Localization Algorithm Research Articles

Research on 3D Localization of Indoor UAV Based on Wasserstein GAN and Pseudo Fingerprint Map

In addition to outdoor environments, unmanned aerial vehicles (UAVs) also have a wide range of applications in indoor environments. The complex and changeable indoor environment and relatively small space make indoor localization of UAVs more difficult and urgent. An innovative 3D localization method for indoor UAVs using a Wasserstein generative adversarial network (WGAN) and a pseudo fingerprint map (PFM) is proposed in this paper. The primary aim is to enhance the localization accuracy and robustness in complex indoor environments. The proposed method integrates four classic matching localization algorithms with WGAN and PFM, demonstrating significant improvements in localization precision. Simulation results show that both the WGAN and PFM algorithms significantly reduce localization errors and enhance environmental adaptability and robustness in both small and large simulated indoor environments. The findings confirm the robustness and efficiency of the proposed method in real-world indoor localization scenarios. In the inertial measurement unit (IMU)-based tracking algorithm, using the fingerprint database of initial coarse particles and the fingerprint database processed by the WGAN algorithm to locate the UAV, the localization error of the four algorithms is reduced by 30.3% on average. After using the PFM algorithm for matching localization, the localization error of the UAV is reduced by 28% on average.

Vision-based weld detection and localization for climbing robots in large-scale storage tank inspections

Abstract Climbing robots are considered an effective solution for inspecting welds on the walls of large storage tanks. For these robotic systems, the efficient and accurate identification and localizing of weld seams are crucial prerequisites for ensuring precise weld seam tracking. In this paper, we investigate machine vision-based algorithms for feature recognition and localization of weld seams on tank walls for inspection of weld seams by a climbing robot. First, we designed the model of the image algorithm to extract the weld features of the tank walls. After extracting the weld features, we propose the novel idea of feature discretization and a Min-outer Rectangle Fitting Algorithm (MRFA), which will achieve the fitting of rectangular features on the discretized weld features. We constructed a mathematical model for calculating the orientation angle of the rectangular box based on the extracted rectangular boxes. This model allows for the real-time and efficient extraction of the rectangular feature’s pose information (x, y, θ). We also propose an efficient method for calculating the curvature of a curve trajectory. The experimental results demonstrate that the proposed image algorithm model and MRFA effectively identify weld features on the storage tank wall surface, while simultaneously achieving high-accuracy feature localization. Positioning errors are maintained within 3 mm for position and 3 degrees for azimuth, indicating both high precision and robustness. Additionally, the algorithm processes each image in approximately 80 milliseconds. The lightweight and efficient design of the proposed model allows it to be easily deployed on a climbing robot for weld seam detection and tracking on tank walls.

Lidar Simultaneous Localization and Mapping Algorithm for Dynamic Scenes

To address the issue of significant point cloud ghosting in the construction of high-precision point cloud maps by low-speed intelligent mobile vehicles due to the presence of numerous dynamic obstacles in the environment, which affects the accuracy of map construction, this paper proposes a LiDAR-based Simultaneous Localization and Mapping (SLAM) algorithm tailored for dynamic scenes. The algorithm employs a tightly coupled SLAM framework integrating LiDAR and inertial measurement unit (IMU). In the process of dynamic obstacle removal, the point cloud data is first gridded. To more comprehensively represent the point cloud information, the point cloud within the perception area is linearly discretized by height to obtain the distribution of the point cloud at different height layers, which is then encoded to construct a linear discretized height descriptor for dynamic region extraction. To preserve more static feature points without altering the original point cloud, the Random Sample Consensus (RANSAC) ground fitting algorithm is employed to fit and segment the ground point cloud within the dynamic regions, followed by the removal of dynamic obstacles. Finally, accurate point cloud poses are obtained through static feature matching. The proposed algorithm has been validated using open-source datasets and self-collected campus datasets. The results demonstrate that the algorithm improves dynamic point cloud removal accuracy by 12.3% compared to the ERASOR algorithm and enhances overall mapping and localization accuracy by 8.3% compared to the LIO-SAM algorithm, thereby providing a reliable environmental description for intelligent mobile vehicles.

