Arrival Of Signals Research Articles

A DAQ system with low-dead-time, high-precision TDC for APD detection efficiency calibration

Correlated photon calibration based on spontaneous parametric down-conversion (SPDC) provides a highly precise means to calibrate the detection efficiency of avalanche photodiode (APD). During the calibration process of detection efficiency via SPDC, precise measurement of the arrival time of correlated photons and accurate photon counting are essential. To achieve this goal, a data acquisition (DAQ) system with low-dead-time, high-resolution Time-to-Digital Converter (TDC) was designed in this paper. This TDC is designed based on tapped delay line (TDL), which is implemented on Xilinx Kintex-7 series field programmable gate array (FPGA). This TDC can accurately measure the time of arrival of input signals. Upon arrival of a photon signal, the TDC rapidly generates a timestamp to record the arrival time of the photon signal. Utilizing these timestamps, time delay and accurate measurements of time intervals can be achieved. The specially designed TDC input stage structure and encoding algorithm enable the alternating propagation and sampling of `01' and `10' transitions on TDL. Coupled with a pipelined architecture, the TDC's dead time is close to one clock cycle, which is 2.33 ns in the current implementation version. The structure of mode recognition and triggering ensures that the TDC can correctly calibrate time measurements and count photon counts. Experimental results show that the TDC achieves an RMS precision of better than 11.08 ps, and this measurement precision is maintained over long time intervals (0–10 us). The reliability of this DAQ for photon counting has been verified through standard signal sources.

Устранение неоднозначности оценок направлений прихода широкополосных сигналов в многосигнальном режиме пеленгования

Direction finding of wideband signals overlapping in the spectral domain by a fixed antenna array requires the correction of steering vector ambiguity and phase differences corresponding to different spectral components. The spectral components of the same signal correspond to unequal values of the distance in wavelengths between the antennas in the array. A method of steering vector ambiguity correction suitable for subspace methods such as MUSIC and ESPRIT is proposed. The ambiguity in the presence of multiple signals is resolved by correcting phase diffe-rences between antennas for each spectral component, with preliminary correlation of the ambiguous steering vector with unambiguous ones obtained for the low-frequency range of the spectrum using maximum correlation. The corrected steering vector for each spectral component is obtained from the adjusted phase differences between the antennas and is used in the direction finding correlation algorithm to obtain unambiguous estimates of the directions of arrival of the wideband signals. Simulation results for direction finding of five sound wideband signals using a square internally filled antenna array consisting of 16 antennas, based on ESPRIT with the proposed method of ambiguity correction, are presented. The proposed method for correcting the steering vector ambiguity caused by different spectral components of wideband signals in the antenna array allows estimation of their directions of arrival, as is done for narrowband radio signals.

UWB Chaotic Pulse-Based Ranging: Time-of-Flight Approach

Nowadays, indoor positioning using ultra-wideband (UWB) signals is actively being developed with the aim of implementing existing ideas and solutions, improving their performance, and searching for new measurement schemes. This paper proposes an approach to estimating the distance between wireless nodes by measuring radio signal propagation time using UWB chaotic radio pulses and UWB transceivers. This type of signal is a simple and practically interesting alternative to radio carriers of other types of UWB signals, which are based on packets of pulses (usually ultra-short pulses). The practical interest is caused by the noise-like nature of chaotic radio pulses, as well as their immunity to multipath fading and ease of generation. The aim of this work is to analyze such a system and identify the fundamental limitations inherent in the proposed approach. This paper describes a wireless system for measuring the signal propagation time based on the envelope of chaotic radio pulses using the SS-TWR (Single-Sided Two-Way Ranging) method. A difference scheme is used to determine the range. The characteristics of the proposed system are studied experimentally. The factors related to the threshold scheme for determining the time of arrival of a radio signal that introduce a systematic error into the measurement results are revealed, and approaches to correcting their influence are proposed.

