Time Difference Of Arrival Research Articles

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.

Hydrological and Hydraulic Modelling of Sekolo River Diversion for Coal Mining Activities

A Hydrological and hydraulic modeling of the proposed diversion of the Sekolo River for coal mining purposes will provide an overview and technical information on the proposed river realignment and identify potential impacts on the use of the Sekolo River. It is being carried out for River extension to maintain river sustainability and functionality. Sekolo River watershed modeling utilizes a combination of hydrological analysis and open-channel hydraulic modeling using HEC-HMS and HEC-RAS software. To meet the area criterion (volume), the dimensions of the diversion channel are planned with B = 15 m. H = 1.5 m; m = 1.5; L = 1253 m. These dimensions make the volume of the diversion approximately the same as the existing river. Hydraulic simulation results showed that the flood level would not rise downstream of the diversion channel. The upstream (Q25) peak flow rate is 41.90 m3/s, while the downstream peak flow rate after diversion is increased by 0.02% compared to the existing flow rate. After introducing the diversion channel, the difference in flood peak arrival time (Tp) will be 3 minutes earlier than before the diversion.

A comprehensive study of orbital evolution of LMC X-4: existence of a second derivative of the orbital period

ABSTRACT We report here results from pulse arrival time delay analysis of the eclipsing high-mass X-ray binary (HMXB) pulsar LMC X-4 using observations made with the Rossi X-ray Timing Explorer, XMM–Newton, NuSTAR (Nuclear Spectroscopic Telescope ARray), and AstroSat. Combining the orbital parameters determined from these observations with the historical measurements dating back to 1998, we have extended the Tπ/2 epoch history of LMC X-4 by about 4600 binary orbits spanning about 18 yr. We also report mid-eclipse time measurements (Tecl) using data obtained from wide-field X-ray monitors of MAXI-GSC (Monitor of All-sky X-ray Image – Gas Slit Camera) and Swift-BAT (Burst Alert Telescope). Combining the new Tπ/2 and Tecl estimates with all the previously reported values, we have significantly improved the orbital evolution measurement, which indicates that the orbital period is evolving at a time-scale ($P_{\rm orb}/\dot{P}_{\rm orb}$) of about 0.8 Myr. For the first time in an accreting X-ray pulsar system, we confirm the existence of a second derivative of the orbital period, having an evolution time-scale ($\dot{P}_{\mathrm{ orb}}/\ddot{P}_{\mathrm{ orb}}$) of about 55 yr. Detection of a second derivative of the orbital period in LMC X-4 makes its orbital evolution time-scale more uncertain, which may also be true for other HMXBs. Independent solutions for the orbital evolution measurement using the mid-eclipse data and the pulse timing data are consistent with each other, and help us put an upper limit of 0.009 on the eccentricity of the binary system.

Multi-frequency probabilistic imaging fusion for impact localization on aircraft composite structures

Since the internal barely visible damage of aircraft composite structures caused by the impact is a critical problem, impact monitoring is essential for the integrity and reliability of aircraft composite structures. This paper presents a multi-frequency probabilistic imaging fusion method for localizing impacts on aircraft composite structures. To capture the impact signals, a network of distributed sensors is mounted on the structure. The impact signals are then processed using the continuous wavelet transform (CWT) to extract the multi-frequency narrowband Lamb wave signals. The time difference of arrival (TDOA), a key feature of the impact source, is measured using averaging techniques employed in the normalized variance sequence. Subsequently, a probabilistic imaging function is established, and the TDOA of narrowband Lamb wave signals at each frequency is used as the feature input to generate the multi-frequency probabilistic imaging results. To determine the performance of the imaging results at each frequency, an efficiency index is introduced, allowing for the retention or abandonment of the imaging results. By utilizing the retained multi-frequency probabilistic imaging results, the proposed method achieves impact localization through imaging fusion. Experimental verification is conducted on a stiffened aircraft composite panel, and a comparison is made with two existing methods: the hyperbolic locus imaging method and the virtual time reversal imaging method. The results show that the proposed method can significantly improve localization accuracy compared to the existing methods, and is effective even in the presence of measurement noise.