Fault diagnosis method for lithium-ion batteries based on relative-range-feature and improved Theil index

ABSTRACT With the widespread adoption of electric vehicles (EVs), battery failures have become a significant concern. To safeguard the lives and property of drivers, accurate and prompt diagnosis of battery failures is crucial. This paper proposes a novel battery fault diagnosis method based on the Relative-Range-Feature (RRF) and an improved Theil index, utilizing actual operating data from EVs. Firstly, the trend component of the voltage is extracted utilizing Singular Spectrum Analysis (SSA) to eliminate noise from the original voltage data. Secondly, to address the challenge of inconspicuous early fault features, a new RRF feature extraction method is introduced. This method significantly amplifies the feature differences between normal and faulty batteries. An improved Theil index is proposed to achieve prompt fault detection with high robustness. Finally, a faulty battery localization algorithm that combines Multidimensional scaling (MDS) and Mahalanobis distance (MD) is proposed. This algorithm excels in distinguishing faulty batteries from normal ones. Validation using actual vehicle data demonstrates that the proposed algorithm offers significant advantages in reliability, effectiveness, and robustness. Compared to the information entropy and correlation coefficient methods, this method demonstrates superior applicability.

Towards using virtual acoustics for evaluating spatial ecoacoustic monitoring technologies

Abstract Small microphone arrays and sound‐source localisation algorithms are increasingly prevalent in the passive acoustic monitoring (PAM) of ecosystems. These technologies enable analysis of natural soundscapes' spatial features, yielding additional insights into biodiversity and ecosystem health. While many of these technologies have been evaluated in the field, there is a lack of controlled, repeatable methods to test them. We developed an ambisonic virtual sound environment (VSE) for simulating real natural soundscapes to evaluate spatial PAM technologies. We validated this novel approach using a PAM recorder with a six‐microphone array, from which we extracted a typical suite of ecoacoustic metrics, including acoustic indices and avian species predictions and localisations from the software BirdNET and HARKBird, respectively. We first verified whether the VSE could replicate natural soundscapes well enough to test PAM technologies by comparing these metrics between field and VSE‐based recordings. To pilot the VSE as an environment for testing PAM hardware, we assessed how orientation impacts the six‐microphone array's performance by using the same suite of metrics to compare VSE recordings made with the array at various pitch angles. Finally, we piloted the VSE as a test platform for PAM software by investigating how BirdNET and HARKBird perform on bird calls added to the VSE‐replicated soundscapes. While the VSE and field recordings had similarities in some metrics, including spectral composition and BirdNET predictions, ambisonics' perceptual bias and susceptibility to spatial aliasing limited the spatial analyses that could be undertaken. Our trials nonetheless revealed that device orientation impacts the performance of HARKBird and certain ecoacoustic indices, and that BirdNET and HARKBird perform best on louder, more directional bird calls. Our results demonstrate the potential for this approach, but highlight limitations to using an ambisonics‐based VSE. We thus provide guidelines for the use and refinement of such systems towards more standardised, controlled benchmarking of PAM technologies, empowering practitioners to make more informed decisions on using these vital tools.

Wireless UV collaborative RSSI and the AOA hybrid localization method for UAV swarms

Wireless ultraviolet (UV) light can guarantee the inter-copter positioning accuracy of cluster-flying unmanned aerial vehicles (UAVs) in an electromagnetic confrontation environment. By combining wireless UV received signal strength indication (RSSI) localization and the angle of arrival (AOA) localization algorithm, a UV-hybrid localization method is proposed for UV communication collaboration between UAV swarms. The method collects the UV signal strength between the anchor UAV node and the unknown UAV node to obtain the inter-aircraft distance information, establishes a Gaussian hybrid noise model based on semi-definite relaxation, and uses the UV angle of the arrival estimation to solve the angle between the UAVs to achieve the maximum likelihood estimation of node positions in UAV swarms. A simulation comparison of the UV-hybrid localization algorithm, weighted least squares, and the Gaussian hybrid semi-definite planning localization algorithm is carried out. The results show that the performance of the UV-hybrid localization algorithm is close to the Cramer–Rao lower bound, and the average localization error is reduced by 32.9% and 15.6% compared to weighted least squares and Gaussian hybrid semi-definite planning algorithms; the algorithm of this paper achieves the node localization with fewer iterations, and it has a higher accuracy and efficiency of localization than the other algorithms.

