Time Synchronization Problem Research Articles

Fixed Time Synchronization of Stochastic Takagi–Sugeno Fuzzy Recurrent Neural Networks with Distributed Delay under Feedback and Adaptive Controls

In this paper, the stochastic Takagi–Sugeno fuzzy recurrent neural networks (STSFRNNS) with distributed delay is established based on the Takagi–Sugeno (TS) model and the fixed time synchronization problem is investigated. In order to synchronize the networks, we design two kinds of controllers: a feedback controller and an adaptive controller. Then, we obtain the synchronization criteria in a fixed time by combining the Lyapunov method and the related inequality theory of the stochastic differential equation and calculate the stabilization time for the STSFRNNS. In addition, to verify the authenticity of the theoretical results, we use MATLABR2023A to carry out numerical simulation.

High-Precision Time Difference of Arrival Estimation Method Based on Phase Measurement

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.

Finite and fixed-time synchronization of memristive chaotic systems based on sliding mode reaching law

Two innovative sliding mode control laws based on the convergence principle of reaching law are presented in this work. These control laws are used to achieve both finite-time and fixed-time synchronization for a specific class of memristive chaotic system, which are known for their intricate and complex dynamical behaviors. By utilizing these control strategies, we can effectively manage the synchronization process and ensure rapid convergence. Firstly, for the finite-time synchronization issue, a novel power reaching law is derived. Compared with the conventional reaching law, the reaching law presented in this work has a prominent advantage that the chattering of the sliding mode control is reduced to a lesser extent and the speed of reaching the sliding surface is quicker. An upper bound of the stabilization time, which is dependent on the initial conditions of the system, is obtained and the system is proved stable. For the fixed time synchronization problem, a new double power reaching law is put forward to minimize the chattering and accelerate the convergence. Then, by utilizing the fixed time stability theory, the upper bound of the convergence time that remains invariant with the initial value of the system is derived. Finally, in order to verify the effectiveness and feasibility of the theoretical derivation in this paper, two sets of control experiments are set up and the influences of the two control laws on the system synchronization state are compared. The experimental phenomenon strongly proves the accuracy of the proposed theorem.

Finite/fixed‐time synchronization of nonidentical chaotic delayed neural networks with Markovian jump and uncertainties via sliding mode control

AbstractThis article investigates the finite/fixed‐time synchronization problem of Markovian jumping delayed chaotic recurrent neural networks with uncertainties by employing sliding mode control method. In order to cope with Markovian jump and parameter uncertainties, sliding model control is used for its strong robustness and insensitivity character to external disturbances and parameter variations. Above all, two simplified sliding mode surfaces are constructed. Following that, matching controllers are constructed to guarantee the finite/fixed‐time reachability of sliding mode surfaces. Additionally, the dynamics of the sliding mode surfaces can be achieved in a finite/fixed‐time. Based on the Lyapunov stability theory, some sufficient conditions are proposed to guarantee the finite/fixed‐time synchronization. Compared with the conclusions of the existing literature, this article generalizes the corresponding results to Markovian jump, parameter uncertainties, and nonidentical systems. Finally, two numerical examples are given to illustrate the effectiveness of the obtained theoretical results.

Ice-Covered Warning System and Terminal Application Based on Beidou Technology

Disaster prevention and mitigation are one of the most prominent industrial applications of Beidou. Through the Beidou system’s short message and location reporting functions, it can realize disaster early warning, disaster rescue command and dispatch, and rapid emergency communication, which can greatly improve the speed of disaster emergency rescue response and decision-making ability. Based on the existing ice monitoring system of power grids and terminal in communication, positioning, and time synchronization problems such as the status quo analysis, based on the use of Beidou communication technology to modify ice monitoring system and terminals, and through system alignment, information coding and check, based on Beidou communication technology is verified in ice monitoring, especially the important role of disaster prevention and mitigation, it has important national strategic significance and multiple meanings such as industry 2025, new infrastructure and digital power grid.

