Signal Source Localization Research Articles

Wavelet-based arcing signal source localization algorithm using a compact multi-square microstrip antenna

Ensuring power system safety involves effective arc fault detection and localization. Existing devices struggle to differentiate between normal and abnormal conditions, especially in confined spaces, posing precision challenges. Strategically placing antennas around the arc helps detect electromagnetic radiation, even in limited areas, enabling valuable data collection for real-time monitoring. To address these challenges, this paper proposes integrating experimental work using a compact multi-square microstrip antenna and signal processing techniques. The study compares the effectiveness of two signal processing approaches: Discrete Wavelet Transform (DWT), and Continuous Wavelet Transform (CWT). These techniques separate genuine arc signals from background noise by identifying unique characteristics and isolating the dominant frequency. The Time of Arrival (ToA) is measured and used in Least Square and Gauss-Jordan Elimination methods to calculate the arc source location. The outcomes illustrate the precision of the proposed model in detecting and pinpointing arc source signals, with error margins ranging from 0.0615 to 0.0713 m for the CWT technique, 0.0688 to 0.0789 m for the DWT technique, and 0.0799 to 0.0844 m from the actual signal captured. These results hold promise for enhancing the integration of experimental approaches in assessing arcing conditions, thereby addressing challenges in insulation systems.

Three-Dimensional Signal Source Localization with Angle-Only Measurements in Passive Sensor Networks

Some passive sensors can provide only relative angles of a signal source. To obtain the signal source location, multiple passive sensors can be constructed into a passive sensor network through communication links. This paper investigates the source localization problem with angle-only measurements in three-dimensional space. First, we present an intersection localization method, which estimates the target position by minimizing the sum of distances between lines formed by angle-only measurements. It has the same target position estimate as the widely used least-squares (LS) method, but with a lower computational cost. Furthermore, considering the differences in measurement accuracy of sensors, the weighted least-squares (WLS) algorithm can achieve better localization performance than the LS method. Unfortunately, since the coefficient matrix and the noise vector are correlated, the WLS method is biased. The bias-compensation WLS (BCWLS) method is also presented in this paper to reduce the bias by estimating the correlation between the coefficient matrix and the pseudolinear noise vector. To evaluate the performance of the presented algorithms, numerical simulations are conducted, indicating that the superiority of the intersection localization method in computational cost and the superiority of the BCWLS method in localization accuracy.

Semantic Communications for Efficient Spectrum Maps Construction and Signal Source Location

Semantic Communications for Efficient Spectrum Maps Construction and Signal Source Location

Free-induction-decay magnetic field imaging with a microfabricated Cs vapor cell.

Magnetic field imaging is a valuable resource for signal source localization and characterization. This work reports an optically pumped magnetometer (OPM) based on the free-induction-decay (FID) protocol, that implements microfabricated cesium (Cs) vapor cell technology to visualize the magnetic field distributions resulting from various magnetic sources placed close to the cell. The slow diffusion of Cs atoms in the presence of a nitrogen (N2) buffer gas enables spatially independent measurements to be made within the same vapor cell by translating a 175 μm diameter probe beam over the sensing area. For example, the OPM was used to record temporal and spatial information to reconstruct magnetic field distributions in one and two dimensions. The optimal magnetometer sensitivity was estimated to be 0.43 pT/H z within a Nyquist limited bandwidth of 500 Hz. Furthermore, the sensor's dynamic range exceeds the Earth's field of approximately 50 μT, which provides a framework for magnetic field imaging in unshielded environments.