Development and Experiment of Adaptive Oolong Tea Harvesting Robot Based on Visual Localization

Aimed to improve the quality of picked tea leaves and the efficiency of tea harvesting, an adaptive oolong tea harvesting robot with an adjustment module of a cutting tool and a harvesting line localization algorithm is proposed. The robot includes a vision measurement module and an adjustment mechanism of a cutting tool, enabling it to assess the shape of tea bushes and adaptively adjust the cutter configuration. To address the challenges of complex tea bush structures and environmental noise, a Prior–Tukey RANSAC algorithm was proposed for accurate harvesting model fitting. Our algorithm leverages prior knowledge about tea bush stem characteristics, uses the Tukey loss function to enhance robustness to outliers, and incorporates workspace constraints to ensure that the cutting tool remains within feasible operational limits. To evaluate the performance of the robot, experiments were conducted in a tea garden in Wuyi Mountain, China. Under ideal conditions, our algorithm achieved an inlier ratio of 43.10% and an R2 value of 0.9787, significantly outperforming traditional RANSAC and other variants. Under challenging field conditions, the proposed algorithm demonstrated robustness, maintaining an inlier ratio of 47.50% and an R2 value of 0.9598. And the processing time of the algorithm met the real-time requirements for effective tea-picking operations. The field experiments also showed an improvement in intact tea rates, from 79.34% in the first harvest to 81.57% in the second harvest, with a consistent usable tea rate of around 85%. Additionally, the robot had a harvesting efficiency of 260.14 kg/h, which was superior to existing handheld and riding-type tea pickers. These results indicate that the robot effectively balances efficiency, accuracy, and robustness, providing a promising solution for high-quality tea harvesting in complex environments.

Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting Methods

The Internet of things (IoT) has significantly impacted various sectors, including healthcare, environmental monitoring, transportation, and commerce, by enhancing communication networks through the integration of sensors, software, and hardware. This paper presents an accurate IoT indoor localization system based on IoT devices and fingerprinting methods. We explore indoor localization techniques using Bluetooth Low Energy (BLE) and a Radio Signal Strength Indicator (RSSI) to address the limitations of GPS in indoor environments. The study evaluates the effectiveness of iBeacon transmitters for indoor positioning, comparing the Weighted Centroid Localization (WCL) and Positive Weighted Centroid Localization (PWCL) algorithms, along with fingerprinting methods enhanced by outlier detection and mapping filters. Our methodology includes mapping a real environment onto a coordinate axis, collecting training data from 47 sampling points, and implementing four localization algorithms. The results show that the PWCL algorithm improves accuracy over the WCL algorithm, and hybrid methods further reduce localization errors. The HYBRID-MAPPED method achieves the highest accuracy, with an average error of 1.44 m.

A submillimeter notch visualization detection method based on local wavenumber imaging algorithm of broadband laser-generated surface-wave

Abstract Laser ultrasound is widely used in metal notch detection because of its non-contact, non-destructive and high accuracy, but there is still the problem of insufficient detection sensitivity. Therefore, this paper proposes a laser ultrasound-based broadband surface wave local wavenumber imaging algorithm to provide an effective method for the detection of surface notches by taking the aluminum alloy metal structure as the research object. Broadband surface waves on a metal plate are excited by a laser, and vibration information is obtained through vibrometer scanning with a Laser Doppler Vibrometer(LDV). It characterizes the surface notches in the wavenumber domain by filtering out the direct waves and retaining only the reflected surface waves of the new modes generated by the notches, successfully detecting and visually characterizing the tiny surface notches at the sub-millimeter level. The surface notch defect detection experiment was carried out using a laser ultrasound detection platform, and the surface notch with a width and depth of 0.1mm detection. The effectiveness of the method was verified by repeated experiments. The results of the study provide an effective surface notch detection method for metallic structures which has a broad application prospect.

A Dijkstra Based Algorithm for Optimal Splitter Location in Passive Optical Local Area Network (POLAN)

Passive Optical Local Area Networks (POLANs) are integral to modern broadband communication systems, offering high bandwidth and immunity to electromagnetic interference. Designing an efficient POLAN requires careful consideration of splitter placement to minimize network costs. This paper presents an algorithmic approach using Dijkstra's algorithm and the Google Maps API to optimize splitter locations in a POLAN. By treating Optical Network Terminals (ONTs) as nodes in a graph and calculating walking distances between them, the algorithm identifies potential splitter locations that minimize fiber length. Using the Dijkstra's algorithm, the total fiber length used to connect every optical network unit is approximately 274km. Finally, a simulation of the full PON network was carried out and the BER and Q-Factor for each ONU was gotten. An average BER value of 1.8e-11 and Q-Factor value of 13.3 was gotten.

