Arrival Of Signals Research Articles

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.

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.

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.

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.

Spatiotemporal Cooperative Spectrum Sensing of Random Arrival of Primary Signals for Cognitive UAV Communications

Spatiotemporal Cooperative Spectrum Sensing of Random Arrival of Primary Signals for Cognitive UAV Communications

Unsupervised Learning-Based Measurement of Ultrasonic Axial Transmission Velocity in Neonatal Bone.

To develop a robust algorithm for estimating ultrasonic axial transmission velocity from neonatal tibial bone, and to investigate the relationships between ultrasound velocity and neonatal anthropometric measurements as well as clinical biochemical markers of skeletal health. This study presents an unsupervised learning approach for the automatic detection of first arrival time and estimation of ultrasonic velocity from axial transmission waveforms, which potentially indicates bone quality. The proposed method combines the ReliefF algorithm and fuzzy C-means clustering. It was first validated using an in vitro dataset measured from a Sawbones phantom. It was subsequently applied on in vivo signals collected from 40 infants, comprising 21 males and 19 females. The extracted neonatal ultrasonic velocity was subjected to statistical analysis to explore correlations with the infants' anthropometric features and biochemical indicators. The results of in vivo data analysis revealed significant correlations between the extracted ultrasonic velocity and the neonatal anthropometric measurements and biochemical markers. The velocity of first arrival signals showed good associations with body weight (ρ = 0.583, P value <.001), body length (ρ = 0.583, P value <.001), and gestational age (ρ = 0.557, P value <.001). These findings suggest that fuzzy C-means clustering is highly effective in extracting ultrasonic propagating velocity in bone and reliably applicable in in vivo measurement. This work is a preliminary study that holds promise in advancing the development of a standardized ultrasonic tool for assessing neonatal bone health. Such advancements are crucial in the accurate diagnosis of bone growth disorders.

Research on direction of arrival estimation for mixed broadband signals

In practical applications, the signals received by a hydrophone array include coherent and incoherent signals, while estimating the direction of arrival (DOA) of coherent or incoherent signals has been studied for simplicity with poor performance for a mixed signal. The present study proposes a novel approach for estimating the DOA of mixed broadband signals to solve this problem, namely the oblique method. The unitary oblique method is simultaneously proposed to improve operational efficiency with the complex matrix shifted into a real valued matrix through a unitary transform. For independent signals, the incoherent signal subspace method (ISSM) is used to estimate the DOA. Matrix difference and oblique projection methods are adopted for multi-group coherent signals in order to eliminate the influence of incoherent signals and other groups of coherent signals. For broadband signals, the Tasseled Cap Transformation focusing transform is utilized to convert the broadband signal to the focusing frequency, and then the high-resolution Multiple Signal Classification algorithm method is employed to solve the problem. Simulation results indicate that the oblique method reconciles DOA estimation performance and computational efficiency and can be extended to other types of arrays.

High-resolution 2D-DoA sequential algorithm of azimuth and elevation estimation in automotive distributed system of coherent MIMO radars

Objectives. One of the main tasks of radiolocation involves the problem of increasing spatial resolution of the targets in the case of limited aperture of the radar antenna array and short length of time samples (snapshots). Algorithms must be developed to provide high angular resolution and low computational complexity. In order to conform with the existing Advanced Driver Assistance Systems requirements, modern cars are equipped with more than one radar having a common signal processing scheme to improve performance during target detection, positioning, and recognition as compared to a single radar. The present study aims to develop a two-dimensional Direction-of-Arrival algorithm with low computation complexity as part of distributed coherent automotive radar system for cases involving short time samples (snapshots).Methods. A virtual antenna array formation algorithm is formulated according to the two-dimensional Capon method. A proposed modification of two-dimensional Capon algorithm is based on sequentially estimating the directions of arrival for the distributed radar system. The Monte Carlo method is used to compare the effectiveness of the considered algorithms.Results. The 2D-DoA sequential algorithm of azimuth and elevation estimation is proposed. The comparative analysis results for the developed algorithm and classical 2D Capon method based on numerical simulation using Monte Carlo method are presented. The proposed scheme of DoA estimation for coherent signal processing of distributed radars is shown to lead to an improvement of the main considered metrics representing the probability of correctly estimating the number of targets, mean square error, and square error compared to a single radar system. The proposed low-computational algorithm shows the gain in complexity compared to full 2D Capon algorithm.Conclusions. The proposed two-stage algorithm for estimating the directions of arrival of signals in azimuth and elevation planes can be applied to the distributed system of coherent radars with several receiving and transmitting antennas representing multiple input multiple output (MIMO) radars. The algorithm is based on sequentially estimating the directions of arrival, implying estimation in the azimuthal plane at the first stage and estimation in the vertical plane at the second stage. The performance of a coherent radar system with limited antenna array configuration of separate radar is close in characteristics to a high-performance 4D-radar with a large antenna array system.

Angle of Arrival Estimator Utilizing the Minimum Number of Omnidirectional Microphones

In sound signal processing, angle of arrival indicates the direction from which a propagating sound signal arrives at a point where multiple omnidirectional microphones are positioned. Considering a small underwater platform (e.g., underwater unmanned vehicle), this article addresses how to estimate a non-cooperative target’s signal direction utilizing the minimum number of omnidirectional microphones. It is desirable to use the minimum number of microphones, since one can reduce the cost and size of the platform by using small number of omnidirectional microphones. Suppose that each microphone measures a real-valued sound signal whose speed and frequency information are not known in advance. Since two microphones cannot determine a unique AOA solution, this study presents how to estimate the angle of arrival using a general configuration composed of three omnidirectional microphones. The effectiveness of the proposed angle of arrival estimator utilizing only three microphones is demonstrated by comparing it with the state-of-the-art estimation algorithm through computer simulations.