Neurophysiological mechanisms underlying the differential effect of reward prospect on response selection and inhibition

Reward and cognitive control play crucial roles in shaping goal-directed behavior. Yet, the behavioral and neural underpinnings of interactive effects of both processes in driving our actions towards a particular goal have remained rather unclear. Given the importance of inhibitory control, we investigated the effect of reward prospect on the modulatory influence of automatic versus controlled processes during response inhibition. For this, a performance-contingent monetary reward for both correct response selection and response inhibition was added to a Simon NoGo task, which manipulates the relationship of automatic and controlled processes in Go and NoGo trials. A neurophysiological approach was used by combining EEG temporal signal decomposition and source localization methods. Compared to a non-rewarded control group, rewarded participants showed faster response execution, as well as overall lower response selection and inhibition accuracy (shifted speed-accuracy tradeoff). Interestingly, the reward group displayed a larger interference of the interactive effects of automatic versus controlled processes during response inhibition (i.e., a larger Simon NoGo effect), but not during response selection. The reward-specific behavioral effect was mirrored by the P3 amplitude, underlining the importance of stimulus–response association processes in explaining variability in response inhibition performance. The selective reward-induced neurophysiological modulation was associated with lower activation differences in relevant structures spanning the inferior frontal and parietal cortex, as well as higher activation differences in the somatosensory cortex. Taken together, this study highlights relevant neuroanatomical structures underlying selective reward effects on response inhibition and extends previous reports on the possible detrimental effect of reward-triggered performance trade-offs on cognitive control processes.

Bubble detection and identification based on the vibration response for the sodium water reaction

Real-time bubble behavior characterization and localization are critical in the chemical industry, especially for leakage of sodium-water reactions. This research investigates the properties of bubble signals under different gas injection rates and sodium-water reactions by employing a vibration-based approach. Specifically, the time-frequency and energy characteristics of bubble signals were studied by vibration response. The bubble signals excited by the two kinds of experiments have the same characteristic frequency band of 200–400 Hz, and the maximum energy is obtained at 250 Hz. The bubble signal from the sodium-water reaction can be effectively recognized by identifying the differences in the characteristics of multiple parameters when the gas injection rate is 0.07 m3/h with a signal-to-noise ratio of 11.8–17.1 dB. On the basis of energy analysis of the bubble signal, a bubble signal source location method based on time domain statistics was proposed, and the trajectory curve of the bubble signal excited by the sodium water reaction in the initial 600 ms was successfully obtained with a position error rate less than 5%. Comparative analysis indicates that kurtosis was more sensitive for bubble signal recognition, while the Root Mean Square (RMS) and Standard Deviation (SD) values were more suitable for bubble positioning. Thus, this study complemented the present techniques and knowledge for bubble identification and localization in multiphase flow engineering.

Acoustic Source Localization in Metal Plates Using BP Neural Network

This study introduces a methodology for detecting the location of signal sources within a metal plate using machine learning. In particular, the Back Propagation (BP) neural network is used. This uses the time of arrival of the first wave packets in the signal captured by the sensor to locate their source. Specifically, we divide the aluminum plate into several areas, design eight receiving points for receiving the excitation signal, and determine the location of each sound source. In order to train and test the machine learning network, the aluminum plate model was established using the COMSOL numerical simulation platform and the propagation of five peak waves was simulated. Correspondingly, experimental verification was carried out and a scanning laser Doppler vibrometer (SLDV) was used to build an experimental platform to collect the corresponding wave field information to obtain a data set for machine learning. The results show that the trained BP neural network can classify the sound source region in both environments.

Signal Source Localization with Long-Term Observations in Distributed Angle-Only Sensors.

Angle-only sensors cannot provide range information of targets and in order to determine accurate position of a signal source, one can connect distributed passive sensors with communication links and implement a fusion algorithm to estimate target position. To measure moving targets with sensors on moving platforms, most of existing algorithms resort to the filtering method. In this paper, we present two fusion algorithms to estimate both the position and velocity of moving target with distributed angle-only sensors in motion. The first algorithm is termed as the gross least square (LS) algorithm, which takes all observations from distributed sensors together to form an estimate of the position and velocity and thus needs a huge communication cost and a huge computation cost. The second algorithm is termed as the linear LS algorithm, which approximates locations of sensors, locations of targets, and angle-only measures for each sensor by linear models and thus does not need each local sensors to transmit raw data of angle-only observations, resulting in a lower communication cost between sensors and then a lower computation cost at the fusion center. Based on the second algorithm, a truncated LS algorithm, which estimates the target velocity through an average operation, is also presented. Numerical results indicate that the gross LS algorithm, without linear approximation operation, often benefits from more observations, whereas the linear LS algorithm and the truncated LS algorithm, both bear lower communication and computation costs, may endure performance loss if the observations are collected in a long period such that the linear approximation model becomes mismatch.

