorithm.

Time Difference of Arrival Estimation Based on a Kronecker Product Decomposition

Time difference of arrival (TDOA) estimation, which often serves as the fundamental step for a source localization or a beamforming system, has a significant practical importance in a wide spectrum of applications. To deal with reverberation, the TDOA estimation problem is often transformed into one of identifying the relative acoustic impulse responses. This letter presents a method to efficiently identify the relative acoustic impulse response between two microphones for TDOA estimation based on the so-called Kronecker product decomposition. By decomposing the relative impulse response into a series of Kronecker products of shorter filters, the original channel identification problem with a long impulse response is converted into one of identifying a number of short filters. Since the TDOA information is embedded only in the direct path of the relative impulse response, the dimension of the Kronecker product decomposition can be very small and, as a result, the developed algorithm is expected to work well in real environments with a small number of data snapshots.

Noise power spectral density scaled SNR response estimation with restricted range search for sound source localisation using unmanned aerial vehicles

A method to locate sound sources using an audio recording system mounted on an unmanned aerial vehicle (UAV) is proposed. The method introduces extension algorithms to apply on top of a baseline approach, which performs localisation by estimating the peak signal-to-noise ratio (SNR) response in the time-frequency and angular spectra with the time difference of arrival information. The proposed extensions include a noise reduction and a post-processing algorithm to address the challenges in a UAV setting. The noise reduction algorithm reduces influences of UAV rotor noise on localisation performance, by scaling the SNR response using power spectral density of the UAV rotor noise, estimated using a denoising autoencoder. For the source tracking problem, an angular spectral range restricted peak search and link post-processing algorithm is also proposed to filter out incorrect location estimates along the localisation path. Experimental results show the proposed extensions yielded improvements in locating the target sound source correctly, with a 0.0064–0.175 decrease in mean haversine distance error across various UAV operating scenarios. The proposed method also shows a reduction in unexpected location estimations, with a 0.0037–0.185 decrease in the 0.75 quartile haversine distance error.

A modified cross-correlation algorithm to achieve the time difference of arrival in sound source localization

The accuracy in ranging and direction in locating a target source is crucial in sound source localization and different methods have been proposed due to important applications of sound source localization. One of the methods in sound source localization is triangulation with the time difference of arrival information. In this literature, a modified cross-correlation algorithm is introduced to increase the accuracy in time difference of arrival, thus further improving the sound source localization results. A numerical model is generated by assuming multiple sound sources broadcasting in room environment and the location of the target sound source is identified with the triangulation algorithm. Real-time data are produced through experimental setup using an array of four microphones with a target source and background noise. The signals are processed by modified cross-correlation and conventional cross-correlation for comparison. The impacts of the signal-to-noise ratio and time difference of arrival on sound source localization results are demonstrated and discussed. Experimental validation conducted in a non-ideal environment has shown that the modified cross-correlation algorithm can minimize the error in time difference of arrival to be used in sound source localization, thus improving the accuracy in both sound source ranging and direction.

The Location of Partial Discharge Sources Inside Power Transformers Based on TDOA Database With UHF Sensors

This paper proposes a new method to locate the partial discharge (PD) sources inside the high-voltage power transformers with ultra-high frequency (UHF) sensors, based on the time difference of arrival (TDOA) database. A TDOA database includes the TDOA information of possible PD sources inside a power transformer, which is established under the construction of the system of the tested power transformer and the high accuracy of the minimum distance between a possible PD source and a UHF sensor. We employ the tangent transmission model of PD signals and the shortest path algorithm to solve the distance problem in case of an obstacle obstructing the transmission of PD signals. In order to verify the feasibility and accuracy of this new TDOA database method, we carry out two experiments. One experiment was conducted with a 110kV power transformer and the other was carried out with a platform for PD of electric power equipment. The experimental research results which are composed of location coordinates, transmission path and TDOA information indicate that the TDOA database method can locate single PD source as well as multiple PD sources simultaneously with the location error less than 0.3m.

A novel time difference of arrival localization algorithm using a neural network ensemble model

In a localization system, time difference of arrival technique is widely used to estimate the location of a mobile station. To improve the performance of mobile station location estimation, a novel algorithm-based artificial neural network ensemble and time difference of arrival information is proposed in non-line-of-sight environments. Back propagation neural network is a classic artificial neural network and may be effectively used for mathematical modeling and prediction, and an artificial neural network ensemble has better generalization ability and stability than a single network. First, the parameters, such as the weights and biases of the single neural network are optimized by the ant lion optimization method which is novel and effective. Then four types of different information from the time difference of arrival measurements are respectively used to train the individual neural network. Finally, the weighted average method is improved to combine the outputs of the different individual neural network, where weights are determined by the training errors. The estimation accuracy of the locating system is evaluated through experimental measurements. The simulation results show that the proposed algorithm is efficient in improving the generalization ability and localization precision of the neural network ensemble model.

