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Due to the influence of the errors of time difference of arrivals (TDOA) and the premeasured velocity in the propagation medium, the existing methods always hold the low location accuracy and efficiency. To increase the location accuracy, the joint inversion of AE/MS sources and propagation velocity is proposed using the full measurements and residual estimation. First, the control equation of the unknown wave velocity is constructed using sensor coordinates and full TDOA measurements, and it is linearized by introducing the additive variables. Second, considering the influence of TDOA errors, the residuals of the TDOA equations are calculated to obtain weights. Finally, the orthogonal projection is applied to separate the additive variables and solve the weighted linear equations for source coordinates. The pencil–lead breaks experiments are carried out to verify the effectiveness and accuracy of the proposed method. The results show that the novel method holds better accuracy and wave velocity inversion than the pre-existing methods. The average positioning error of the novel method is only 3.49 mm, while the errors of the traditional methods are 4.62 mm, 5.08 mm, 6.57 mm, and 11.93 mm, respectively. The positioning performance of the proposed method under different TDOA errors is further proved by simulation tests. The simulation results demonstrate that the positioning performance of the proposed method is better than that of the existing methods under different errors. Moreover, the blast vibration experiment validates that the proposed method holds the location accuracy of 16.65 m, which presents good prospects in engineering applications. Highlights No prior measurement of wave speeds and selection of reference sensors reduce the effect of velocity and arrival errors. Analytical solution being given by linearizing the control equation, complex computations and local convergence are circumvented. Estimating the noise term in the arrival time difference, the tolerance to TDOA errors is improved. Redundant time-difference-of-arrival measurements are fully used to provide new constraints. Blasting experiments reveal that positioning accuracy of the method is 72% more accurate than the traditional method. [ABSTRACT FROM AUTHOR]

In passive localization, the time-difference-of-arrival (TDOA) measurement model is commonly used for source location estimation. Methods for TDOA-based estimation can be categorized into two main groups: closed-form algebraic solutions and iterative approaches. Algebraic solutions circumvent convergence issues and achieve global optima, but are usually sensitive to TDOA measurement inaccuracies. Iterative methods optimize the objective function through multiple iterations, including deterministic iterative methods that require an iterative initial value and stochastic optimization methods reliant on optimization algorithms. In this paper, a stochastic optimization algorithm named snake optimization (SO) is used to solve the TDOA localization problem and improved to meet the localization requirements. Initially, a chaotic system is utilized to generate three random sequences to establish the initial snake population. The search strategy in the exploration phase is subsequently improved to enhance the early-stage convergence speed of the algorithm. Moreover, an adaptive evolutionary stage threshold is introduced to adaptively handle various noise conditions and source locations in TDOA localization scenarios. Lastly, a snake oviposition strategy, inspired by genetic principles, is proposed. Simulations show that the improved SO algorithm can converge to the source position quickly and stably and has better positioning accuracy. [ABSTRACT FROM AUTHOR]

This paper introduces a Triangle Network‐Joint Slope (TN‐JS) approach to characterize the spatial and temporal dynamics of Equatorial Plasma Bubbles (EPBs) during geomagnetic storms. To collaboratively determine the EPB drift directions from multiple stations, a Delaunay triangle network is constructed, utilizing the distribution of Ionospheric Piercing Points (IPPs). The Time Difference of Arrival (TDOA) is extracted through cross‐correlating the Rate of Total Electron Content (ROT). The EPB drift direction can be approximately calculated by considering TDOA and IPP distances within each individual triangle of the network. This calculation is then refined through a joint statistical analysis. Using a reference station as the origin, the remaining stations within the network are projected along the estimated EPB drift direction. A spatial‐temporal color map illustrating regional ionospheric anomaly ROT observations is constructed. The EPB drift velocity among multiple stations can be collectively estimated by fitting the slope of this map, facilitating outlier exclusion. Accounting for satellite dynamic effects and the diverse orbit characteristics of GPS and BDS, corresponding IPP scan velocity compensation is performed and analyzed for EPB dynamic estimation. Using the geomagnetic storm event that occurred on September 8 as a case study, the spatial‐temporal kinetic properties of EPBs is characterized by analyzing Global Navigation Satellite System (GNSS) observations from 17 Hong Kong monitoring stations with the proposed TN‐JS approach. The results indicate during this magnetic event, that EPBs exhibit a westward drift trend with velocities ranging from a few tens to hundreds of meters per second in GPS and BDS observations. Plain Language Summary: Total Electron Content (TEC) is a path integrated electron density and its rate (ROT) of change reflect the ionospheric disturbance during magnetic storms. This article introduces a new method called Triangle Network‐Joint Slope (TN‐JS) to study the movement of Equatorial Plasma Bubbles (EPBs). TN‐JS uses a network of GNSS monitoring stations to determine the drift velocity of EPBs. By resampling ROT correlation using triangulation along the drift direction, TN‐JS transforms traditional EPB dynamic estimation into image processing of the color‐coded ROT maps. The TN‐JS algorithm is tested with data collected from 17 monitoring stations around Hong Kong during a geomagnetic storm on 8 September 2017 to show EPBs drifting westward at speeds ranging from tens to hundreds of meters per second. Key Points: A Delauny Triangle Network is built for statistically inferring EPB drift velocity by cross‐correlating and slope fitting multi‐sites' ROTThe orbit diversity offered by GPS MEO and BDS GEO/IGSO satellites provides measures of ionospheric irregularities inhomogeneityThe analysis unveiled a significant EPB westward drift event with a speed exceeding 500 m/s during the 2017 geomagnetic storm over Hong Kong [ABSTRACT FROM AUTHOR]