THE DEVELOPMENT AND VALIDATION OF A NAVIGATION GRADE EGI SYSTEM FOR LAND VEHICULAR NAVIGATION APPLICATIONS

Abstract. In recent years, most people use commercial integrated navigation systems to develop navigation algorithms. However, due to the different levels of sensors on the market, it is difficult to customize commercial systems and leads to limited development of navigation algorithms. Therefore, the purpose of this research is to develop a real-time integrated navigation system EGI-1000 (Embedded GNSS and INS) including software and hardware, and effectively reduce the cost with the commercial price. The real-time integrated navigation system EGI-1000 contains a navigation-grade IMU, IMU1000 and NovAtel OEM 7720 GNSS receiver module. In this research, the integration process can be divided into three parts. The first part is the integration of hardware, and the architecture diagram of the real-time integrated navigation system will be displayed. The second part is the pre-processing of data. In the multi-sensor time synchronization problem, this research will propose a method about cross-correlation to validate whether the timestamp of IMU data is delay. The last part is algorithm about fusing data from multiple sensors and motion constraints. Extended Kaman Filter (EKF) will be the core and motion constraints including Zero Velocity Update (ZUPT) and Non-Holonomic Constraints (NHC) are integrated in the Loosely Coupled (LC) scheme. The calibration of Inertial navigation Measurement Unit (IMU) will also be conducted to determine the parameter in algorithm. The results of the experiments will be shown in this paper. Both of hardware and navigation algorithm in the integrated navigation system of this research are used to conduct multiple experiment including open sky environments, GNSS challenging environments, and GNSS denied environment. In comparison with the reference data, the navigation accuracy of the developed integrated navigation system can achieve centimeter-level accuracy (“Active Control” level and “Where in lane” level) in open sky and GNSS challenging environments. According to the propagation error theory, the result in GNSS denied environment also meet the expected value. The navigation algorithm is also feasible for the commercial integrated navigation system.

Joint Method of Moments (JMoM) and Successive Moment Cancellation (SMC) Multiuser Time Synchronization for ZP-OFDM-Based Waveforms Applicable to Joint Communication and Sensing

It has been recently shown that zero padding (ZP)-orthogonal frequency-division multiplexing (OFDM) is a promising candidate for 6G wireless systems requiring joint communication and sensing. In this paper, we consider a multiuser uplink scenario where users are separated in power domain, i.e., non-orthogonal multiple access (NOMA), and use ZP-OFDM signals. The uplink transmission is grant-free and users are allowed to transmit asynchronously. In this setup, we address the problem of time synchronization by estimating the timing offset (TO) of all the users. We propose two non-data-aided (NDA) estimators, i.e., the joint method of moment (JMoM) and the successive moment cancellation (SMC), that employ the periodicity of the second order moment (SoM) of the received samples for TO estimation. Moreover, the coding assisted (CA) version of the proposed estimators, i.e., CA-JMoM and CA-SMC, are developed for the case of short observation samples. We also extend the proposed estimators to multiuser multiple-input multiple-output (MIMO) systems. The effectiveness of the proposed estimators is evaluated in terms of lock-in probability under various practical scenarios. Simulation results show that the JMoM estimator can reach the lock-in probability of one for the moderate range of Eb/N0 values. While existing NDA TO estimators in the literature either offer low lock-in probability, high computational complexity that prevents them from being employed in MIMO systems, or are designed for single-user scenarios, the proposed estimators in this paper address all of these issues.

Predefined-time synchronization of coupled neural networks with switching parameters and disturbed by Brownian motion

This article focuses on predefined time synchronization problem for a class of signal switching neural networks with time-varying delays. In the network models, we not only consider the coupling characteristics in the following networks, but also consider the disturbance with standard Brownian motion. In the design of the controller, the control gain is designed as 1ɛ+Tp−t (t∈[T0,Tp), ɛ is an optional smaller positive number), which avoids the infinite gain (the control gain is designed as 1Tp−t in other reference). In order to get the predefined time control law, a power function is multiplied to the Lyapunov functional, from which it can get an exponential upper bound function via the derivative and mathematical expectation operation. Utilizing the martingale theory and the method of Laplace matrix, some novel predefined time synchronization criteria are obtained for the leader-following neural networks, meanwhile the following networks can maintain the leader network after achieved synchronization. Based on the special network of the main system, five corollaries separately develop the predefined time synchronization results from different perspectives. An example with some simulation figures and computing results fully exhibits the effectiveness of the achieved synchronization scheme. In this case, although the error signal is disturbed by Brownian motion, the trace signal can still stably converge to zero by this control scheme, meanwhile the predefined-time control effect is achieved.