Sensitivity-based region selection in the steered response power algorithm

Abstract The steered response power (SRP) algorithm is a well-studied method for acoustic source localization using a microphone array. Recently, different improvements based on the accumulation of all time difference of arrival (TDOA) information have been proposed in order to achieve spatial resolution scalability of the grid search map and reduce the computational cost. However, the TDOA information distribution is not uniform with respect to the search grid, as it depends on the geometry of the array, the sampling frequency, and the spatial resolution. In this paper, we propose a sensitivity-based region selection SRP (R-SRP) algorithm that exploits the nonuniform TDOA information accumulation on the search grid. First, high and low sensitivity regions of the search space are identified using an array sensitivity estimation procedure; then, through the formulation of a peak-to-peak ratio (PPR) measuring the peak energy distribution in the two regions, the source is classified to belong to a high or to a low sensitivity region, and this information is used to design an ad hoc weighting function of the acoustic power map on which the grid search is performed. Simulated and real experiments show that the proposed method improves the localization performance in comparison to the state-of-the-art.

Robust Target Tracking with Multi-Static Sensors under Insufficient TDOA Information.

This paper focuses on underwater target tracking based on a multi-static sonar network composed of passive sonobuoys and an active ping. In the multi-static sonar network, the location of the target can be estimated using TDOA (Time Difference of Arrival) measurements. However, since the sensor network may obtain insufficient and inaccurate TDOA measurements due to ambient noise and other harsh underwater conditions, target tracking performance can be significantly degraded. We propose a robust target tracking algorithm designed to operate in such a scenario. First, track management with track splitting is applied to reduce performance degradation caused by insufficient measurements. Second, a target location is estimated by a fusion of multiple TDOA measurements using a Gaussian Mixture Model (GMM). In addition, the target trajectory is refined by conducting a stack-based data association method based on multiple-frames measurements in order to more accurately estimate target trajectory. The effectiveness of the proposed method is verified through simulations.

Exploiting a geometrically sampled grid in the steered response power algorithm for localization improvement.

The steered response power phase transform (SRP-PHAT) is a beamformer method very attractive in acoustic localization applications due to its robustness in reverberant environments. This paper presents a spatial grid design procedure, called the geometrically sampled grid (GSG), which aims at computing the spatial grid by taking into account the discrete sampling of time difference of arrival (TDOA) functions and the desired spatial resolution. A SRP-PHAT localization algorithm based on the GSG method is also introduced. The proposed method exploits the intersections of the discrete hyperboloids representing the TDOA information domain of the sensor array, and projects the whole TDOA information on the space search grid. The GSG method thus allows one to design the sampled spatial grid which represents the best search grid for a given sensor array, it allows one to perform a sensitivity analysis of the array and to characterize its spatial localization accuracy, and it may assist the system designer in the reconfiguration of the array. Experimental results using both simulated data and real recordings show that the localization accuracy is substantially improved both for high and for low spatial resolution, and that it is closely related to the proposed power response sensitivity measure.

A multi-hypothesis tracker for clicking whales.

This paper describes a tracker specially designed to track clicking beaked whales using widely spaced bottom-mounted hydrophones, although it can be adapted to different species and sensors. The input to the tracker is a sequence of static localization solutions obtained using time difference of arrival information at widely spaced hydrophones. To effectively handle input localizations with high ambiguity, the tracker is based on multi-hypothesis tracker concepts, so it considers all potential association hypotheses and keeps a large number of potential tracks in memory. The method is demonstrated on actual data and shown to successfully track multiple beaked whales at depth.

생체모방 알고리즘을 이용한 OFDMA 무선 메쉬 통신시스템의 분산 시간 동기화 기법 연구

본 논문에서는 최근 차세대 무선 통신네트워크 기술로 주목받고 있는 OFDMA 무선 메쉬통신시스템에서의 분산 시간동기화 기법을 제안한다. 제안방식은 물리계층 기반에서 이루어지는 생체모방 알고리즘을 활용한 자가 분산 시간동기화 기법으로, 인접 이웃노드로부터 수신된 신호의 TDoA(Time Difference of Arrival) 정보를 이용하여 다음 주기의 전송주기와 FFT 시작점을 업데이트한다. 분산처리가 가능한 생체모방 알고리즘의 장점이 반영되어, 제안된 동기화 기법에서도 중앙제어 없이 주위 정보(local Information)와 각 노드에 의한 분산처리만으로 빠르고 간단하게 노드 간 시간 동기 획득이 가능하다. 제안된 분산 시간 동기화 기법은 모의실험을 통하여 시간동기화 수렴 확률 및 정확도 등의 측면에서 성능을 분석한다. In this paper, we propose a distributed time synchronization technique for OFDMA- based wireless mesh communication systems. The proposed technique employs the bio-inspired algorithm for self-time synchronization in the physical layer. To achieve the network synchronicity, the proposed technique updates the transmission time and the FFT window using the TDoA information of received signals from the neighbor nodes. The proposed technique can achieve fast self-synchronization among nodes with a simple algorithm, without the need of a centralized controller. The performance of the proposed time synchronization technique is evaluated by simulation in terms of convergence speed and convergence probability.