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Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To investigate the effectiveness of intelligent optimization algorithms in indoortime difference of arrival (TDOA) localization. Firstly, six intelligent optimization algorithms, namelywhite shark optimizer (WSO), chameleon swarm algorithm(CSA), snake optimizer (SO), whale optimization algorithm (WOA), grey wolf optimizer (GWO), sparrow search algorithm (SSA), were used for two-dimensional indoor TDOA localization. A comparative analysis of these algorithms was conducted in the indoor localization domain, and their performance was compared with the traditional Taylor algorithm in terms of localization error. Subsequently, the SOA algorithm was employed to optimize a back propagation (BP) neural network, and the optimized SOA-BP was used for localization. A comparison was made between the localization error of SOA-BP and the basic BP neural network. The results show that the six intelligent optimization algorithms use exhibit promising performance in indoor localization, with similar effects. The average localization error for each intelligent optimization algorithm is 0. 44 m, representing an improvement of about 9. 2% compared to the traditional Taylor algorithm. Furthermore, the localization error of SOA-BP is reduced by more than 30% when compared to the basic BP neural network. [ABSTRACT FROM AUTHOR]

High-precision position awareness is essential to ubiquitous wireless networks, which can provide real-time position information and abundant status information for numerous practical applications. However, It is a challenge to obtain accurate position estimation utilizing traditional onefold parameter estimation, especially for the accurate position estimation of moving radiation source in the presence of receiver position errors. In this work, we developed an Improved Taylor Series estimation method in three-dimensional positioning scene, in which time difference of arrival (TDOA) and frequency difference of arrival (FDOA) are used to estimate the position and velocity of the target, and the position of the receiver is iteratively updated to reduce the influence of the receiver position errors. The closed-form expressions of Cramer–Rao low bound (CRLB) based on joint TDOA and FDOA positioning with receiver position errors are derived. In the simulation, CRLB with and without receiver position errors are evaluated to illustrate the influence of the receiver position errors on the positioning performance. Theory analysis and simulation results show that the proposed algorithm has lower complexity, smaller RMSE and better positioning performance than multidimensional scaling (W-MDS) algorithm, constrained total least squares algorithm and two-step weighted least squares algorithm for both near-field and far-field emitters. [ABSTRACT FROM AUTHOR]

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Reconfigurable intelligent surfaces (RISs) are anticipated to constitute a critical component of forthcoming communication networks due to their ability to establish controllable wireless environments. Furthermore, RIS can be leveraged to solve infeasible localization problems. This paper presents a novel adoption of the geometric dilution of precision (GDoP) analysis in the design of non-line of sight (NLoS) single anchor millimetre wave (mmWave) large RIS-aided localization in the near-field. Considering downlink transmission, the time difference of arrival (TDoA) is used to estimate the positioning by extracting the signal contribution of each RIS tile. Considering the enriched time of arrival (ToA) measurements provided by RIS, the RIS tiles involved in TDoA-based estimation should be selected properly. Therefore, the GDoP adopted in this context is to select RIS tile combinations that achieve minimum GDoP values. It has been shown via numerical simulation that the proposed design of the RIS-aided localization outperforms other state-of-the-art techniques that adopt the signal-to-noise ratio (SNR) for the selection of the RIS tiles. It is demonstrated that the proposed scheme can provide sufficient localization accuracy involving only 10% of RIS tiles, whereas the SNR method requires about (70–80) % of the tiles to approximately achieve the same accuracy. [ABSTRACT FROM AUTHOR]

ABSTRACTReconfigurable intelligent surfaces (RISs) are anticipated to constitute a critical component of forthcoming communication networks due to their ability to establish controllable wireless environments. Furthermore, RIS can be leveraged to solve infeasible localization problems. This paper presents a novel adoption of the geometric dilution of precision (GDoP) analysis in the design of non-line of sight (NLoS) single anchor millimetre wave (mmWave) large RIS-aided localization in the near-field. Considering downlink transmission, the time difference of arrival (TDoA) is used to estimate the positioning by extracting the signal contribution of each RIS tile. Considering the enriched time of arrival (ToA) measurements provided by RIS, the RIS tiles involved in TDoA-based estimation should be selected properly. Therefore, the GDoP adopted in this context is to select RIS tile combinations that achieve minimum GDoP values. It has been shown via numerical simulation that the proposed design of the RIS-aided localization outperforms other state-of-the-art techniques that adopt the signal-to-noise ratio (SNR) for the selection of the RIS tiles. It is demonstrated that the proposed scheme can provide sufficient localization accuracy involving only 10% of RIS tiles, whereas the SNR method requires about (70–80) % of the tiles to approximately achieve the same accuracy.