Time Synchronization Uncertainty Estimation Methodology for data-centric Production Scenarios

In modern data-centric production, multiple sensors, machines, equipment, and systems are connected providing a high variety and amount of data to enable insights into ongoing production. By adding more of those data sources to production, the Time Synchronization Problem becomes more intense when aggregating the data streams for analysis. To tackle the Time Synchronization Problem and perform the actual synchronization of the data, an estimation of the potential uncertainties is required. In this paper, we present a methodology to identify and describe the chain of time series uncertainties from data sources to aggregation. This calculation takes into account the uncertainties from sensory and signal processing requirements (like signal processing cycle times) to network communication latencies and jitter in the production network. In the end, each data point gets augmented with information regarding the uncertainty of its time information in terms of the minimum and maximum added processing times in the data pipeline. Using this information, the data aggregation can be adapted accordingly, and the final analysis tailored down to the actual result uncertainties. To show this method and its applicability, a use-case in battery cell manufacturing is presented including a description of how this method can be applied and the data pipeline adapted.

BioSync: Offline-Synchronization of time-series data using bio-inspired semantic synchronization strategies

In modern data-centric production, multiple sensors, machines, equipment, and systems are connected providing high variety and amount of data to enable insights to ongoing production. By adding more of those data sources to production, the Time Synchronization Problem becomes more intense when aggregating the data streams for analysis afterwards (offline synchronization). This synchronization is crucial for the quality of the whole dataset used for analytics and services like machine learning applications. A similar effect is existing in the human brain and the parietal cortex when a human is performing a multisensory fusion. The human brain uses information about the environment and the understanding of cause-effect relationships to present synchronized sensor information to the conscious human to act upon. In this paper, we show how this concept can be adapted to the time-series synchronization of production data. By using the information of the production system and the insights on cause-effect relationships on the components and inside the production process, probabilities of data points being connected by physical events in the process. The application and integration strategies for using this bio-inspired synchronization are presented in addition to the requirements for such applications in the production domain. By this, the bio-inspired semantic synchronization enables a completely new way of tackling the Time Synchronization Problem, especially for offline data sets.

Robust Synchronization of Ambient Vibration Time Histories Based on Phase Angle Compensations and Kernel Density Function.

The output-only modal analysis is ubiquitously used for structural health monitoring of civil engineering systems. The measurements for such applications require the use of multiple data acquisition systems (DAS) to avoid complicated meshes of cables in high-rise buildings, avoid traffic constriction on a bridge during measurements, or to avoid having limited channels in a single DAS. Nevertheless, such requirements introduce time synchronization problems which potentially lead to erroneous structural dynamic characterization and hence misleading or inconclusive structural health monitoring results. This research aims at proposing a system-identification-based time synchronization algorithm for output-only modal analysis using multiple DAS. A new procedure based on the compensation of the phase angle shifts is proposed to identify and address the time synchronization issue in ambient vibration data measured through multiple DAS. To increase the robustness of the proposed algorithm to the inherent inconsistencies in these datasets, the kernel density function is applied to rank multiple time-shift estimates that are sometimes detected by the algorithm when inaccuracies exist in the data arising from low signal-to-noise ratio and/or presence of colored noise in the ambient excitations. First, the synchronized ambient vibration dataset of a full-scale bridge is artificially de-synchronized and used to present a proof of concept for the proposed algorithm. Next, the algorithm is applied to ambient vibration data of a 30-story, reinforced concrete building, where the synchronization of the data could not be achieved using two DAS despite best efforts. The application of the proposed time synchronization algorithm is shown to both detect and correct the time synchronization discrepancies in the output-only modal analysis.

A Survey on Hadoop Security and Comparative Analysis on Authentication Frameworks in Hadoop Clusters

This paper presents a survey on various security frameworks which aim to secure Hadoop clusters and the files stored there. Most of the Hadoop platforms rely on Kerberos Authentication Protocol for user authentication. It is known that Kerberos itself has some vulnerabilities like Password Guessing Attacks, Single Point of Failure, Insider Attacks, and Time Synchronization problems. A new authentication framework that can address most of the identified issues and is computationally feasible than other schemes is introduced in this paper. The proposed framework is based on Secure Remote Password Protocol (SRP), Threshold Cryptography, and Blockchain Technology. The system focus on eradicating Password Guessing Attacks and Single Point of Failure in Kerberos Enabled Hadoop Clusters. The analysis results strengthen the claim that the proposed scheme beats the others in terms of computational efficiency and feasibility while offering the same security features. The paper is concluded with directions for future work.

