Time Difference Of Arrival Technique Research Articles

Partial discharge localization using time reversal: Application to gas insulated switchgear

• Single sensor partial discharge (PD) localization in gas insulated switchgear. • Localization of PD using electromagnetic the time reversal (EMTR) method. • Investigation of the effect of noise, PD length, location, and orientation. • Single sensor localization of multiple, simultaneous PD in gas insulated switchgear. Gas-insulated switchgear (GIS) are some of the most reliable and most expensive equipment that are nowadays used in power systems. Condition monitoring of the power system equipment can help operators detect defects like partial discharges (PDs) early, prevent severe damage to the insulation of the apparatus and the related costs. Different techniques such as time difference of arrival (TDoA) have already been implemented to localize PDs. The TDoA technique requires four synchronized sensors to find the 3D location, it does not yield accurate results in a realistic situation when having multiple PDs, noise, scattering or obstacles which mask the line of sight between the sensor(s) and the PD. To have a powerful and accurate technique that can localize single or multiple PDs in power transformers in the presence of noise, scattering, obstacles, etc., using a single sensor, the time reversal (TR) technique has recently been proposed in both the acoustic and electromagnetic regimes. In this paper, the electromagnetic time reversal (EMTR) technique is applied to localize PDs in GIS, which have a more complicated structure than a transformer. The performance of the method is demonstrated considering several realistic case studies involving two pipe-shape and T-shape parts of a GIS.

Modified Zhang neural algorithm for time-varying overdetermined linear models and applications

The challenges of the inefficiency of the existing static least-squares method and the insufficient precision of the existing neural algorithms need to be overcome. With the aid of the ten-instant Zhang time discretization formula, a novel ten-instant discrete-time neural network algorithm is proposed and generalized to the problem-solving of overdetermined system of time-varying linear equations. Theoretical analyses illustrate that the maximal steady-state residual error of the proposed algorithm has an order-6 pattern. Comparative experiment results further substantiate the superiority of the proposed algorithm. Moreover, applications of mobile localization based on the angle of arrival technique and the time difference of arrival technique are provided to display the applicability of the proposed algorithm.

The Complete Analytical Solution of the TDOA Localization Method

This article is focused on the analytical solution of a TDOA (Time Difference of Arrival) localization method, including analysis of accuracy and unambiguity of a target position estimation in 2D space. The method is processed under two conditions - sufficiently determined localization system and an overdetermined localization system. It is assumed that the TDOA localization system operates in a LOS (Line of Sight) situation and several time-synchronized sensors are placed arbitrarily across the area. The main contribution of the article is the complete description of the TDOA localization method in analytical form only. It means, this paper shows a geometric representation and an analytical solution of the TDOA localization technique model. In addition, analyses of unambiguity and solvability of the method algorithm are presented, together with accuracy analysis of this TDOA technique in analytical form. Finally, the description of this TDOA method is extended to an overdetermined TDOA system. This makes it possible to determine and subsequently optimize its computational complexity, for example increase its computational speed. It seems that such a description of the TDOA localization technique creates a simple and effective tool for technological implementation of this method into military localization systems.

Partial discharge localization in power transformers using acoustic time reversal

The localization of partial discharges (PD) is important to monitor the conditioning of high voltage insulator materials. One of the key components in power grids is the high power transformer. Localization of PDs in these pieces of equipment can increase the reliability of power networks. One of the most commonly used methods to localize PDs is the time difference of arrival (TDoA) technique. Recently, time reversal (TR) techniques in the electromagnetic regime have been proposed to localize PD sources with a lower number of needed sensors compared to the TDoA technique. Contrary to TDoA, TR-based techniques do not need line-of-sight wave propagation from PD sources to the sensor(s). In this study, we extend the TR technique to localize PD sources in the acoustic regime for three-dimensional structures. Similar to the electromagnetic TR methods, a typical time reversal process includes three steps: 1) record the acoustic fields caused by a PD source using at least one sensor, 2) time reverse and back inject into the medium the recorded acoustic signal(s), 3) use a proper criterion to obtain the focusing point associated with the location of the PD source. The acoustic TR technique presented in this paper uses only one sensor. Numerical simulations are used to evaluate the performance of the acoustic TR technique for three-dimensional power transformer models. To do this, an open source MATLAB toolbox is used to solve the acoustic wave equation. The numerical results show that the TR technique can accurately localize the PD sources in three-dimensional power transformer models. Our analysis shows that the presence of windings inside the transformer tank does not degrade the efficiency of the acoustic TR technique.