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Localizing a moving source by Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) commonly requires at least N + 1 sensors in N-dimensional space to obtain more than N pairs of TDOAs and FDOAs, thereby establishing more than 2 N equations to solve for 2 N unknowns. However, if there are insufficient sensors, the localization problem will become underdetermined, leading to non-unique solutions or inaccuracies in the minimum norm solution. This paper proposes a localization method using TDOAs and FDOAs while incorporating the motion model. The motion between the source and sensors increases the equivalent length of the baseline, thereby improving observability even when using the minimum number of sensors. The problem is formulated as a Maximum Likelihood Estimation (MLE) and solved through Gauss–Newton (GN) iteration. Since GN requires an initialization close to the true value, the MLE is transformed into a semidefinite programming problem using Semidefinite Relaxation (SDR) technology, while SDR results in a suboptimal estimate, it is sufficient as an initialization to guarantee the convergence of GN iteration. The proposed method is analytically shown to reach the Cramér–Rao Lower Bound (CRLB) accuracy under mild noise conditions. Simulation results confirm that it achieves CRLB-level performance when the number of sensors is lower than N + 1 , thereby corroborating the theoretical analysis. [ABSTRACT FROM AUTHOR]

In unmanned aerial vehicle (UAV)-based time difference of arrival (TDOA) positioning technique, baselines are limited due to communication constraints. In this case, the accuracy is highly sensitive to the TDOA measurements' error. This article primarily addresses the problem of short-baseline high-precision time synchronization and TDOA measurement. We conducted a detailed analysis of error models in TDOA systems, considering both the time and phase measurement. We utilize the frequency division wireless phase synchronization technique in TDOA systems. Building upon this synchronization scheme, we propose a novel time delay estimation method that relies on phase measurements based on the integer least squares method. The performance of this method is demonstrated through Monte Carlo simulations and outdoor experiments. The standard deviations of synchronization and TDOA measurements in experiments are 1.12 ps and 1.66 ps, respectively. Furthermore, the circular error probable (CEP) accuracy is improved from 0.33%R to 0.02%R, offering support for the practical application of distributed short-baseline high-precision passive location techniques. [ABSTRACT FROM AUTHOR]

In order to support the development of more efficient spectrum management by using Big Data and Artificial Intelligence (AI), the authors study and propose a methodological framework that allows the application of Big Data and AI into spectrum management. The authors benchmark how spectrum regulators across the world are currently applying Big Data and AI technologies into their spectrum management, together with advantage(s) and disadvantage(s) of each of the approaches. The authors analyze the current status of the spectrum management under Thailand’s Office of the National Broadcasting and Telecommunications Commission (NBTC). Moreover, the authors identify gaps that might exist between the current status and the aimed future in which Big Data and AI technologies could be applied, and how to close the gaps so that the more efficient spectrum management could be achieved. Based on these studies and analyses, the authors propose a framework and a prototype of a web application applying Big Data and AI Platform to support the mission on spectrum management of the Office of the NBTC, Thailand.

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the inherent defect of single jammer against synthetic aperture radar (SAR) system with ground moving target indication (GMTI), this defect is usually solved by using the idea of dual-jammer cooperative jamming. However, the jamming methods of dual-jammer cooperative jamming in the current public literature need to reconnoitre some key parameters of SAR-GMTI system in advance, such as the carrier platform speed, the nearest slant distance between the jammer and the carrier platform, and the self-position of the two jammers. The acquisition of these parameters usually requires complex reconnaissance equipment. To solve this problem, an implementation method of dual-jammer system against SAR-GMTI based on integration of reconnaissance and jamming is proposed. This method uses the layout of jammers and receivers as well as the time difference of arrival (TDOA) information between receivers to effectively solve the problem of reconnaissance key parameters of complex equipment, and simplifies the configuration of jamming system. The results show that, considering the measurement error of TDOA and the spacing error of two antennas in SAR-GMTI system, the proposed method can also generate false moving targets with controllable velocity and azimuth position. Theoretical analysis and simulation results verify the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

This article presents a pulse radio transmission system operating in the millimeter-wave regime for simultaneous binary information transmission and location in an intrabuilding environment. In particular, the location solution mutualizes the hardware architectures of three sensors of known position to extract the location information of a sensor entering their vicinity and, thus, uses the radio communication signals to extract this location information. Experimental validation considering the 60-GHz transceiver designed and realized on pseudomorphic high electron mobility transistor (pHEMT) technology is exemplary shown with localization error quantified to 16 cm.