Device‐Free Detection of Pedestrian and Movement Direction Using Bluetooth Direction Finding Function

From Bluetooth 5.1, a direction finding function has been added that allows the receiver to measure the angle of arrival (AoA) of the wireless signal from the sender. In this letter, we propose a method for detecting pedestrians and their movement direction passing between Bluetooth devices equipped with direction finding functions. As a result of the evaluation, pedestrians could be detected with higher accuracy (F‐score 0.929) by combining received signal strength indicator and AoA than when using each alone. In addition, the accuracy rate for estimating the movement direction using AoA was 0.704. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Preliminary detection of seismic signal arrival by polarization feature

Research is aimed at developing methodological principles for preliminary detection of the seismic signal arrival registered by a three-component seismic station (TCSS), taking into account polarization properties of background and signal components. Methods. Seismic signals were recorded using the GURALP CMG seismic observation network of the Main Special Control Center (MSCC) of the State Space Agency (SSA) of Ukraine. Result. The main difference between a signal component of a three-component seismic record and a background is polarization properties. Considering these characteristics makes it possible to detect seismic signals and determine their components. Traditional methods for analyzing polarization in a three-component seismic record often involve significant computational effort and are typically employed for processing and analyzing seismic data in real time. In this study, we propose a new approach that evaluates the linearity of the implemented methods and determines the angles of seismic wave arrivals. This is particularly crucial for monitoring potential emergency sources, such as hazardous objects and seismically active areas. Our method can also be applied in real-time scenarios. Scientific novelty. Considering the properties of polarization, as opposed to relying solely on amplitude detection criteria, enables the detection of signals with a lower signal-to-noise ratio. This increases the sensitivity of the Transient Coherent Seismic Source (TCSS) to magnitudes. By utilizing polarization analysis in seismic signal detection, we not only enhance detection capabilities but also gain additional information about the parameters of seismic signal components, such as their azimuth and angle of arrival at the surface. This information can be instrumental in identifying the seismic signal components and determining the location of the seismic event source in relation to the observation point (OP). Significance of research. This approach makes it possible to increase the magnitude sensitivity of OP and the observation system as a whole. The relative simplicity of implementation makes it possible to apply it in real time. Determining angular characteristics of seismic wave arrival allows applying the proposed approach in a continuous monitoring loop for potential emergency sources.

Semi picking: a semi-supervised arrival time picking for microseismic monitoring based on the TransUGA network combined with SimMatch

Summary An accurate and efficient method for picking the first arrival of microseismic signals is crucial for processing microseismic monitoring data. However, the weak magnitude and low signal-to-noise ratio (SNR) of these signals make picking arrivals challenging. Recent advancements in deep learning-based methods for picking the first arrivals of microseismic signals have effectively addressed the inefficiencies and inaccuracies of traditional methods. Nevertheless, these methods often require many training samples, and the substantial size and labelling effort significantly hinder the development of deep learning-based first-arrival picking methods. This study introduces Semi-Picking: a semi-supervised method for picking the first arrival of microseismic signals, utilising the TransUGA network and SimMatch. This approach automatically labels microseismic signals following sample augmentation by establishing a semi-supervised learning framework, significantly reducing the time required for sample labelling. Initially, the TransUNet model is enhanced by incorporating the Self-Supervised Predictive Convolutional Attention Block (SSPCAB) module to create a Deep-TransUNet architecture, which more effectively separates signal from noise in microseismic signals with low SNR and improves the accuracy of first-arrival picking. Subsequently, the datasets for this study are compiled from microseismic traces collected from field monitoring records. Finite-difference forward modelling is applied to the microseismic data to train the network, and hyperparameter tuning is performed to optimise the UGATIT and Deep-TransUNet architecture. The outcomes of the arrival-picking experiments, conducted under conditions of low SNR using both synthetic and real microseismic records, demonstrated that Semi-Picking offers robust resistance to incorrect labels. This resilience stems from the synergistic use of the semi-supervised learning framework and self-attention mechanisms. The proposed method demonstrates superiority over the TransUNet, the SSPCAB-TransUNet, the UNet++, and the traditional STA/LTA method, respectively, with the picking error rate of the Semi-Picking Net being less than 0.1 s. The proposed method outperforms the commonly used deep learning-based approaches for picking the first arrivals of microseismic signals, exhibiting superior performance.