A New Hybrid Algorithm to Image Lightning Channels Combining the Time Difference of Arrival Technique and Electromagnetic Time Reversal Technique

Very-high-frequency (VHF) electromagnetic signals have been well used to image lightning channels with high temporal and spatial resolution due to their capability to penetrate clouds. A lightning broadband VHF interferometer with three VHF antennas configured in a scalene-triangle shape has been installed in Lhasa since 2019, to detect the lightning VHF signals. Using the signals from the VHF interferometer, a new hybrid algorithm, called the TDOA-EMTR technique, combining the time difference of arrival (TDOA) and the electromagnetic time reversal (EMTR) technique is introduced to image the two-dimensional lightning channels. The TDOA technique is firstly applied to calculate the initial solutions for the whole lightning flash. According to the results by the TDOA method, the domain used for the EMTR technique is predetermined, and then the EMTR technique is operated to obtain the final positioning result. Unlike the original EMTR technique, the low-power frequency points for each time window are removed based on the FFT spectrum. Metrics used to filter noise events are adjusted. Detailed imaging results of a negative cloud-to-ground (CG) lightning flash and an intra-cloud (IC) lightning flash by the TDOA method and the TDOA-EMTR are presented. Compared with the original EMTR method, the positioning efficiency can be improved by more than a factor of 3 to 4, depending on the scope of the pre-determined domain. Results show that the new algorithm can obtain much weaker radiation sources and simultaneously occurring sources, compared with the TDOA method.

Crack detection and localization in a fluid pipeline based on acoustic emission signals

This paper introduces a novel approach to crack detection and localization in a pipeline transporting fluid under high pressure. From acoustic emission signals acquired by two R15i-AST sensors at two ends of a fluid pipeline, the proposed method scans peaks in the individual signal channels in the time-frequency domain and filters out noise to obtain acoustic emission events. Subsequently, adjacent events are combined into grouped events, and these are picked and paired together on two sensor channels to localize emission sources using the time difference of arrival technique. To improve the location accuracy, the mechanism only determines the arrival time of Rayleigh waves with a similar frequency in event pairs. Furthermore, the Rayleigh wave velocity is calibrated by a pencil lead breaking procedure. Additionally, false emission sources are eliminated by considering the wave energy attenuation characteristics in their propagation path. After locating the emission sources, the approach observes their distribution according to the position and time of occurrence. The variation in acoustic emission activity against applied load, which is established by counting the returned sources, can indicate irregular structural changes in a material. The location of the structural change can be surmised by the emission source distribution and density according to the position along the pipeline. Experimental results show that the proposed method correctly diagnoses faults in the considered pipeline from acoustic emission signals, whereas a conventional approach (performed by detecting hits with a threshold) inaccurately localizes acoustic emission sources and imprecisely exposes signs of abnormal structural transformations.

Positioning of radio emission sources with unmanned aerial vehicles using TDOA-AOA measurement processing

Positioning of radio emission sources (RES) with ground and flying network segments exploits interaction of sensors aboard Unmanned Aerial Vehicles (UAVs) with terrestrial stationary ground sensors. Time Difference of Arrival (TDOA) and Angle of Arrival (AOA) measurements processing are the most wide-spread techniques for passive geolocation systems. Successful implementation of TDOA-AOA positioning in existing UAV based geolocation systems achieves accuracy results of tens and hundreds of meters for overoptimistic scenarios without reflections in so called Line of Sight (LOS) conditions. Prevailing application specific cases for UAV based positioning include search and rescue (S&R) operations in mountains, hills and other regions of irregular surface topography with Non-Line of Sight (NLOS) conditions, where radio waves could experience severe reflection and diffraction. For geolocation tasks NLOS problem usually leads to significant errors for both TDOA and AOA measurement processing techniques. Joint processing of TDOA and AOA primary measurements could partially overcome this problem by preliminary identification and exclusion of reflected signals. In current research we refine existing mathematical model and develop new simulation models to investigate NLOS tolerance during positioning with one stationary ground sensor and one sensor aboard flying UAV using joint TDOA-AOA measurement processing technique. The contribution of current research is performance evaluation of considered positioning system with AOA noise and confirmation of its practicability to handle NLOS problem, when AOA deviation is less than 10 degrees.

Improvements in signal processing for the estimation of environmental parameters with machine learning using a compact tetrahedral array and sources of opportunity

In previous work, we showed that we could localize sound sources using a compact tetrahedral hydrophone array in a continental shelf environment south of Block Island, Rhode Island. The tetrahedral array of phones, 0.5 m on a side, was deployed to monitor the construction and operation of the first offshore wind farm in the United States. Directions of arrival (DOAs) for a number of ships were computed using a time difference of arrival technique. Given the DOAs, ranges are estimated using supervised machine learning techniques. We extended that work to estimate a number of environmental parameters including water depth and sediment composition. Here, we report on results using new spectrogram processing techniques based on high resolution PE modeling. These results include inversions for sediment parameters with estimates of error. With this new higher resolution spectrogram processing, we also report on the impact of the sediment parameters on range estimation. We generalize the technique to generic continental shelf environments. [Work supported by the Office of Naval Research.]