Maximizing Electric Power Recovery through Advanced Compensation with MPPT Algorithms

The present investigation introduces an advanced methodology for maximum power point tracking (MPPT) applied to a piezo harvester scheme. A comprehensive rectifier circuit, equipped with an embedded MPPT component, is established to optimize energy production by monitoring a DC-DC inverter connected to the rectifier. Furthermore, the system’s sensitivity error has been finely tuned to dynamically adjust its impedance unit in real time, thereby optimizing load acquisition. This innovative approach seamlessly integrates the MPPT algorithm into the piezo harvester circuit. Moreover, the vehicle’s road handling is significantly augmented through the incorporation of a robust steering front and an active differential control system. Leveraging the MPPT module, the rectifier consistently achieves a power recovery efficiency exceeding 85%, independent of varying load conditions. Additionally, a DC-DC converter circuit has been seamlessly integrated to finely adjust the output voltage to meet specified levels. Numerical simulations demonstrate the effectiveness of the harvesting scheme, extracting a substantial output power of 90 W with an overall efficiency of 70%. The improved MPPT approach, employing angles of arrival (AoA) DV-Hop control strategies, minimizes the system’s power consumption based on the Global Positioning System (GPS). The utilization of Harris Hawks optimization (HHO) and the generation of quadrants in the four-quadrant operation mode of DC motors in the wireless sensor network (RCSFs) have been significantly enhanced in this study. Simulations reveal that, at a velocity of 50 km/h, shock absorbers utilizing the received signal strength indication (RSSI) can harvest between 60 and 90 W on a class C road, based on the time of arrival (TOA). Striking a balance in ride comfort using the time difference of arrival (TDOA) as a trade-off constitutes approximately 30% of the piezoelectric harvester (PEH) system’s power consumption when operating in active suspension mode, optimized by particle swarm optimization (PSO).

An Ultra-Wideband Indoor Localization Algorithm with Improved Cubature Kalman Filtering Based on Sigmoid Function

In this paper, an improved cubature Kalman filtering (CKF) is proposed using the Sigmoid function to address the problems of positioning accuracy degradation and large deviations in ultra-wideband (UWB) indoor positioning in non-line-of-sight environments. The improved CKF is based on the squared range difference (SRD) model of the time difference of arrival (TDOA) algorithm. The inaccurate impact of model estimation under non-Gaussian noise is reduced by updating the measurement noise matrix in real time. The covariance matrix is estimated using singular value decomposition (SVD) to solve the problem of degraded state estimation performance. The filtering effect of the improved CKF algorithm is evaluated by referring to the checkpoints in the dynamic trajectory. The experimental results show that the proposed algorithm effectively mitigates the impact of UWB ranging outliers in the occluded experimental environment, which makes the dynamic positioning trajectory smoother, better fitted, and more stable. The algorithm improves the positioning accuracy by up to 39.29% compared with the SRD model used alone.

Time-difference reassigned transform with application to time difference of arrival for impulsive signal

Time difference of arrival (TDOA) is essential in localization, communication, and navigation. Under ambient noise interference, the impulsive signal is transmitted over a long distance and reaches the sensor with a low signal-to-noise ratio (SNR). Aiming to achieve a precise time delay estimation in scenarios with low SNR, this paper extends the time-reassigned extracting transform (TRET) theory to TDOA and proposes a time difference rearrangement extracting transform (TDRT) algorithm. The implementation process of the proposed algorithm comprises three steps. Initially, a rough two-dimensional time delay estimation is obtained by calculating the partial derivative of the short-time cross-power spectrum with respect to the frequency variable. Secondly, a rearrangement operation separates the signal's time-frequency (TF) points from the noise. Thirdly, a refined TDOA estimation is obtained by inverse Fourier transforming TF points extracted from the time-delay energy ridge. Simulation results show that the TDRT algorithm is effective in time delay estimation. Furthermore, experimental results prove that the performance of the TDRT algorithm outperforms comparable algorithms in low SNR environments.

A hybrid algorithm based on TDOA and DOA for underwater target localization

To address the problems of complex scenarios, low accuracy of a single localization algorithm, and high requirements for localization system equipment, this paper proposes a Taylor-weighted least squares algorithm based on Time Differences of Arrival (TDOA) and Direction of Arrival (DOA) joint localization method. The method first performs DOA estimation of the target by the Bayesian algorithm with off-grid sparse reconstruction and converts the obtained orientation information into position coordinates, and finds the final position of the target through iterative operations. The proposed algorithm is compared with the existing TDOA method based on the Chan algorithm and the Taylor algorithm based on the TDOA with the Chan algorithm as the initial value and the Two-Step Weighted Least Squares (TSWLS) algorithm in a simulation environment. The results show that the proposed algorithm has better performance in terms of localization accuracy, noise immunity, stability and is suitable for target localization in underwater.