A Serial Sampling Closed‐Loop System for Transceiver Beam Nulling

ABSTRACTConsidering the limited space, power of the unmanned platform, and the inconsistency of the antenna array transceiver beam pattern, we design a serial sampling closed‐loop system for transceiver beam nulling, which has a strong robustness and global convergence. It is mathematically proved that the serial sampling closed‐loop system is capable of adaptively nulling jamming signals by analyzing the system mathematical model for the first time. Our work addresses the issue that the existing transceiver system on unmanned platforms cannot estimate the directions of arrival (DOAs) of jamming signals causing the transmit beam not to be nulled in the DOAs of jammers. Simulation results show that the proposed system can be applied in scenarios with strictly limitations on space, weight, and power (SWaP) to suppress jamming signals when receiving desired signals while covering the transmissions.

Vibrational noise disrupts Nezara viridula communication, irrespective of spectral overlap

Insects rely on substrate vibrations in numerous intra- and interspecific interactions. Yet, our knowledge of noise impact in this modality lags behind that in audition, limiting our understanding of how anthropogenic noise affects insect communities. Auditory research has linked impaired signal perception in noise (i.e., masking) to spectral overlap. We investigated the impact of noise with different spectral compositions on the vibrational communication of the stink bug Nezara viridula, examining courtship behaviour and signal representation by sensory neurons. We found negative effects of vibrational noise regardless of spectral overlap, challenging common expectations. Noise impaired the ability of males to recognize the female signal and localise its source: overlapping noise decreased sensitivity of receptor neurons to the signal and disrupted signal frequency encoding by phase-locking units, while non-overlapping noise only affected frequency encoding. Modelling neuronal spike triggering in sensory neurons linked disrupted frequency encoding to interference-induced alterations of the signal waveform. These alterations also affected time delays between signal arrivals to different legs, crucial for localisation. Our study thus unveils a new masking mechanism, potentially unique to insect vibrosensory systems. The findings highlight the higher vulnerability of vibration-mediated behaviour to noise, with implications for insect interactions in natural and anthropogenically altered environments.

Improved MER algorithm for lost circulation detection using transient pressure waves

In order to solve the difficult problem of locating the leakage layer in the bare eye well section during drilling, this study proposes a transient pressure wave well leakage detection method based on the modified energy ratio (MER) analysis. The method makes clever use of the similarity between the transient pressure waveform and the first arrival of the microseismic signal, and accurately captures the sudden change moments of the pressure wave to obtain the time difference of the signal through the MER method, and combines with the wave velocity of the pressure wave to achieve the precise location of the leakage layer. To address the problem of high noise content in transient pressure wave signals, this study proposes an improved variational modal decomposition (VMD) adaptive denoising method, which effectively removes the noise components and retains the key features of the signals to the maximum extent, and the signal-to-noise ratio of the signal after noise reduction can be up to 15.64. The experimental results show that the leaky layer localisation error of the method ranges from 0.13% to 5.30%. Under the same conditions, the accuracy of the MER localisation method is better than that of the wavelet mode maxima method, the frequency domain method and the short-time averaging/long-time averaging (STA/LTA) method, and the localisation error rate can be as low as 2.11%. The transient pressure wave well leakage detection method based on the improved MER algorithm provides a low-cost, high-precision and efficient solution for well leakage detection during drilling.