Sound source localization remains a critical task for increasing sufficiently the location accuracy and typically decreasing the computational complexity of a real-time passive or active target tracking by multiple-microphone in topology-based concurrent sensor arrays for far- or near-filed domains. This paper properly presents a passive real-time localization method scientifically based on the time difference of arrival signal in reliably estimating the arrival angles for meaningfully improving the location accuracy and typically decreasing the prediction time of a stationary source in a three-dimensional (3D) model for an outdoor environment in free-field conditions. The proposed scalable distributed method significantly increases the localization accuracy by integrating local direction-finding information of two concurrent parallel microphone arrays and naturally deriving a specific piece of global location-finding information to sufficiently reduce the predictions' run-time. The designing approach accurately represents a two-step process to properly implement a suitable decomposition method and gently apply an interpolation technique in the near-filed ideal domain. The primary goal of our innovative design realistically is to seemingly indicate the key role of hardware architecture and popularly use suitable techniques in complexity reduction and accuracy improvement of the intelligent model for future scalable localization systems to progressively increase outstanding performance. The empirical experiments using different real and simulated datasets show the modern TDOA-SSL method ordinarily has a low-time 360 msec in 3D accurately estimating the prime location with an average error of 12.07 cm in the near-field and 296 cm in the far-field. In key addition, the proposed method invariably is robust to ambient noise and sound reflection in the near- and far-field. However, the average accuracy of the proposed system efficiently is 99.43% with an error factor of 0.19% for the near-field and 99.70% with an error factor of 0.016% for the far-field for the used range of 100 m and 1 km, respectively. Performance evaluation of the proposed method sufficiently shows the real-time prediction and the reasonable accuracy of the specific prediction positively enhances by carefully applying the geometric architectural specification. [ABSTRACT FROM AUTHOR]

Distributed radar systems promise to significantly enhance target localization by virtue of the superiority of multi-view observations from widely separated radars, compared to their monostatic counterparts. Nevertheless, when the radar number is limited, performing target localization bears the brunt of the parameter identifiability requirement that the parameter number must be no less than the number of independent measurements. In this way, the canonical two-stage target localization method, as well as its developments, is no longer appropriate for direct application. Hence, in this paper, we propose a novel target localization method using time-difference-of-arrival (TDOA) measurements with the minimum number of radars under platform position uncertainties. The referred distributed system is a bistatic multi-receiver system, where the primary signal is transmitted by a geostationary Earth orbit (GEO) satellite while receivers are equipped on several unmanned aerial vehicles (UAVs). In the first stage, the reference range from the reference radar to the target is estimated by a quadratic function, and then the weighted least squares (WLS) solution of the target location is updated by substituting the range estimate back into it. In the second stage, we invoke the Taylor series approximation to further refine the target localization obtained by the first stage. It can be foreseen that the developed method is beneficial for scenarios with a limited number of radars, including engineering projects such as fire control, surveillance, and guidance, to support high-accuracy target localization. The simulation results show the superiority of the localization performance of the proposed method over other existing methods. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) target localization by using hybrid time difference of arrival (TDOA) and angle of arrival (AOA) measurements from multiple sensors has been an active research area for several decades due to its extensive applications in various fields. For this nonlinear estimation problem, the pseudolinear system of equations constructed by using the measurements generally acts as the basis of numerous localization algorithms. In this paper, we aim to improve the performance of 3D TDOA–AOA localization by introducing the constrained total least squares (CTLS) framework wherein the inherent characteristics of the pseudolinear equations can be properly taken into consideration. On the basis of the total least squares model, the CTLS model for 3D TDOA–AOA localization is established by imposing the inherent characteristics of the pseudolinear equations as additional constraints. Then, the multi-constraint optimization problem in CTLS model is solved by using an iterative algorithm based on successive projections. Extensive numerical simulations are accomplished for evaluating the performance of the proposed CTLS algorithm. The results show that the proposed algorithm gives moderate accuracy enhancement with acceptable computational cost, and more importantly, it is more robust to large measurement noise than the compared algorithms. [ABSTRACT FROM AUTHOR]

Localizing a moving source by Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) commonly requires at least N+1 sensors in N-dimensional space to obtain more than N pairs of TDOAs and FDOAs, thereby establishing more than 2N equations to solve for 2N unknowns. However, if there are insufficient sensors, the localization problem will become underdetermined, leading to non-unique solutions or inaccuracies in the minimum norm solution. This paper proposes a localization method using TDOAs and FDOAs while incorporating the motion model. The motion between the source and sensors increases the equivalent length of the baseline, thereby improving observability even when using the minimum number of sensors. The problem is formulated as a Maximum Likelihood Estimation (MLE) and solved through Gauss–Newton (GN) iteration. Since GN requires an initialization close to the true value, the MLE is transformed into a semidefinite programming problem using Semidefinite Relaxation (SDR) technology, while SDR results in a suboptimal estimate, it is sufficient as an initialization to guarantee the convergence of GN iteration. The proposed method is analytically shown to reach the Cramér–Rao Lower Bound (CRLB) accuracy under mild noise conditions. Simulation results confirm that it achieves CRLB-level performance when the number of sensors is lower than N+1, thereby corroborating the theoretical analysis.

In wireless communication, it is a challenge to find the accurate position of moving objects. The presented work proposes the GPS receiver to measure the position and velocity of moving objects. These measurements are dynamic, the Least Squares (LS) technique is used to linearize the measurement for further processing. Afterward, the time difference of arrival (TDOA) mythology is applied. The obtained data is then processed through a Kalman filter to mitigate non-line-of-sight errors and smoothen the range values. The Kalman filter applies standard deviation on received data and performs an NLOS/LOS hypothesis test. By processing the received data, the algorithm generates readings that mitigate the NLOS error and reduces position error. The simulations demonstrate that the proposed algorithm reduced the noise by 28.64% and 34.4% in LOS and NLOS regions respectively. These findings indicate that the accuracy of object tracking was significantly improved as compared to other algorithms while also being less computationally intensive and cost efficient.