Liquid Metal-Tailored PEDOT:PSS for Noncontact Flexible Electronics with High Spatial Resolution.

Electric field-based noncontact flexible electronics (EF-NFEs) allow people to communicate with intelligent devices through noncontact human-machine interactions, but current EF-NFEs with limited detections (usually <20 cm) distance often lack a high spatial resolution. Here, we report a versatile material for preparing EF-NFE devices with a high spatial resolution to realize everyday human activity detection. Eutectic gallium-indium alloy (EGaIn) was introduced into poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) chains to fabricate this material, named Ga-PP. The introduction of EGaIn successfully regulates the intra- and interchain interactions of PEDOT chains and thus increases the π-electron accumulation on Ga-PP chains, which facilitates improvement of the electron storage of Ga-PP and its noncontact sensing ability. The water solubility of the obtained Ga-PP can reach approximately 15 mg/mL, comparable to that of commercial PEDOT:PSS, thus making Ga-PP suitable for various design strategies to prepare EF-NFE devices. We demonstrate that a conductive textile with a noncontact sensing ability can be achieved by immersing a commercial silk fabric into a Ga-PP solution for 5 min. With a detection distance exceeding 1 m, the prepared Ga-PP-based conductive textile (Ga-PP-CT) possesses outstanding noncontact sensing sensitivity, showing advantages in tracing the locations of signal sources and distinguishing motion states. Surprisingly, even when placed in water, Ga-PP-CT can be used to monitor the movement signals of athletes in different sporting events and output specific noncontact response signals for different sports. Intriguingly, the Ga-PP solution itself can be used to construct noncontact sensing conductive circuits, displaying the potential to be incorporated into smart electronics.

Temporal Logic Planning and Receding Horizon Control for Signal Source Localization

This article copes with signal source localization by employing a receding horizon control approach with temporal logic planning in the light of a single mobile robot. First, a temporal logic planning approach is proposed such that the task requirements from the temporal logic specifications can be effectively dealt with based on the product automaton in an offline fashion. Second, in order to label the key nodes of the product automaton, a particle filter is utilized to predict the source positions as the key nodes. Third, on the basis of the product automaton, a receding horizon control approach with temporal logic planning is developed to produce the robot’s trajectory that satisfies a given linear temporal logic specification. Finally, the effectiveness of the proposed control approach is illustrated for signal source localization.

Localization of Lamb wave source with a single transmitter-receiver combination by elastic metamaterials

Many nondestructive testing and structural health monitoring systems utilize Lamb wave transducer arrays for identification and localization of scattering wave sources. A large number of transducers are usually required during array signal processing and source localization, which increases hardware cost and reduces system reliability. To overcome this limitation, a metamaterial structure is designed in this study to realize source localization with a single transmitter-receiver combination. The basic design principle is the utilization of local resonators to customize propagation characteristics of Lamb waves. In particular, amplitude-frequency modulation and velocity dispersion are customized during metamaterial design. Uncorrelated amplitude-frequency modulation is extracted from signal frequency spectrum for orientation identification. Velocity dispersion is extracted from signal time-frequency spectrum for distance evaluation. After orientation identification and distance evaluation, wave source localization is then realized. Finally, the metamaterial structure is 3D printed using PA2200 nylon and bonded to the aluminum specimen to experimentally demonstrate the effectiveness of source localization with a single transmitter-receiver combination.