Localization of a BLE Device Based on Single-Device RSSI and DOA Measurements

Indoor location services often use Bluetooth low energy (BLE) devices for their low energy consumption and easy implementation. Applications like device monitoring, ranging, and asset tracking utilize the received signal strength (RSS) of the BLE signal to estimate the proximity of a device from the receiver. However, in multipath environments, RSS-based solutions may not provide an accurate estimation. In such environments, receivers with antenna arrays are used to calculate the difference in time of flight (ToF) and therefore calculate the direction of arrival (DoA) of the Bluetooth signal. Other techniques like triangulation have also been used, such as having multiple transmitters or receivers as a network of sensors. To find a lost item, devices like Tile© use an onboard beeper to notify users of their presence. In this paper, we present a system that uses a single-measurement device and describe the method of measurement to estimate the location of a BLE device using RSS. A BLE device is configured as an Eddystone beacon for periodic transmission of advertising packets with RSS information. We developed a smartphone application to read RSS information from the beacon, designed an algorithm to estimate the DoA, and used the phone’s internal sensors to evaluate the DoA with respect to true north. The proposed measurement method allows for asset tracking by iterative measurements that provide the direction of the beacon and take the user closer at every step. The receiver application is easily deployable on a smartphone, and the algorithm provides direction of the beacon within a 30° range, as suggested by the provided results.

Space-code division multiple access for broadband acoustic networks

We present an investigation into the design of an acoustic communication network, where multiple users are distributed across space and transmit and receive simultaneously in the same band to and from a common base station. Specifically, we focus on a system that utilizes orthogonal frequency division multiplexing as a modulation method, and allows users to transmit and receive in either synchronous or asynchronous fashion. To distinguish between users on the uplink, the base station employs a combination of code-division and space-division multiple access. The base station iteratively steers a beam to each stable propagation path of the desired user’s channel while placing nulls in the direction of other paths, as well as in the directions of interfering users. Finally, the multiple paths of the desired user are recombined before data detection. The process is repeated for each user. Broadband beamforming is employed to account for the broadband nature of acoustic signals. The beamformer coefficients on each carrier depend on the angles of signal arrivals, which are estimated during the uplink transmission and used to construct both the uplink and the downlink beamformer. On the downlink, the base station utilizes angle information to assemble beamforming weights and point in the direction of stable paths of the users. It superimposes multiuser signals and transmits the sum signal to the users. The signal intended for a given user reaches only that user, requiring just a simple detector. Each user is equipped by a single-element transducer. To demonstrate the design concepts, we conducted simulations using a shallow water channel model and performed experimental over-the-air tests in an indoor environment using an acoustic communications testbed. The results were excellent, thereby encouraging future implementations in practical systems.

Simple DFS and AOA simultaneous measurement scheme without directional ambiguity with co-frequency self-interference signal cancellation

A photonic method based on a dual-polarization dual-parallel Mach–Zehnder modulator (DPol-DPMZM) for the simultaneous measurement of the Doppler frequency shift (DFS) and angle of arrival (AOA) of microwave signals is proposed and demonstrated by simulation. The upper arm of each sub-DPMZM is driven by the echo and self-interference signals from the antenna, while the lower arm is driven by the reference signal 1 and reference signal 2. The phase and amplitude of the reference signal 1 are adjusted to match the interference signals for achieving the self-interference cancellation (SIC). At the central office (CO), the DFS and AOA can be acquired in real time without directional ambiguity by processing the two downconverted low-frequency tones in the photocurrent. The simulation results show that the presence of the SI signal will seriously interfere with the observation of the SOI frequency and waveform, and the self-interference cancellation depth of about 42 dB can be obtained after the SIC. The measurement errors of the DFS without direction ambiguity are within 0.2 Hz. After the Hilbert transformation of the intermediate frequency (IF) signal, the AOA can be measured from −87.31∘ to +87.31∘ with errors less than 3.9°. The system has a large bandwidth, excellent real-time performance, and better invisibility, and is expected to be used in modern electronic warfare systems.

Enhancing Damage Detection in 2D Concrete Plates: A Comprehensive Study on Interpolation Methods and Parameters

In an era marked by increasing demands for stability and durability in construction, the importance of damage detection in concrete structures cannot be overstated. As these structures underpin the safety and longevity of vital assets, this paper embarks on a comprehensive exploration of methodologies to enhance precision and reliability in 2D concrete plate damage detection. By focusing on the interpolation of damage index values and leveraging the insights gained from energy loss analysis and the characterization of the time of arrival of signals, we address the pressing need for improved non-destructive damage detection techniques. Our study encompasses a range of simulation attempts, each involving various interpolation parameters, and systematically evaluates their performance. The culmination of this research identifies the most effective combination of techniques and parameters, leading to the best results in damage detection. This multidimensional investigation promises to provide valuable contributions to the field of structural health monitoring, benefiting both researchers and practitioners engaged in the evaluation of concrete structures.