Network Analysis of Active and Passive Radar Based on the Extended Kalman Filter

In this current work, a sequential extended Kalman filter (EKF) algorithm is proposed to solve the quandary of regional accuracy disparity for time difference of arrival (TDOA) positioning in passive radar. The proposed approach entails merging the complete set of active radar observation data with the TDOA data of passive radar in a serial fashion, followed by the deployment of the EKF algorithm for the purpose of filtering. The simulation results show that the proposed data fusion method effectively solves the regional accuracy imbalance problem caused by the fuzzy area and no solution area of the TDOA positioning solution through the conflation of the precision merits of active and passive radar, and improves the overall target detection accuracy.

A method for localization TDOA estimation based on signal‐level fusion and analysis

AbstractPassive localization using time difference of arrival (TDOA) is one of the common methods for radiated source target localization, and its localization performance depends on the accuracy of TDOA estimation. In many localization scenarios, the signals of target are intricate and often contain multiple ones, so how to effectively extract the time difference parameters from them becomes a difficult problem. This letter proposes a signal‐level fusion method for TDOA estimation to address this problem. At the receiver terminal, multiple signals are fused to obtain their cross‐ambiguity function, so as to estimate the TDOA value for target localization. The effect of fused signals on the accuracy of TDOA estimation is analyzed and the concept of frequency depression is proposed. Simulation experiments verify that the estimation method with signal‐level fusion gets a higher accuracy of the TDOA value and a smaller position error in target localization.

Analysis of influence of sensor placement for source localization based on TDOA algorithm

Locating the source of a radio wave signal is an essential research topic in various applications, such as tracking systems, navigation systems, and systems related to safety and security. This research has studied and analyzed the effect of sensor device positioning on the efficiency in determining the source’s location based on Time Difference of Arrival (TDOA) technology. We focused on the system’s efficiency in areas where sensors cannot be installed. The results and analysis demonstrate that the configuration pattern of the sensor device instantly influences the dimensions and coverage area at which the radiation source can be accurately detected. This research furnishes an essential basis for further developing and deploying transmitter detection approaches, particularly in inaccessible places.

Three-dimensional sound source localization system based on fiber optic sensor array with an adaptive algorithm

Sound Source Localization (SSL) based on microphone arrays nowadays is widely used in the fields of machinery rattle localization, weapon navigation, and robot hearing. However, there is also a need for SSL in some special environments, such as long-distance gas pipelines where there is a risk of leakage. In these environments, arrays built with ordinary microphones can be a safety hazard, which can be compensated by the External Fabry-Perot interferometer (EFPI) fiber-optic sensor's advantages of being uncharged, inexpensive, and highly sensitive. To expand the application areas of microphone array-based SSL techniques, this paper using four EFPI fiber-optic sensors to constructs a 1 m × 1 m × 1 m sensor array, which is used for real-time localization of sound sources based on three-wavelength adaptive intensity demodulation methods and Time Difference Of Arrival (TDOA) localization techniques. An improved method is proposed for the three-wavelength adaptive intensity demodulation method to avoid the occurrence of signal flips. Finally, formulas are provided for this sensor array to quickly calculate the sound source position based on the arrival delay. Several experiments show that the system can achieve sound source localization over a wide range with an average error of less than 3 cm.

Detection of Emulation Attacks in Cognitive Radio Networks using Heuristic Techniques

This paper discusses the issue of primary user emulation attacks (PUEAs) in cognitive radio networks (CRNs). These attackers imitate the signal characteristics of primary users (PUs), preventing secondary users (SUs) from accessing their assigned spectrum. The research focuses on detecting PUEAs using the time difference of arrivals (TDOA) to detect the attacker and reduce detection errors using heuristic techniques. Differential evolution (DE) and cuckoo search (CS) algorithms are utilized to optimize detection accuracy. Simulation results confirm that the proposed CS algorithm outperforms and gives better performance as compared to the DE algorithm.