Spatial information coding with artificially engineered structures for acoustic and elastic wave sensing

Acoustic and elastic waves carry a wealth of useful physical information in real world. Sensing acoustic and elastic waves is very important for discovering knowledge in various fields. Conventional wave sensing approaches generally require multiple expensive sensors and complex hardware systems due to the uniform spatial transmission characteristics of physical fields. These limitations prompt the development of wave sensing strategies with high integration degree, lightweight structure, and low hardware cost. Due to their extraordinary physical properties, artificially engineered structures such as metastructures can encode the physical field information by flexibly manipulating the transmission characteristics of acoustic and elastic waves. The fusion of information coding and wave sensing process breaks through the limitations of conventional sensing approaches and reduces the sensing cost. This review aims to introduce the advances in spatial information coding with artificially engineered structures for acoustic and elastic wave sensing. First, we review the enhanced spatial wave sensing with metastructures for weak signal detection and source localization. Second, we introduce computational sensing approaches that combines the spatial transmission coding structures with reconstruction algorithms. Representative progress of computational sensing with metastructures and random scattering media in audio source separation, ultrasonic imaging, and vibration information identification is reviewed. Finally, the open problems, challenges, and research prospects of the spatial information coding structures for acoustic and elastic wave sensing are discussed.

Deep Learning-Based Localization with Urban Electromagnetic and Geographic Information

There is a growing demand for localization of illegal signal sources, aiming to guarantee the security of urban electromagnetic environment. The performance of traditional localization methods is limited due to the non-line-of-sight (NLOS) propagation and sparse layouts of sensors. In this paper, a deep learning-based localization method is proposed to overcome these issues in urban scenarios. Firstly, a model of electromagnetic wave propagation considered with geographic information is proposed to prepare reliable datasets for intelligent cognition of urban electromagnetic environment. Then, this paper improves an hourglass neural network which consists of downsampling and upsampling layers to learn the propagation features from sensing data. The core modules of VGG and ResNet are, respectively, utilized as feature extractors in downsampling. Moreover, this paper proposes a weighted loss function to expand the attention on position features, in order to improve the performance of localization with sparse layouts of sensors. Representative numerical results are discussed to assess the proposed method. ResNet-based extractor performs more efficiently than VGG-based extractor, and the proposed weighted loss function increases the localization accuracy by more than 50%. Additionally, the established geographic model supports qualitative and quantitative evaluation of the robustness with varied degree of NLOS propagation. Compared with other deep learning-based algorithms, the proposed method presents the more robust and superior performance under severe NLOS propagation and sparse sensing conditions.

816 Traveling Waves Reveal a Dynamic Source of Human Focal Seizures and Interictal Spikes

INTRODUCTION: Treatment of patients with drug resistant focal epilepsy relies upon accurate seizure localization. Ictal activity captured in intracranial EEG (iEEG) has traditionally been interpreted to suggest that the underlying cortex is actively seizing. On the other hand, an emerging hypothesis suggests that seizures may in fact emerge from focal regions, which emit discharges that travel over the surface of the cortex as traveling waves. METHODS: 20 patients underwent placement of subdural electrodes for invasive monitoring at the National Instutes of Health, underwent surgical resection for epilepsy, and had at least 1 year of follow-up. Outcomes were measured by the Engel scale. In these patients, we measured the time delay in discharge receipt at adjacent electrodes to compute the location of the seizure source. This algorithm, known as multilateration, has been used for signal source localization with sparsely-spaced sensors for over a century in geophysics, radar, and acoustics, but has not been used in epilepsy until the present. RESULTS: In patients with good outcome, but not in patients with poor outcome, the seizure source tends to localize to the resection territory. The source is a focal, dynamic entity that moves and evolves over the time course of a seizure. Our approach also successfully localizes interictal spikes. We find that interictal spikes emerge from multiple distinct foci in each patient. Seizures either arise from or travel to interictal spike generators. CONCLUSIONS: The seizure source appears to be more focal than has traditionally been conceptualized. This focal source may be localized by measuring the time difference of discharge receipt in intracranial electrodes. Algorithmic source localization, as performed here, may guide pre-surgical planning for drug-resistant epilepsy and may improve outcomes.