In recent years, sound source localization, as a passive localization technique with higher safety and convenience compared with other localization techniques such as active emission of electromagnetic waves, has received more and more attention in academia. This paper researches and improves the far-field sound source localization algorithm based on the generalized cross-correlation method (GCC) Time Difference of Arrival (TDOA) estimation algorithm and completes the design and implementation of the microphone array sound source localization system. This paper adds a frequency divider to the traditional generalized correlation time delay estimation algorithm for pre-processing, sampling, and localization of sound source acoustic waves and adopts a low-cost microphone array deployment scheme as far as possible to improve the flexibility and practicality of the localization system; at the same time, the "Minimum Sphere Method" is used at the back end of the algorithm to classify the localization coordinates at different frequencies and, finally, output reasonable sound source coordinates. In the back-end of the algorithm, the "Minimum Sphere Method" is used to classify the localization coordinates at different frequencies and, finally, output the reasonable sound source coordinates. The experimental results show that the sound source localization system designed in this paper has good performance in terms of localization accuracy and cost-effectiveness and overcomes the failure of the generalized mutual correlation algorithm in the original application of high noise environment and multi-source environment localization. [ABSTRACT FROM AUTHOR]

Underwater acoustic localization (UWAL) is extremely challenging due to the multipath nature of extreme underwater environments, the sensor position uncertainty caused by unpredictable ocean currents, and the lack of underwater observation data due to sparse array, which all affect localization performance. Addressing these issues, this paper proposes a simple and effective underwater acoustic localization method using the time difference of arrival (TDOA) measurements based on the multipath channel effect of the underwater environment. By introducing the calibration source, localization performance was improved, and the sensor position error was corrected. The Cramér–Rao lower bound (CRLB) was derived, and the proposed method was able to achieve the CRLB with small deviation. Numerical simulations confirm the improved performance of the proposed method, including (1) a 20 dB and 30 dB reduction in the CRLB for far and near source scenarios, respectively, indicating improved accuracy and reliability when estimating unknown sources; (2) better Mean Squared Error (MSE) performance compared to existing methods and an efficiency of over 90% in low noise and above 80% in moderate noise in several scenarios, with a delayed threshold effect; and (3) achieving CRLB performance with only three sensors in a 3D space, even under moderate noise, while existing methods require at least five sensors for comparable performance. Our results demonstrate the efficacy of the proposed method in enhancing the accuracy and efficiency of source localization. [ABSTRACT FROM AUTHOR]

High-capacity impulse-radio ultra-wideband (IR-UWB) indoor localization systems are typically based on the time difference of arrival (TDoA) principle. When the fixed and synchronized localization infrastructure, the anchors, transmit precisely timestamped messages, a virtually unlimited number of user receivers (tags) are able to estimate their position based on differences in the time of arrival of those messages. However, the drift of the tag clock causes systematic errors at a sufficiently high magnitude to effectively deny the positioning, if left uncorrected. Previously, the extended Kalman filter (EKF) has been used to track and compensate for the clock drift. In this article, the utilization of a carrier frequency offset (CFO) measurement for suppressing the clock-drift related error in anchor-to-tag positioning is presented and compared to the filtered solution. The CFO is readily available in the coherent UWB transceivers, such as Decawave DW1000. It is inherently related to the clock drift, since both carrier and timestamping frequencies are derived from the identical reference oscillator. The experimental evaluation shows that the CFO-aided solution performs worse than the EKF-based solution in terms of accuracy. Nonetheless, with CFO-aiding it is possible to obtain a solution based on measurements from a single epoch, which is favorable especially for power-constrained applications. [ABSTRACT FROM AUTHOR]

To improve the estimation accuracy, a novel time delay estimation (TDE) method based on the closed-form offset compensation is proposed. Firstly, we use the generalized cross-correlation with phase transform (GCC-PHAT) method to obtain the initial TDE. Secondly, a signal model using normalized cross spectrum is established, and the noise subspace is extracted by eigenvalue decomposition (EVD) of covariance matrix. Using the orthogonal relation between the steering vector and the noise subspace, the first-order Taylor expansion is carried out on the steering vector reconstructed by the initial TDE. Finally, the offsets are compensated via simple least squares (LS). Compared to other state-of-the-art methods, the proposed method significantly reduces the computational complexity and achieves better estimation performance. Experiments on both simulation and real-world data verify the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Nodes' aware-mobility in the Internet of Things (IoTs) stills open defy for researchers, due to the dynamic changing of routing path and networks’ resource limitations. Therefore, in this study a new method is proposed called Mobility Aware - “Routing Protocol for Low power and Lossy Networks” (MARPL), that consists of two phases: in the first phase splitting the entire network into sub areas based on reference nodes with “Time Difference of Arrival” (TDoA) technique. While, the second phase, is about managing mobile nodes (MNs) in RPL according to the sub areas' ID. The Cooja simulator software has been used to implement and assess MA-RPL method performance, according to the data packet metrics (lost packet, packet delivery ratio PDR), latency and nodes' power usage in comparison with two methods: Corona (Co-RPL) and Mobility Enhanced (ME-RPL). The simulation results have been shown that the MA-RPL method consumes less nodes' energy usage, gives less latency with minimum data packet loss in comparison with Co-RPL and MERPL.