Wireless fieldbus networking with precision time synchronization for a low-power WSAN

A wireless sensor actuator network (WSAN) is an essential part of the industrial Internet of things (IIoT) with a cyber-physical system (CPS). However, the lack of time synchronization support for WSANs to date means that losses in synchronization accuracy have not been overcome. In this manuscript, we design a real-time kernel to resolve the precision time synchronization (PTS) problem and achieve dynamic automatic synthesis and rapid deployment in a complex WSAN system. Furthermore, tight coupling between wireless control and time synchronization motivates a Modbus-based precision time protocol (PTP) codesign that integrates industrial fieldbus networks and a PTS design. The experimental results indicate that our proposed PTP performance can far exceed the time synchronization requirement of an industrial real-time system. Finally, the proposed solution is successfully applied to an IoT-based smart lighting system.

Predefined Time Synchronization Control for Uncertain Chaotic Systems.

In this study, the predefined time synchronization problem of a class of uncertain chaotic systems with unknown control gain function is considered. Based on the fuzzy logic system and varying-time terminal sliding mode control technology, the predefined time synchronization between the master system and the slave system can be realized by the proposed control method in this study. The simulation results confirm the theoretical analysis.

Inter-satellite time synchronization and ranging link assignment for autonomous navigation satellite constellations

The BeiDou Navigation Satellite System (BDS) has equipped its new-generation satellites with inter-satellite link (ISL) payloads for the autonomous operation. In the autonomous operation mode, the navigation constellation realizes the time unification and orbit determination through inter-satellite time synchronization and ranging, so as to self-maintain the time-space reference. For solving the problem of inter-satellite time synchronization and ranging link assignment for autonomous navigation constellations, assignment algorithms of time synchronization links based on satellite layers, as well as ranging links combining satellite selection and weighting, were proposed. The ISL assignment simulation of the BeiDou-3 global constellation was implemented using our algorithms, and the assignment was completed almost within 5 cycles. According to the statistical results, using 56.6% of ISL timeslots, the constellation had achieved the optimal mode of inter-satellite time synchronization with the least skips; the number of inter-satellite ranging links established per minute of each satellite was not less than 10, which met the constraint of the minimum links; the average PDOP of ranging links of the MEO, GEO and IGSO satellite was about 1, 1.6 and 1.5 respectively, close to the lower reference limit. It was demonstrated that the ISLs were reasonably assigned for the demand of inter-satellite time synchronization and ranging with constraints of the TDMA system, narrow beams, the quantity of payloads and inter-satellite visibility.

A Tracking Algorithm for Sparse and Dynamic Underwater Sensor Networks

An underwater sensor network (UWSN) has sparse and dynamic characteristics. In sparse and dynamic UWSNs, the traditional particle filter based on multi-rate consensus/fusion (CF/DPF) has the problems of a slow convergence rate and low filtering accuracy. To solve these problems, a tracking algorithm for sparse and dynamic UWSNs based on particle filter (TASD) is proposed. Firstly, the estimation results of a local particle filter are processed by a weighted average consensus filter (WACF). In this way, the reliability difference of state estimation between nodes in sparse and dynamic UWSN is reasonably eliminated. Secondly, a delayed update mechanism (DUM) is added to WACF, which effectively solves the problem of time synchronization between the two particle filters. Thirdly, under the condition of limited communication energy consumption, an alternating random scheme (ARS) is designed, which optimizes the mean square convergence rate of the fusion particle filter. Simulation results show that the proposed algorithm can be applied to maneuvering target tracking in sparse and dynamic UWSN effectively. Compared with the traditional method, it has higher tracking accuracy and faster convergence speed. The average estimation error of TASD is 91.3% lower than that of CF/DPF, and the weighted consensus tracking error of TASD is reduced by 85.6% compared with CF/DPF.