Wellbore fracture recognition and fracture parameter identification method using piezoelectric ultrasonic and machine learning

Real-time monitoring of wellbore status information can effectively ensure the structural safety of the wellbore and improve the drilling efficiency. It is especially important to recognize the wellbore fractures and identify their parameters, which motivates us to propose a wellbore fracture recognition and parameter identification method using piezoelectric ultrasonic and machine learning. To realize a self-model emission detection, we innovatively utilize a single transducer to act as both an actuator and a sensor, allowing for the efficient acquisition of ultrasonic echo signals of the wellbore. For fracture recognition, we use the wavelet packet transform to extract features from the ultrasonic echo signal, while constructing a convolutional neural network model for fracture recognition. Then, we establish the relationships between the fracture width-depth parameter and the echo signal, including the peak value as well as the arrival time difference. The experimental results show that the proposed method effectively recognizes the fractures from the ultrasonic echo signal of the wellbore. At the same time, the established function truly reflects the relationship between the fracture parameters and the echo signal. Therefore, the proposed method can provide an identification function for quantitative monitoring of wellbore fracture parameters. Moreover, the functions can be used as a reference for other structural health monitoring, which has good application prospects.

Automatic Dependent Surveillance-Broadcast Deceptive Jamming Detection Method Based on Track Data

The characteristics of automatic dependent surveillance-broadcast (ADS-B) technology, such as unencrypted channels and open data protocols, make it extremely vulnerable to various intentional or unintentional spoofing and jamming, which seriously threatens air traffic safety. Traditional approaches do not update their high-bit data for a long time, which causes the situations of the same encoding results to have different aircraft positions and it is difficult to determine the real position of the aircraft and effectively detect ADS-B jamming. Therefore, it is urgent to propose more effective detection methods for ADS-B spoofing and jamming. First, the methods and principles of frequency shift calculation and automatic monitoring algorithm are introduced. Then, signal consistency detection and error robustness are analyzed, and a track-data-based detection model for abnormal ADS-B data are proposed. The track-data-based detection methods can use multi-point sampling judgment algorithm to realize the reception and processing of data bits. This method makes full use of the information of multiple sampling values of each data bit and the reference power value obtained in the preamble detection, and determines the data value and confidence value through the relationship between the two. The detection methods for ADS-B spoofing and jamming distinguishes the true and false signals and the authenticity of target positions by comparing the arrival time difference of each signal at the ground station. Finally, the research results show that, with the increase of signal to noise ratio, no matter the estimation based on virtual array translation or the estimation based on circular stationary signal, their estimation accuracies are improved with gradually increasing trends.

High-Precision Joint TDOA and FDOA Location System

Passive location based on TDOA (time difference of arrival) and FDOA (frequency difference of arrival) is the mainstream method for target localization. This paper proposes a fast time–frequency difference positioning method to address issues such as low accuracy, large computational resource utilization, and limited suitability for real-time signal processing in the conventional CAF (cross-ambiguity function)-based approach, aiming to complete the processing of the target radiation source to obtain the target parameters within a short timeframe. In the mixing product operation step of the CAF, a frequency-domain approach replaces the time-domain convolution operation in PW-ZFFT (pre-weighted Zoom-FFT) to reduce the computational load of the CAF. Additionally, a quadratic surface fitting method is used to enhance the accuracy of TDOA and FDOA. The localization solution is obtained using Newton’s method, which can provide more accurate results compared to analytical methods. Next, a signal processing platform is designed with FPGA (field-programmable gate array) and multi-core DSP (digital signal processor), and works by dividing and mapping the algorithm functional modules according to the hardware’s characteristics. We analyze the architectural advantages of multi-core DSP and design methods to improve program performance, such as EDMA transfer optimization, inline function optimization, and cache optimization. Finally, this paper constructs simulation tests in typical positioning scenarios and compares them to hardware measurement results, thus confirming the correctness and real-time capability of the program.