Photonics-assisted Wideband RF Source Localization Method Based on Synthetic Aperture Interferometric Detection

Angle of arrival (AOA) is one key parameter for passive signal source localization. However, with the increase in RF systems' instantaneous bandwidth and working frequency, it is a great challenge for traditional narrowband array signal processing methods to achieve effective AOA measurement of wideband RF sources. This paper proposes a photonics-assisted wideband RF source localization method based on synthetic aperture interferometer radiometer (SAIR) detection. The proposed method introduced cross-power spectrum (CPS), which can characterize the interference at different frequencies to define the wideband visibility function. Then the wideband SAIR detection is achieved from the wideband visibility function, which is Fourier transformed in the spatial domain and then performs integration in the frequency domain. Numerical simulation and proof-of-concept experiment results show that the proposed method can suppress the grating lobes caused by large element spacing. With a microwave photonic (MWP) channelizer and an antenna array, a wideband signal covering 16-20 GHz is detected. The AOA resolution of the system is 3.54&#x00B0;, and the power of the maximum grating lobe in the AOA measurement is suppressed by over 80&#x0025;.

BCAuth: Physical Layer Enhanced Authentication and Attack Tracing for Backscatter Communications

Backscatter communication (BC) enables ultra-low-power communications and allows devices to harvest energy simultaneously. But its practical deployment faces severe security threats caused by its nature of openness and broadcast. Authenticating backscatter devices (BDs) is treated as the first line of defense. However, complex cryptographic approaches are not desirable due to the limited computation capability of BDs. Existing physical layer authentication schemes cannot effectively support BD mobility, multiple attacker identification and attacker location tracing in an integrated way. To tackle these problems, this paper proposes BCAuth, a multi-stage authentication and attack tracing scheme based on the physical spatial information of BDs to realize enhanced BD authentication security for both static and mobile BDs. After initial authentication based on BD identity with its position information registration, preemptive authentication and re-authentication are performed according to spatial correlation of backscattered signal source locations associated with the BD. By exploiting clustering-based analysis on spacial information, BCAuth is capable of determining the number of attackers and localizing their positions. In addition, we propose a reciprocal channel-based method for BD re-authentication with better authentication performance than the clustering-based method for mobile BDs when the BDs is able to measure received signal strength (RSS), which also enables mutual authentication. We theoretically analyze BCAuth security and conduct extensive numerical simulations with various settings to show its desirable performance.

ECG Localization Method Based on Volume Conductor Model and Kalman Filtering.

The 12-lead electrocardiogram was invented more than 100 years ago and is still used as an essential tool in the early detection of heart disease. By estimating the time-varying source of the electrical activity from the potential changes, several types of heart disease can be noninvasively identified. However, most previous studies are based on signal processing, and thus an approach that includes physics modeling would be helpful for source localization problems. This study proposes a localization method for cardiac sources by combining an electrical analysis with a volume conductor model of the human body as a forward problem and a sparse reconstruction method as an inverse problem. Our formulation estimates not only the current source location but also the current direction. For a 12-lead electrocardiogram system, a sensitivity analysis of the localization to cardiac volume, tilted angle, and model inhomogeneity was evaluated. Finally, the estimated source location is corrected by Kalman filter, considering the estimated electrocardiogram source as time-sequence data. For a high signal-to-noise ratio (greater than 20 dB), the dominant error sources were the model inhomogeneity, which is mainly attributable to the high conductivity of the blood in the heart. The average localization error of the electric dipole sources in the heart was 12.6 mm, which is comparable to that in previous studies, where a less detailed anatomical structure was considered. A time-series source localization with Kalman filtering indicated that source mislocalization could be compensated, suggesting the effectiveness of the source estimation using the current direction and location simultaneously. For the electrocardiogram R-wave, the mean distance error was reduced to less than 7.3 mm using the proposed method. Considering the physical properties of the human body with Kalman filtering enables highly accurate estimation of the cardiac electric signal source location and direction. This proposal is also applicable to electrode configuration, such as ECG sensing systems.

Pushing to the Limits: What Processes during Cognitive Control are Enhanced by Reaction-Time Feedback?