An Innovative Push-To-Talk (PTT) Synchronization Scheme for Distributed SAR

Bistatic/multistatic synthetic aperture radar (Bi-/M-SAR) has extensive applications, including cross- and along-track interferometry, multiangle imaging, and so on. However, these applications are affected by several factors, especially the time and frequency synchronization that this article focuses on. Here, the new insights about the focus are described, based on which an innovative push-to-talk (PTT) scheme is first put forward to solve the time and frequency synchronization problems of distributed SAR. It realizes unidirectional and long-distance synchronization through the two-segment frequency rate (TSFR) waveform composed of the two linear frequency modulation (LFM) signals with different frequency rates and achieves radar imaging and synchronization with a high rate ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${f}_{\mathrm{ syn}}$ </tex-math></inline-formula> ) at the same time by the frequency diversity (FD) waveforms and bandpass filters. To clarify the PTT scheme, its system framework and synchronization error models are detailed. Besides, the channel impulse response, multiple errors, the signal-to-noise ratio (SNR) of synchronous links, and time synchronization accuracy are analyzed. In addition, some simulations are carried out to evaluate its performance. Those results demonstrate that the PTT scheme can perform the long-distance synchronization with high <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f_{\mathrm{ syn}}$ </tex-math></inline-formula> , its synchronization accuracy increases with the improvement of SNR and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f_{\mathrm{ syn}}$ </tex-math></inline-formula> , and it can measure the time and frequency deviations in different ranges accurately. In a word, the PTT scheme has the potential to be used in future distributed SAR missions.

The Time Synchronization Problem in data-intense Manufacturing

Modern production systems rely heavily on data from the machines, processes and other sources. The amount and resolution of the data increases continuously allowing complex models like Machine Learning models to be trained and applied in production. All analytics and models that rely on the production data implicitly require a suitable data quality. Central level of data generation and acquisition is the machine level where data from machine and sensors are generated and need to be aggregated. In this paper, we define the Time Synchronization Problem that arise from the different data sources and technical properties of computerized systems in manufacturing directly impacting the quality of Time-Series data. Further, we cluster current approaches and discuss their applicability in data-intense manufacturing.

Fixed-time synchronization of delayed impulsive inertial neural networks with discontinuous activation functions via indefinite LKF method

This article investigates the fixed time synchronization (FXTSY) problem of time-varying delayed impulsive inertial neural networks (INNs) with discontinuous activation functions. First, the addressed delayed discontinuous INNs are converted into a first-order differential equation using a generalized variable transformation with suitable tunable variables. Due to the existence of the discontinuities, the delayed discontinuous differential equations are transformed into the differential inclusions by using the differential inclusion theory and set-valued map concepts. Furthermore, by designing the suitable centralized impulsive control and discontinuous control, constructing the novel indefinite type Lyapunov functionals, new algebraic conditions are derived to realize the FXTSY for the leader-following impulsive INNs. Moreover, the settling time is explicitly calculated. Finally, the developed theoretical results are verified by two numerical simulation results.

Influence of synchronization within a sensor network on machine learning results

Abstract. Process sensor data allow for not only the control of industrial processes but also an assessment of plant conditions to detect fault conditions and wear by using sensor fusion and machine learning (ML). A fundamental problem is the data quality, which is limited, inter alia, by time synchronization problems. To examine the influence of time synchronization within a distributed sensor system on the prediction performance, a test bed for end-of-line tests, lifetime prediction, and condition monitoring of electromechanical cylinders is considered. The test bed drives the cylinder in a periodic cycle at maximum load, a 1 s period at constant drive speed is used to predict the remaining useful lifetime (RUL). The various sensors for vibration, force, etc. integrated into the test bed are sampled at rates between 10 kHz and 1 MHz. The sensor data are used to train a classification ML model to predict the RUL with a resolution of 1 % based on feature extraction, feature selection, and linear discriminant analysis (LDA) projection. In this contribution, artificial time shifts of up to 50 ms between individual sensors' cycles are introduced, and their influence on the performance of the RUL prediction is investigated. While the ML model achieves good results if no time shifts are introduced, we observed that applying the model trained with unmodified data only to data sets with time shifts results in very poor performance of the RUL prediction even for small time shifts of 0.1 ms. To achieve an acceptable performance also for time-shifted data and thus achieve a more robust model for application, different approaches were investigated. One approach is based on a modified feature extraction approach excluding the phase values after Fourier transformation; a second is based on extending the training data set by including artificially time-shifted data. This latter approach is thus similar to data augmentation used to improve training of neural networks.