The influence of atmospheric turbulence-induced optical intensity scintillation on the accuracy of satellite-to-ground laser one-way timing

This paper elucidates the mechanism by which atmospheric turbulence leads to errors in satellite-to-ground laser one-way timing, investigates the influence of optical intensity scintillation on the time delay jitter of laser signal arrival in single-satellite-to-ground transmission link. Furthermore, the impact patterns and the magnitude of atmospheric turbulence-induced one-way timing errors and positioning errors across various gradients of observation zenith angles in the GPS four-satellite-to-ground transmission link is also investigated. Besides, the study also explores the impact patterns and magnitude ranges of the effects of optical intensity scintillation on the four-satellite-to-ground time transfer system under different observed zenith angles with similar geometric dilution of precision (GDOP) conditions. The results show that smaller receiver zenith angles result in reduced errors caused by atmospheric turbulence-induced optical intensity scintillation, leading to higher accuracy in four-satellite positioning. When the GDOP factors range from 6.5 to 7.5, the timing errors for multiple sets of satellites consistently exhibit a normal distribution. Specifically, the four-satellite-to-ground laser one-way time transfer deviation averages around 2 ns, with jitter remaining within 1 ns.

Indoor localization algorithms based on Angle of Arrival with a benchmark comparison

Indoor localization is crucial for developing intelligent environments capable of understanding user contexts and adapting to environmental changes. Bluetooth 5.1 Direction Finding is a recent specification that leverages the angle of departure (AoD) and angle of arrival (AoA) of radio signals to locate objects or people indoors. This paper presents a set of algorithms that estimate user positions using AoA values and the concept of the Confidence Region (CR), which defines the expected position uncertainty and helps to remove outlier measurements, thereby improving performance compared to traditional triangulation algorithms. We validate the algorithms with a publicly available dataset, and analyze the impact of body orientation relative to receiving units. The experimental results highlight the limitations and potential of the proposed solutions. From our experiments, we observe that the Conditional All-in algorithm presented in this work, achieves the best performance across all configuration settings in both line-of-sight and non-line-of-sight conditions.

Machine learning-based intelligent localization technique for channel classification in massive MIMO

Multiple-input multiple-output (MIMO) technology has been widely adopted in wireless communications, which enables the simultaneous transmission of multiple data streams via multiple transmitting and receiving antennas. In a MIMO system with non-line-of-sight (NLOS), transmitted signals are reflected by various obstacles along the path, reaching the antenna at different angles and times. In 5G networks, the NLOS problem is a major challenge for massive MIMO localization, significantly reducing positioning accuracy. In this work, an intelligent localization technique based on NLOS identification and mitigation is proposed to address this problem. In this solution, a Convolutional Neural Network (CNN) based hybrid Archimedes-based Salp Swarm Algorithm (HASSA) technique is proposed to detect NLOS or the line of sight (LOS) and estimate the location. The accuracy can be analyzed by considering the angle of arrival of signals, threshold-based time of arrival, and time difference of arrival from different antennas. A novel reinforcement learning-based optimization approach is used for the mitigation of NLOS in the radio wave propagation path, which in turn reduces the computational complexity. We use the Ensemble Deep Deterministic Policy Gradient-Based Approach (EDDPG)-based Honey Badger algorithm (HBA) for the aforementioned process. The simulation of this approach assesses diverse scenarios and considers different parameters, and the approach is compared with various state-of-the-art works. From the simulation results, our proposed approach can be used for the identification and detection of LOS and NLOS components and can precisely enhance the localization compared with other approaches.

Concept study of a storage ring-based gravitational wave observatory: Gravitational wave strain and synchrotron radiation noise