To respond as quickly as possible in a given task is a widely used instruction in cognitive neuroscience; however, the neural processes modulated by this common experimental procedure remain largely elusive. We investigated the underlying neurophysiological processes combining electroencephalography (EEG) signal decomposition (residue iteration decomposition, RIDE) and source localization. We show that trial-based response speed instructions enhance behavioral performance in conflicting trials, but slightly impair performance in nonconflicting trials. The modulation seen in conflicting trials was found at several coding levels in EEG data using RIDE. In the S-cluster N2 time window, this modulation was associated with modulated activation in the posterior cingulate cortex and the superior frontal gyrus. Furthermore, in the C-cluster P3 time window, this modulation was associated with modulated activation in the middle frontal gyrus. Interestingly, in the R-cluster P3 time window, this modulation was strongest according to statistical effect sizes, associated with modulated activity in the primary motor cortex. Reaction–time feedback mainly modulates response motor execution processes, whereas attentional and response selection processes are less affected. The study underlines the importance of being aware of how experimental instructions influence the behavior and neurophysiological processes.

A Multi-Objective Particle Swarm Optimization Based Algorithm for Primary User Emulation Attack Detection

The cognitive radio network (CRN) has been proposed to overcome the spectrum scarcity and the massive demand on the radio frequencies through efficient utilization and smart management of the free channels. Using a spectrum sensing process, the secondary users (SUs) share the spectrum of the primary users (PUs) without causing any interference to these latter. One of the main threats affecting the channel utilization in CRN is the primary user emulation (PUE) attack. The PUE attack utilizes a sophisticated technique to craft the same signal of a legitimate PU to gain unauthorized access to the unused channels, forcing the SUs to immediately free up vacant spectrum space, resulting in a denial of service (DoS), degradation of service, and causing a noticeable impact on CRN performance. To circumvent this attack, the anchor nodes must accurately estimate the coordinates of the PU signal source. In the literature, most of the localization of unknown signal source, in our case the PU/PUE, are based on the ranging schemes which measure the distance between the blind node and the anchors. Those anchors are aware of their location and situated in optimized positions to the signal source in order to permit an accurate PU/PUE position detection. The detection rate depends tightly on the distance separating the anchors to the signal source as well as the signal-to-noise ratio (SNR). In this paper, we demonstrate the impact of the distance on the detection error while highlighting the particle swarm optimization’s (PSO) advantage in optimizing the anchors positioning. Furthermore, we illustrate the SNR impact on the probability of detection, particularly in the situation of low SNR and the attacker in the vicinity to a real PU. The main contribution in this work is the proposition of an approach with the aim of protecting an area containing more than a single PU with a limited number of anchor nodes while providing a higher detection rate to stop any eventual PUE attack. This approach is based on a multi-objective particle swarm optimization (MOPSO) algorithm for PU/PUE position detection. It minimizes concurrently the probability of detection error related to the received signal strength (RSS)/trilateration and the SNR, with the main objective of finding all optimized positions for the mobile anchor nodes and obtains the most accurate PUE attack detection.

Position Report Enhancement Using Bayesian Estimator

In this article, a Bayesian estimator for a target position report is proposed. It is based on a maximum a priori algorithm, where the user's device knowledge about its location is used to deduce the prior probability density function. The algorithm does not require knowledge about the signal and noise levels, meaning, noninformative priors are used. It is well known that with a higher number of antenna elements in an array, narrower beams can be formed. In beamforming, task narrow beams are useful for serving many users simultaneously, whereas in direction-of-arrival (DOA) estimation, we are not interested in narrow beams as such; instead, estimation accuracy is important. So, reducing the number of antenna elements used for DOA estimation is beneficial from a system complexity point of view. In this article, signal source location report is used to enhance the estimated DOA, for the task, MAP estimator is developed. We will show that in the case of small array size and large array covariance matrix error values, the proposed estimator is the only one capable of improving the prior knowledge about the transmitter when comparing it with other popular algorithms, such as the maximum likelihood estimator and Root MUSIC. The algorithm could be used in a variety of different scenarios, but its advantages emerge in the case of complex signal propagation environments, such as urban canyons and large airports.