In the conventional lightning location system (LLS) based on time difference of arrival (TDOA), the nonlinear equations in lightning location calculation easily get to be divergent when the time information acquired from electromagnetic sensors is redundant. The LLS setup in lightning-sensitive regions in China usually experiences a development from detecting the thunderclap signal to the electromagnetic signal, such as the LLSs in oil tank farms. Therefore, an improved TDOA lightning location approach was proposed considering the acoustic and electromagnetic information emitted from lightning discharges. The targeted lightning monitoring region was divided into 16 sub-regions according to the location of the existing detection stations. The lightning location was calculated based on the Levenberg-Marquardt (L-M) iterative algorithm, which improves the lightning location accuracy and the resistance ability to measuring errors. The results show that the average error of the traditional lightning location method is 203.2 m. In contrast, the proposed approach can reduce the lightning location error to 108.4 m by considering the acoustic information and L-M iteration algorithm. The location accuracy at the edge of the targeted area is improved by 51.2%. This research can be potentially counseled in the improvement of existing LLSs and making an effective use of acoustic information.

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Frontiers of Information Technology & Electronic Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to the inhomogeneity of the atmosphere, radio waves are subjected to refraction during the propagation process, which reduces its propagation speed and bends the propagation path. If the influence is not considered, a large error will be caused by using time difference of arrival (TDOA). The influence of atmospheric refraction on the TDOA localization under the known elevation constraint is analyzed, a subsection iterative method is proposed to correct the localization error caused by atmospheric refraction, the Cramer-Rao lower bound (CRLB) for location estimation under atmospheric refraction is deduced. The effectiveness of the proposed method is validated through simulation results and analysis. [ABSTRACT FROM AUTHOR]

Automatic Dependent Surveillance—Broadcast (ADS-B) is an emerging means of aeronautical surveillance for air traffic control. Aircraft periodically broadcast positional updates to ground stations. Although ADS-B outperforms traditional radars in terms of accuracy and update rate, positional verification—a technique used to check the validity of the position report—is necessary to counter anomalies. In this study, two different methods were compared when the ground stations measure time difference of arrival (TDOA). One is direct; the test statistic is essentially the difference between the measurement and a prediction calculated from the position report. Another method is multilateration (MLAT)-based and two-step; the emitter position is firstly estimated, whereupon the difference between the estimated and reported positions constitutes the test statistic. As a result of the comparison, a performance difference, which depending on the number of receivers, was revealed. This is an useful suggestion for implementing ADS-B when the existing multilateration infrastructure is exploited.

Localization of radio-frequency (RF) transmitters using time difference of arrival (TDOA)-based methods is one of the conventional passive techniques that admits noncooperative source position finding, but suffers from challenging requirements such as precise intersensor synchronization and high-throughput transmission data links. A tradeoff governing the TDOA systems is in the sensor placement configuration. The more distance the sensors are placed, the more accurate localization is carried out, while the cost for the synchronization and data link increases, at the same time. In this work, a novel reconfigurable intelligent surface (RIS)-aided localization system is proposed that enjoys precise location accuracy while reducing the required expenses. The study reveals that the new setup, utilizing the beam-scanning property of the RIS sensors, improves the localization algorithm and outperforms the conventional approach. Also, comparisons with Cramér Rao lower bound (CRLB) are provided, which confirms the efficiency of the proposed method. Several numerical examples indicate the effectiveness of the proposed method, especially in a low signal-to-noise ratio (SNR) regime. [ABSTRACT FROM AUTHOR]

Achieving accurate localization of partial discharge (PD) sources is of great importance to ensure the long-term safe operation of electrical equipment. To improve the accuracy of PD source localization, the adaptive inertial weight bacterial foraging localization algorithm (AIW-BFA) was proposed based on the principle of time difference of arrival (TDOA) method, which adaptively adjusts the search step size and performs global optimization seeking through repulsion, replication and dispersal. Since electromagnetic wave propagation inside the transformer will be reflected, refracted and diffracted, which makes the localization of PD source more complicated, based on the shortest optical range principle, the electromagnetic wave transmission model in the presence of obstacles inside the transformer box was proposed to reduce the time delay difference. Simulation and field experiment results show that the positioning accuracy of AIW-BFA can meet the positioning requirements, and is better than BFA method and particle swarm optimization (PSO) method, which proves the effectiveness of the proposed model and positioning algorithm. [ABSTRACT FROM AUTHOR]