This work for the first time addresses the feasibility of measuring millihertz gravitational waves (mHz GWs) with a storage ring-based detector. While this overall challenge consists of several partial problems, here we focus solely on quantifying design limitations imposed by the kinetic energy and radiated power of circulating ions at relativistic velocities. We propose an experiment based on the measurement of the time-of-flight signal of an ion chain. One of the dominant noise sources inherent to the measurement principle for such a GW detector is the shot noise of the emitted synchrotron radiation. We compute the noise amplitude of arrival time signals obtained by analytical estimates and simulations of ions with different masses and velocities circulating in a storage ring with the circumference of the Large Hadron Collider. Thereby, we show that our experiment design could reduce the noise amplitude due to the synchrotron radiation in the frequency range 10−4–10−2 Hz to one or two orders of magnitude below the expected GW signals from of astrophysical sources, such as supermassive binary black holes or extreme mass-ratio inspirals. Other key requirements for building a working storage ring-based GW detector include the generation and acceleration of heavy ion chains with the required energy resolution, their injection and continued storage, as well as the detection method to be used for the determination of the particle arrival time. However, these are not the focus of the work presented here, in which we instead concentrate on the definition of a working principle in terms of ion type, kinetic energy, and ring design, which will later serve as a starting point when addressing a more complete experimental setup. Published by the American Physical Society 2024

A threshold activation-based simplified Lv's transform algorithm for transient multi-component linear frequency modulation signals analysis.

The high sampling rate in modern digital systems generates a large scale of data. To address the computational burden, this paper proposes a threshold activation-based simplified Lv's transform (SLVT) algorithm to analyze the transient multi-component linear frequency modulation signals. Only the signal arrival can trigger the signal analysis. This mechanism alleviates the computation pressure because of the sparsity of signals. The threshold activation mechanism enables transient signal detection and sampling rate adjustment, thereby enhancing the efficiency and effectiveness of the analytical process. The simplified Lv's transform (LVT) removes redundant computations on stretch weighting and the Discrete Fourier Transform (DFT) in Lv's transform. SLVT uses the efficient Bluestein chirp-z algorithm to implement the stretch keystone transform. The comparison results show that SLVT reduces the computational complexity of the original LVT by at least 30.8%. This algorithm exhibits superior performance compared to other advanced signal processing methods, such as discrete chirp Fourier transform, fractional Fourier transform, and Radon Wigner transform algorithms, in terms of parameter extraction accuracy, computational complexity, and execution time. Moreover, the implementation of a field programmable gate array accelerates SLVT computing by a factor of 116 in comparison to the CPU (Central Processing Unit) platform.

Quasi-likelihood estimation of the arrival time of an ultra-wideband quasi-radio signal with unknown amplitude and initial phase

The problem of estimating the time of arrival of a signal is relevant in the fields of radio and hydrolocation, radio astronomy, remote observation and sensing, etc. The useful signal is often described by a quasi-deterministic function of time, which can be regular or discontinuous. In recent years, problems of processing ultra-wideband signals have been of significant interest. Among the many UWB signals, it is customary to distinguish a separate class of ultra-wideband quasi-radio signals, the structure of which is similar to the structure of narrowband radio signals, but the condition of relative narrowband for which may not be met. Algorithms for estimating the arrival time of regular ultra-wideband quasi-radio signals have been studied in the existing literature. In this case, the synthesis and analysis of an algorithm for quasi-plausible estimation of the arrival time of a discontinuous signal of finite duration and arbitrary shape is of practical interest. The purpose of the work is to synthesize and analyze a quasi-likelihood algorithm for estimating the time of arrival of a discontinuous ultra-wideband quasi-radio signal with an unknown amplitude and initial phase against a background of white Gaussian noise. In this work, closed asymptotically exact expressions for the bias and scattering of the estimate are obtained, which make it possible to study the effectiveness of the synthesized algorithm, as well as compare its effectiveness with special cases of signal models - narrowband and wideband radio and video signals. The results of the study make it possible to determine losses in the accuracy of estimation the signal arrival time due to a priori ignorance of the initial phase and shape of the modulating function of the signal. Depending on the required accuracy of estimate the time of arrival of an ultra-wideband quasi-radio signal, it is possible to optimize the parameters of the transmitted signals and determine the limits of applicability of the designed radio system.