A Novel Ranging Technique Based on Optical Camera Communications and Time Difference of Arrival

In this work, a new Time Difference of Arrival (TDoA) scheme for distance measurement based on Optical Camera Communication (OCC) systems is proposed. It relies on the use of optical pulses instead of radio-frequency signals as the time reference triggers, and the introduction of a rolling shutter camera, whose characteristics allows substituting the timer modules used in conventional TDoA techniques by image processing of the illuminated area in the picture. This processing on the camera’s images provides time measurements and implies and specific analysis, which is presented in this work. The system performance and properties, such as resolution and range, mainly depends on the camera characteristics, such as the frames capture rate and the image quality. This new technique is suitable to be implemented in smartphones or other Commercial Off-The-Shelf (COTS) devices equipped with a camera and speakers.

Joint Exploitation of TDOA and PCL Techniques for Two-Dimensional Target Localization

This paper is focused on noncooperative target position estimation via the joint use of two-dimensional (2-D) hyperbolic and elliptic passive location techniques based on time difference of arrival (TDOA) and passive coherent locator (PCL) measurements, respectively. A fusion strategy is laid down at the signal processing level to obtain a reliable estimate of the current target position. With reference to the scenario with a single transmitter of opportunity, the mathematical model for joint exploitation of TDOA and PCL strategies is formulated. Then, the Cramer–Rao lower bound (CRLB) for the Cartesian coordinates of the target is established and the theoretical performance gains achievable over the localization technique using only TDOA or PCL observations are assessed. Finally, TDOA-PCL hybrid 2-D localization algorithms are provided and their performance in terms of root-mean-square error is compared with the square root of the CRLB.

Estimation of environmental parameters with machine learning using a compact tetrahedral array and sources of opportunity

In a previous paper, we showed that we could localize sound sources using a compact tetrahedral hydrophone array in a continental shelf environment south of Block Island, Rhode Island. The tetrahedral array of phones, 0.5 m on a side, was deployed to monitor the construction and operation of the first offshore wind farm in the United States. Directions of arrival (DOAs) for a number of ships were computed using a time difference of arrival technique. Given the DOAs, ranges are estimated using supervised machine learning techniques. Here, we extend this work to estimate a number of environmental parameters including water depth and sediment composition. Training sets of range-dependent ocean waveguides and sediment sound speeds were generated using a propagation model for a neural network. Data from the tetrahedral array were processed by the neural network, which provided estimates of the water depth and sediment parameters such as sound speed and density. These estimates are compared to bathymetric data and core data collected as part of the site characterization for the wind farm. [Work supported by the Office of Naval Research and the Bureau of Ocean Energy Management.]

Optimal Sensor Formation for 3D Cooperative Localization of AUVs Using Time Difference of Arrival (TDOA) Method.

The cooperative localization of submerged autonomous underwater vehicles (AUVs) using the Time Difference of Arrival (TDOA) measurements of surface AUV sensors is an effective method for many applications of AUVs. Proper positioning of the sensors to maximize the observability of the AUVs is very critical for cooperative localization. In this paper, a novel method for obtaining the optimal formation of sensor AUVs has been presented for the three-dimensional (3D) cooperative localization of targets using the TDOA technique. An evaluation function for estimating the optimal formation has been derived based on Fisher Information Matrix (FIM) theory for a single target as well as multiple-target cooperative localization systems. An iterative stepping algorithm has been followed to solve the evaluation function and obtain the optimal positions of the sensors. The algorithm ensured that the computation complexity should remain limited, even when the number of sensor AUVs is increased. Various simulation examples are then presented to calculate the optimal formation for different systems/situations. The effect of the position of the reference sensor and operating depth of the target AUVs on the optimal formation of the sensors has also been studied, and conclusions are drawn. For implementation of the proposed method for more practical scenarios, a simulation example is also presented for cases when the target’s position is only known with uncertainty.

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.

Source localization using a compact tetrahedral array

We localized sound sources collected on a compact tetrahedral hydrophone array in a continental shelf environment south of Block Island, Rhode Island. The tetrahedral array of phones, 0.5 m on a side, was deployed to monitor the underwater sound of construction and operation of the first offshore wind farm in the United States. Signals from shipping and marine mammals, including fin whales, humpback whales, and right whales, were detected on the array. Directions of arrival (DOAs) for a number of signals were computed using a time difference of arrival technique. Given the DOAs, ranges were estimated using supervised machine learning techniques outlined by Niu et al. (JASA, 2017). The approach was tested using simulated data from Kraken assuming environmental information consistent with this continental shelf environment. Performance on signals from individual ships and marine mammals is presented. Ship localizations are compared to Automated Identification System (AIS) fixes. An error analysis is also presented. [Work supported by the Office of Naval Research and the Bureau of Ocean Energy Management.]