This article proposes an approach of time-difference-of-arrival (TDoA) positioning in ultrawideband networks, where user tags localize themselves by means of the exploitation of the broadcasted synchronization messages of the anchor network. Such an approach promises an unlimited number of localized devices; moreover, the position is available directly at the user terminal. The key challenge of this method is to eliminate the errors caused by tag clock drifts, which render the TDoA measurements useless when left uncorrected. Our method employs extended Kalman filtering for the estimation of position and the elimination of the drift-induced errors. It is shown that the system performance is similar to that of the more common TDoA method, where the tags transmit blinks received by the anchors. However, the anchors are still able to receive the blink messages and estimate the position of those tags, since the synchronization messages are exploited. Therefore, it is possible to use both the directions of TDoA positioning concurrently; a limited number of tags is tracked by the infrastructure and all tags may compute their positions. The TDoA solutions have achieved a root-mean-square horizontal accuracy of 25.9 and 33.6 cm, respectively. [ABSTRACT FROM AUTHOR]

This paper studies the localization problem using noisy range-difference measurements, or equivalently time difference of arrival (TDOA) measurements. There is a reference sensor, and for each other sensor, the TDOA measurement is obtained with respect to the reference one. By minimizing the sum of squared errors, a nonconvex constrained least squares (CLS) problem is formulated. In this work, we focus on devising an algorithm to seek the global minimizer of the CLS problem, hoping that the numerical solution meets some precision requirement in terms of relative error. Based on the Lagrange multiplier method, we first branch the feasible Lagrange multiplier set into several subsets and develop a workflow in terms of if-then-else control structure to seek the global minimizer by searching for the optimal Lagrange multiplier. The execution order is carefully organized so that it is in line with the general principle of putting the flow that one normally understands to be executed first. We then dive into detailed searching methods in different cases and conduct computational error analysis, giving the error bound on the Lagrange multiplier, when we search for it, to meet the precision requirement on an approximate solution. Based on the above achievements, a programmable global minimizer seeking algorithm is proposed for the CLS problem. Simulations and experimental tests on a public dataset demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the radiation source localization problem based on time‐difference‐of‐arrival (time difference of arrival (TDOA)) measurements in the presence of sensor position errors. In order to alleviate the estimation degradation in locating a radiation source caused by the sensor position uncertainty, we propose to use a single calibration source and develop a novel two‐stage localization algorithm based on the constrained scoring algorithm (CSA). In the first stage, we use the information of the calibration source to reduce the sensor position deviation, and here the CSA method is utilised to solve a quadratic constrained maximum likelihood (ML) objective function which is formulated from a set of underdetermined pseudo‐linear equations. In the second stage, using the updated sensor positions and the TDOA measurements of the radiation source, another ML estimation problem with quadratic constraints is formulated, and we again invoke the CSA method to jointly estimate the source and sensor positions. Moreover, the asymptotically optimal performance of the proposed algorithm is mathematically analysed and proved based on the first‐order error analysis. The simulation results verify the excellent performance of our algorithm and also demonstrate its robustness of resisting large measurement noise and sensor position errors. [ABSTRACT FROM AUTHOR]

Abstract By utilizing the time difference of arrival (TDOA), the frequency difference of arrival (FDOA), and the differential Doppler rate (DDR) measurements from sensors, this paper proposes an effective moving source localization algorithm with closed solutions. Instead of employing the traditional two-step weighted least squares (WLS) process, the Lagrange multiplier technique is employed in the first step to obtain the initial solution. This initial solution yields a better solution than the existing solution because the dependence among the variables are taken into account. The initial solution is further refined in the second step. The simulation results verify the effectiveness of the proposed algorithm when compared with the relevant existing algorithms.

Range-based localization has received considerable attention in wireless sensor networks due to its ability to efficiently locate the unknown source of a signal. However, the localization accuracy with a single set of measurements may be inadequate, especially in dynamic and noisy environments. To mitigate this problem, received signal strength difference (RSSD) and time difference of arrival (TDOA) measurements are used to develop an efficient estimator to reduce the bias and improve localization accuracy. First, the RSSD/TDOA-based maximum likelihood (ML) localization problem is transformed into a hybrid information nonnegative constrained least squares (HI-NCLS) framework. Then, this framework is used to develop an effective bias-reduction localization approach (BRLA) with a two-step linearization process. The first step employs a linear solving method (LSM) which exploits an active set method to obtain a sub-optimal estimator. The second step uses a bias reduction method (BRM) to mitigate the correlation from linearization and a weighted instrumental variables matrix (IVM) which is weakly correlated with the noise but strongly correlated with the data matrix (DM) is used in place of the DM. Performance results are presented which demonstrate that the proposed BRLA provides better localization performance than state-of-the-art methods in the literature. [ABSTRACT FROM AUTHOR]