Testing the algorithm for determining the velocity model of the upper part of the medium based on the first times arrivals

The results of testing the algorithm and the software module created on its basis are presented. The algorithm allows us to build a velocity model of the medium upper part using the times of the first arrivals of seismic signals related to refracted waves. The module implements two possibilities of these times determination. The first involves determining the times before the module is starting. The second implements the automatic determination of such times from the original seismograms using neural networks.

SFA: A Robust Sparse Fractal Array for Estimating the Directions of Arrival of Signals

SFA: A Robust Sparse Fractal Array for Estimating the Directions of Arrival of Signals

Adapting UWB AoA estimation towards unseen environments using transfer learning and data augmentation

Ultra-wideband technology has become increasingly prevalent in localization systems, particularly with the emergence of multi-antenna systems capable of estimating both distance and angle of arrival (AoA) for incoming signals. However, most scientific research analyzes the accuracy of AoA in one specific environment for which the estimator is trained. In this paper, we analyze the performance of various AoA estimation algorithms, such as deep convolutional neural networks (DCNN), multiple signal classification (MUSIC), and phase difference of arrival (PDoA), in unseen environments. Prior work already demonstrated the superior performance of ML for AoA estimation compared to PDoA or MUSIC. We show that MUSIC, PDoA and ML solutions suffer from degradation in unseen environments at the 90th percentile of error, with ML-based AoA estimation degrading by about 14 degrees in unseen environments compared to 4 degrees for PDoA. We demonstrate that while PDoA more effectively corrects AoA at the median level in unseen environments, ML-based methods excel at correcting higher-percentile AoA errors, including outliers. Finally, we propose a novel framework to further improve the correction of AoA outliers for DCNN-based AoA estimators using data augmentation and transfer learning, resulting in a median angular error of only 5 degrees in unseen environments, even considering a field of view up to 90 degrees.

Direct position determination algorithm based on wideband array signal model in external illuminator system

Abstract Target location and tracking are among the most critical applications for external illuminator systems. Most traditional array signal direct position determination (DPD) techniques are based on narrowband signals. With the advancement of communication technology, various wideband signals are extensively utilized in communication systems, leading to the failure of some traditional DPD techniques. To address this issue, this article proposes a new DPD algorithm for wideband array signals. First, we suppress the direct wave (DW) interference in the received signal. Then, a cost function for the target coordinate is constructed based on the Maximum Likelihood (ML) criterion, and a phase matching matrix is created for the problem of unknown signal transmission time. Finally, the position estimation is obtained from the peak of the cost function. Simulation results demonstrate that the proposed algorithm outperforms traditional two-step localization methods based on the time difference of arrival of signals, particularly in cases where DW interference is present in the received signal.

First- and Second-Order Sliding Mode Control Design for Networked 2-D Systems Under Round-Robin Protocol.

This article investigates the sliding mode control (SMC) problem for a class of uncertain 2-D systems described by the Roesser models with a bounded disturbance. In order to reduce the communication usage between the controller and the actuators, it is supposed that only one actuator node can gain the access to the network at each sampling time along horizontal or vertical direction, where a proper 2-D round-robin protocol is designed to periodically regulate the access token and a set of zero-order holders (ZOHs) is employed to keep the other actuator nodes unchanged until the next renewed signal arrives. Based on a novel 2-D common sliding function, a token-dependent 2-D SMC scheme with first-order sliding mode is appropriately constructed to cope with the impacts from the periodic scheduling signal and the ZOHs. Furthermore, a novel super-twisting-like 2-D SMC scheme with second-order sliding mode is designed to improve the robustness against the bounded disturbance. By resorting to token-dependent Lyapunov-like function, sufficient conditions are obtained to guarantee the ultimate boundedness of the horizontal and vertical states as well as the 2-D common sliding function. For acquiring the optimized gain matrices, two searching algorithms are formulated to solve two optimization problems arising from finding optimized control performance. Finally, two comparative examples are exploited to demonstrate the effectiveness and the advantageous of the proposed first- and second-order 2-D SMC design schemes under round-robin scheduling mechanism.