Tracking solar radio bursts using Bayesian multilateration

Solar radio bursts (SRBs), such as Type IIs and IIIs, are emitted by electrons propagating through the corona and interplanetary space. Tracking such bursts is key to understanding the properties of accelerated electrons and radio wave propagation as well as the local plasma environment that they propagate through. In this work, we present a novel multilateration algorithm called BayEsian LocaLisation Algorithm (BELLA) and validate the algorithm using simulated and observed SRBs. In addition, apparent SRB positions from BELLA are compared with comparable localisation methods and the predictions of solar wind models. BELLA uses Bayesian inference to create probabilistic distributions of source positions and their uncertainties. This facilitates the estimation of algorithmic, instrumental, and physical uncertainties in a quantitative manner. We validated BELLA using simulations and a Type III SRB observed by STEREO A and STEREO B at pm 116 degr from the Sun-Earth line and by Wind at L1. BELLA tracked the Type III source from sim 10--150 $R_ (2--0.15 MHz) along a spiral trajectory. This allowed for an estimate of an apparent solar wind speed of sw 400 km $ and a source longitude of $ 30degr . We compared these results with well-established methods of positioning: Goniopolarimetric (GP), analytical time-difference-of-arrival (TDOA), and Solar radio burst Electron Motion Tracker (SEMP). We found them to be in agreement with the results obtained by BELLA. Additionally, the results aligned with solar wind properties assimilated by the Heliospheric Upwind Extrapolation with time dependence (HUXt) model. We have validated BELLA and used it to identify apparent source positions as well as velocities and densities of the solar wind. Furthermore, we identified higher than expected electron densities, suggesting that the true emission sources were at lower altitudes than those identified by BELLA, an effect that may be due to appreciable scattering of electromagnetic waves by electrons in interplanetary space.

Improved Damage Mapping with Hyperbola Approach for Guided Waves Based Structural Health Monitoring Using Fiber Bragg Grating sensors

Ultrasonic guided waves (GW) are commonly used in aerospace, civil, and mechanical industries for inspecting the health of a structure non-destructively. Traditionally the piezoelectric based actuators and sensors have been used for the actuation and sensing of GW. But the use of fiber Bragg grating sensors (FBG) is on the increase due to the unique opportunities offered by these sensors. There are several techniques that are used for damage mapping. The most commonly used in reflection based assessment domain are the ellipse based and hyperbola based approaches. Hyperbola approach is based on the time difference of arrival (TDOA). The time difference in the arrival time for two sensors with a common actuator is used for obtaining the hyperbola. The superposition of these hyperbolas for all possible actuator and sensor pairs may be used for damage localization. In this paper a new improved hyperbola approach for damage mapping is proposed by combining it with the ellipse based approach. In order to limit the number of computations a binary variable is introduced to check if the pixel fulfils the hyperbola and ellipse conditions. So only the regions of the structure which fulfil both conditions are mapped. This limits the number of hyperbolas which are identified as possible damage and improves the damage localization. The methodology is employed on a simple aluminium plate with acoustically FBG sensors. The performance of the new approach is compared with existing methods. The results indicate that indeed the improved hyperbola approach improves the localization. Also the computational load is reduced allowing more real time damage mapping.

Confidence intervals of location for marine mammal calls via time-differences-of-arrival: Sensitivity analysis.

Confidence intervals of location (CIL) of calling marine mammals, derived from time-differences-of-arrival (TDOA) between receivers, depend on errors of TDOAs, receiver location, clocks, and sound speeds. Simulations demonstrate a time-differences-of-arrival-beamforming-locator (TDOA-BL) yields CIL in error by O(10-100) km for experimental scenarios because it is not designed to account for relevant errors. The errors are large and sometimes exceed the distances of detection. Another locator designed for all errors, sequential bound estimation, yields CIL always containing the true location. TDOA-BL have and are being used to understand potential effects of environmental stress on marine mammals; a use worth reconsidering.

Audio-Visual Bimodal Combination-Based Speaker Tracking Method for Mobile Robot

Initiative service is a key research direction for the new generation of service robots. It is important to automatically track humans for initiative service in human-robot interaction. To solve the problems of low precision and poor anti-interference capability of only using single-modal (audio or visual) information, a speaker positioning and tracking method based on an audio-visual bimodal combination is proposed. First, the azimuth of the speaker is obtained based on the time difference of arrival using a microphone array, and face detection based on AdaBoost is carried out using the camera. A distance and azimuth calculation model is established to obtain the position of the speaker. Second, a speaker positioning strategy based on an audio-visual bimodal combination is designed to handle different situations. Third, the path is planned by which the azimuth and distance between the robot and the speaker are maintained in a limited range. Different azimuths and distances for speaker tracking are set to perform various simulations. Finally, the mobile robot is driven to follow the path using the STM32 real-time control system. Information from the microphone array and the camera is collected and processed by Raspberry Pi. The tracking accuracy was tested under a single-face situation by setting 20 different target points, and 10 tests were carried out under each point. Under multi-face situations, the audio-visual bimodal information is combined to identify the speaker, and then the Kalman filter is used in face tracking. The experimental results demonstrate that the running trajectory of the mobile robot is close to the ideal trajectory, which ensures effective speaker tracking.