The time difference of arrival (TDOA) and frequency difference of arrival (FDOA) between two receivers are widely used to locate an emitter. Algorithms based on cross ambiguity functions can simultaneously estimate the TDOA and FDOA accurately. However, the algorithms, including the joint processing of received data, require transferring a large volume of data to a central computing unit. It can be a heavy load for the data link, especially for a wideband signal obtained at a high sampling rate. Thus, we proposed a multi-pulse cross ambiguity function (MPCAF) to compress the data before transmitting and then estimate the TDOA and FDOA with the compressed data. The MPCAF consists of two components. First, the raw data are compressed with a proposed two-dimensional compression function. Two methods to construct a reference pulse used in the two-dimensional compression function are considered: a raw data-based method constructs the pulse directly from the received signal, and a signal parameter-based method constructs it through the parameters of the received signal. Second, a wideband cross-correlation function is studied to refine the TDOA and FDOA estimates with the compressed data. The simulation and Cramer–Rao lower bound (CRLB) analyses show that the proposed method dramatically reduces the data transmission load but estimate the TDOA and FDOA well. The hardware-in-the-loop simulation confirms the method's effectiveness. [ABSTRACT FROM AUTHOR]

This paper proposes a method to localize multiple partial discharge (PD) sources in air-insulated substation by using radio frequency (RF) antenna sensors combining with clustering algorithm, which solves the problem that multiple PD or interference sources are difficult to localize in air-insulated substation (AIS). Our research group made four omnidirectional RF antenna sensors, the detection frequency band was 250 MHz-3 GHz, the voltage standing wave ratio (VSWR) of the RF antenna in the detection frequency band was less than 2, which meets the detection requirements. The scenes of multiple PD sources in AIS were simulated in the laboratory, the time difference of arrival (TDOA) was collected through four RF antennas, and the clustering effects of K-means and Two Step algorithms were compared. Based on the normal distribution characteristics of TDOAs and the better Two Step algorithm, a $3\sigma $ -Two Step algorithm was proposed to process TDOAs and output the centers of TDOAs. Newton iterative algorithm was used to solve the localization equations. The results show that the average localization error of two PD sources was 0.21 m and that of three PD sources was 0.45 m. The PD localization tests were carried out in an actual AIS in China, the proposed method localized a corona discharge on a post insulator and a corona discharge on a disconnector. Through the verifications with an ultraviolet camera, it was obtained that the localization errors at each position were within the range of 0.6-2 m. [ABSTRACT FROM AUTHOR]

Nowadays, accurate localization plays an essential role in many fields, such as target tracking and path planning. The challenges of indoor localization include inadequate localization accuracy, unreasonable anchor deployment in complex scenarios, lack of stability, and the high cost. So, the universal positioning technologies cannot meet the real application requirements scarcely. To overcome these shortcomings, a comprehensive ultra wide-band (UWB)-based real-time localization system (RTLS) is presented in this paper. We introduce the architecture of a real-time localization system, then propose a new wireless clock synchronization (WCS) scheme, and finally discuss the time difference of arrival (TDoA) algorithm. We define the time-base selection strategy for the TDoA algorithm, and we analyze the relationship between anchor deployment and positioning accuracy. The extended Kalman filter (EKF) method is presented for non-linear dynamic localization estimation, and it performs well in terms of stability and accuracy on moving targets. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the mobile node localization problem in an underwater positioning system using multiple surface beacons, where time synchronization between the surface beacons and underwater mobile nodes is not required. First, we propose a Time-Difference-of-Arrival-based (TDoA-based) node localization method. In the proposed method, as the underwater node receives the positioning messages from more than or equal to three surface beacons belonging to the same grid at one epoch, it calculates the location by itself using the information of received positioning messages and the corresponding arrival times. To deal with the problem that the number of received positioning messages at one epoch by the underwater node is not enough for the location calculation, we present the modified TDoA- based node localization method with buffered messages and the modified TDoA-based node localization method with motion information. The effectiveness of proposed TDoA-based node localization method and two modified methods is validated by simulation results and sea experiment data analytical results. The robustness of the TDoA-based node localization method is improved by two modified methods. Moreover, using the proposed TDoA-based node localization method and two modified methods, the underwater node can localize itself in low energy consumption and passive mode.

The detection of partial discharge (PD) is a crucial method to evaluate the insulation status of transformers. The main difficulties of the current localization algorithms are the complexity of the solution and sensitivity to time delay errors. This article proposes a PD localization method in transformers based on linear conversion and density peak clustering (DPC). First, to reduce the complexity of solving the localization equations, the nonlinear localization equations are transformed into linear localization equations by eliminating the second-order terms. Then, to reduce the influence of time delay errors on localization accuracy, the initial localization values are located by multiple acoustic emission (AE) sensors. Finally, the optimal PD coordinates are determined by clustering the initial location values using density peaks clustering algorithm with automatic finding centers (AFC-DPC). The experimental results show that the proposed method can improve PD localization accuracy in transformers, and the average localization error is 5.30 cm.

This paper provides a comprehensive review of the acoustic method used for partial discharge (PD) localization in power transformer. The paper outlines the partial discharge mechanism, basic principle and properties of the acoustic emission (AE), advantages and limitations of the acoustic method compared to the electrical method, and different AE based PD localization techniques along with their algorithms used in power transformer. Most of the algorithms proposed in the literature for PD localization in power transformer are based on the time difference of arrival (TDOA) of AE signals. Thus, this paper would help researchers, academicians, and engineering communities working in the area of AE based PD localization in power transformer to avoid insulation damage. [ABSTRACT FROM AUTHOR]

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