A TDOA Technique with Super-Resolution Based on the Volume Cross-Correlation Function

Time Difference of Arrival (TDOA) is widely used in wireless localization systems. Among the enormous approaches of TDOA, high resolution TDOA algorithms have drawn much attention for its ability to resolve closely spaced signal delays in multipath environment. However, the state-of-art high resolution TDOA algorithms still have performance weakness on resolving time delays in a wireless channel with dense multipath effect, as well as difficulties in implementation for their high computation complexity. In this paper, we propose a novel TDOA algorithm with super resolution based on a multi-dimensional cross-correlation function: the Volume Cross-Correlation Function (VCC). The proposed TDOA algorithm has excellent time resolution capability in multipath environment, and it also has a much lower computational complexity. Because our algorithm does not require priori knowledge about the waveform or power spectrum of transmitted signals, it has great potential of usage in various passive wireless localization systems. Numerical simulations is also provided to demonstrate the validity of our conclusion.

Low‐power ultra‐wide band pulse generator based on a PIN diode

Radio-frequency identification (RFID) tags (passive kind) are typically not designed to include ultra-wide band (UWB) pulse generators, but may be in the future. The reason is that the very short pulses produced by the RFID tags could be used to estimate their positions with errors of solely a few centimetres using time difference of arrival techniques. Currently, the method most commonly used to estimate tag positions relies on received signal strengths (less accurate). An UWB pulse generator based on a PIN diode is presented in this study. This pulse generator was designed to produce a positive unipolar pulse for each high-to-low transition (1.8 V to 0) it receives at its input. This trigger signal is generated by a microntroller-based battery-free RFID tag. The end result is a pulse with an amplitude of ∼300 mV and a duration of slightly <2 ns measured using a 50 Ω load.

Highly accurate 3D wireless indoor positioning system using white LED lights

A wireless indoor positioning system using white LED lights is proposed. The time difference of arrival technique is employed and the phase differences between the received signals are determined to develop a positioning algorithm which can estimate the receiver location with a mean localisation error as low as 1 mm in a room of dimensions 5 × 5 × 3 m. Through simulations, it is identified that the optimum receiver height where localisation error gets minimised is between 2.5 and 3 m from the ceiling which corresponds well with the typical dimensions of a room.

ALO4: Angle Localization and Orientation System with Four Receivers

This paper presents a 2D indoor localization and orientation system based on a TDOA (Time Difference of Arrival) technique. It uses an array of receivers (four low-cost ultrasonic resonant devices in a square distribution) to implement low-computational-effort DOA (Direction of Arrival) algorithms, based on assumed plane-wave reception. The system only demands two transmitters at well-known positions on the ceiling of the room for obtaining the node position and orientation when it is deployed on the floor of the room. This system has been tested using a Xilinx Spartan-3A FPGA that implements a 52 MHz MicroBlaze. The experimental results include a total of 1,440 points, obtaining a mean localization error of 5.17 cm and a mean orientation error of 3.34°. For this system, the localization and orientation processes are executed in less than 50 us.

Lightning VHF radiation location system based on short-baseline TDOA technique — Validation in rocket-triggered lightning

A lightning VHF radiation location system based on short-baseline time-difference of arrival (TDOA) technology is newly developed. Based on the orthogonal 10m-baseline antenna array with four identical broadband flat plane antennas, this system receives the lightning broadband VHF radiation signals and calculates TDOA between antennas in order to determine the location of lightning radiation sources in two dimensions (elevation and azimuth). To reduce noise and improve estimation accuracy of time delay, a general correlation time delay estimation algorithm based on direct correlation method and wavelet transform is proposed. Moreover, parabolic interpolation algorithm is used in the fractional delay estimation to improve the time resolution of the positioning system. In this paper, a rocket-triggered lightning discharge and a cloud lightning discharge are analyzed respectively, combining with simultaneous observations of high-speed camera and fast/slow electric field changes. The results indicate that the TDOA location system could effectively map the lightning radiation sources in 2 dimensions and are in a good agreement with the high-speed video camera images.

Mobile station positioning using time difference of arrival and received signal strength

In this paper, a new Mobile Station Positioning (MSP) technique is proposed and its performance is evaluated. The proposed technique combines the Received Signal Strength (RSS) and the Time Difference of Arrival (TDOA) techniques. Each RSS measurement yields a circle on which the Mobile Station (MS) may lie. Meanwhile, each TDOA measurement defines a hyperbola on which the MS may reside. The proposed hybrid TDOA/RSS technique uses Taylor series expansion to solve the hyperbolas and circles specified by the distance measurements obtained by the Base Stations (BSs). The MS position is determined based on Linear Least Squares (LLS) method in an iterative fashion. Without hardware modification, the proposed technique reduces location errors compared with either technique separately. Simulation results demonstrate superior performance over both TDOA and RSS techniques.