음향 신호를 이용한 수중로봇의 위치추정

This paper proposes particle filter(PF) method using acoustic signal for localization of an underwater robot. The method uses time of arrival(TOA) or time difference of arrival(TDOA) of acoustic signals from beacons whose locations are known. An experiment in towing tank uses TOA information. Simulation uses TDOA information and it reveals dependency of the localization performance on the uncertainty of robot motion and senor data. Also, comparison of the PF method with the least squares method of spherical interpolation(SI) and spherical intersection(SX) is provided. Since PF uses TOA or TDOA which comes from measurement of external information as well as internal motion information, its estimation is more accurate and robust to the sensor and motion uncertainty than the least squares methods.

Auditory inspired methods for localization of multiple concurrent speakers

The use of microphone arrays offers enhancements of speech signals recorded in meeting rooms and office spaces. A common solution for speech enhancement in realistic environments with ambient noise and multi-path propagation is the application of so-called beamforming techniques. Such beamforming algorithms enhance signals at the desired angle using constructive interference while attenuating signals coming from other directions by destructive interference. However, these techniques require as a priori the time difference of arrival information of the source. Therefore, the source localization and tracking algorithms are an integral part of such a system. The conventional localization algorithms deteriorate in realistic scenarios with multiple concurrent speakers. In contrast to conventional methods, the techniques presented in this paper make use of pitch information of speech signals in addition to the location information. This “position–pitch”-based algorithm pre-processes the speech signals by a multiband gammatone filterbank that is inspired from the auditory model of the human inner ear. The role of this gammatone filterbank is analyzed and discussed in details. For a robust localization of multiple concurrent speakers, a frequency-selective criterion is explored that is based on a study of the human neural system's use of correlations between adjacent sub-band frequencies. This frequency-selective criterion leads to improved localization performance. To further improve localization accuracy, an algorithm based on grouping of spectro-temporal regions formed by pitch cues is presented. All proposed speaker localization algorithms are tested using a multichannel database where multiple concurrent speakers are active. The real-world recordings were made with a 24-channel uniform circular microphone array using loudspeakers and human speakers under various acoustic environments including moving concurrent speaker scenarios. The proposed techniques produced a localization performance that was significantly better than the state-of-the-art baseline in the scenarios tested.

Tracking calling depths and movements of North Atlantic right whales using multipath localization

The track and calling depths of a North Atlantic right whale (NARW) recorded by 10 bottom-mounted Autonomous Acoustic Recording Units (ARUs) in the Stellwagen Bank National Marine Sanctuary was determined using the Direct Reflected Time Difference of Arrival (DRTD) localization method. An autocorrelation technique was used to extract direct-reflected time difference of arrival information from recorded NARW up-calls containing several overlapping multipath signal arrivals. The method’s feasibility was tested using data from play back transmissions to localize an acoustic transducer at a known depth and location. The method was then used to track an hour of movements and depths of a single NARW using periodic up-calls for localization purposes.

Acoustic Reflection Localization from Room Impulse Responses

The localization of acoustic reflections, i.e., the image-sources, is of interest when analyzing the acoustics of concert halls and auditoriums. The location is needed, for example, in room acoustic studies, auralization, inference of room geometry, or when estimating the acoustic properties of surfaces. This article studies the localization of acoustic reflections from spatial impulse responses. The contribution of this article is threefold. First, the article proposes a new method for localization that takes advantage of the time of arrival (TOA) estimation. Secondly, it is proposed that TOA and time diff erence of arrival (TDOA) information, present in the spatial room impulse responses, are combined in two novel ways. Thirdly, the performance of the proposed localization methods is compared to the existing state-of-the-art localization methods in the acoustic reflection localization task. Theoretical performance is investigated and experiments using real and simulated data are conducted. The TOA-based methods are found to achieve reasonably good performance in the reflection localization task. When TOA and TDOA information is combined the performance clearly improves.

Passive Range-Difference Estimation in a Dispersive Medium

Passive source localization can be achieved by utilizing the range-difference (RD) measurements between the source and several spatially separated sensors. When the signal propagation medium is nondispersive, the RD is typically obtained from time-difference-of-arrival (TDOA) between sensor outputs. However, for a dispersive medium which corresponds to in-solid localization, the TDOA information is not reliable. In this correspondence, two RD estimation algorithms for dispersive medium are derived and analyzed, assuming that the dispersion curve is available. Computer simulations are included to contrast the estimation performance of the two methods with the Cramer-Rao lower bound (CRLB) for different conditions.

Discrimination of near-field infrasound sources based on time-difference of arrival information

A computationally efficient method for discriminating between near- and far-field infrasound sources using array time-difference of arrival (TDOA) information is described. Rather than assess wave-front curvature, the discriminant quantifies the statistical departure of TDOA information from that of a plane wave passing the array. Since the method constrains neither the functional form nor the amplitude characteristics of a signal it is suited for discrimination of signals across large-aperture infrasound arrays. Experimental results confirm theoretical predictions to a range of order ten array apertures. The discriminant is applied to data from an Antarctic infrasound array.