An $\ell _{0}$-Norm Optimization-Based Algorithm for Robust and Efficient MIMO Localization

An $\ell _{0}$-Norm Optimization-Based Algorithm for Robust and Efficient MIMO Localization

A Distributed Learning Algorithm for Communication Development

We study the question of how a local learning algorithm, executed by multiple distributed agents, can lead to a global system of communication. First, the notion of a perfect communication system is defined. Next, two measures of communication system quality are specified. It is shown that maximization of these measures leads to perfect communication production. Based on this principle, local adaptation rules for communication development are constructed. The resulting stochastic algorithm is validated in computational experiments. Empirical analysis indicates that a mild degree of stochasticity is instrumental in reaching states that correspond to accurate communication.

Multi-crack damage identification and quantification using Lamb wave-based structural health monitoring

The simultaneous presence of multi-crack damage in a structural component is a crucial issue affecting system safety. To address the accurate and quantitative monitoring of multi-crack damage in an aeronautical structure, an innovative multi-crack damage localization, orientation and quantification algorithm using Lamb wave-based structural health monitoring is presented in this paper. An improved Hausdorff distance-based weighted average imaging methodology is introduced for precise multi-crack damage position identifications. To account for the critical role of crack orientation in damage quantification, a cross-orthogonality-based method is developed, enabling orientation detection at arbitrary positions and angles while simplifying the problem into a mathematical formulation. A wave scattering sources-based quantification algorithm incorporating the estimated position and orientation information is further proposed to estimate the multi-crack lengths. Additionally, a singular elliptic trajectories removal scheme is presented to suppress useless ambient noise and enhance the damage information discriminability. Experiments on the aircraft wing-box structure of a real airplane are implemented to substantiate the proposed techniques. Due to the outstanding properties, such as flexibility, electrically stabilized and applicability to complex structures, the sensor layer with built-in PZT sensor network surface-installed on the monitored structure is adopted to generate and receive Lamb wave signal. The results manifest that the proposed monitoring algorithm, without prior information or calibration required, is effective and straightforward for detecting multi-crack damage. This study can also provide a feasible technique for accurately locating and quantitatively identifying cracks in some concealed parts or inside the structure.

A Localization Algorithm for Underwater Acoustic Sensor Networks With Improved Newton Iteration and Simplified Kalman Filter

A Localization Algorithm for Underwater Acoustic Sensor Networks With Improved Newton Iteration and Simplified Kalman Filter

Moving ships detection via the trajectory feature extraction from spatiotemporal slices of infrared maritime videos

In practical application scenarios, maritime infrared videosoften contain different types of sea state scenes, weather conditions, shooting time, shooting distance, etc. In these different types of maritime videos, ship targets have great differences in size, grayscaledistribution and contrast, which brings difficulties to ship target detection.At the same time, the diversity of fluctuation, grayscale distribution and reflected light of the sea surface will bring unpredictable interference noise to ship target detection.How to accurately detect ship targets in complex and changeable maritime infrared videos is a challenging task and research focus.The key to achieving accurate ship detection is to extract robust target features that can effectively distinguish targets from all thebackground noises.In this paper, a novel infrared video ship target detection algorithm based on spatiotemporal slice target trajectory features extraction is proposed. The algorithm is very sensitive to real targets and has excellentanti-noise ability.The main innovation of the algorithm is to extract the target trajectory feature from the spatiotemporal slice of the sequence image and generate the trajectory feature map.We use the trajectory texture formed by the ship target in the spatiotemporal slice to extract the target feature, which can greatly suppress the background noise.The adaptive dilation linear model algorithm can effectively detect the target trajectory line in the spatiotemporal slice. In addition, we also make full use of the gradient of the target trajectory line to distinguish different target trajectory pixels, and propose an adaptive iterative dilation target region localization algorithm combined with gradient consistency.For object segmentation, we calculate the segmentation double-threshold using the adjacent surrounding background pixels of the target, so as to achievetarget segmentationof multiple grayscaledistribution types. Finally, in the comparison experiment, our algorithm shows superior target detection performance, especially when detecting ships from a large number of highlighted sea clutter background, the robust anti-noise ability of the algorithm can be highlighted.

Data-Science-Informed Operation, Maintenance Practices Reduce Emissions

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTeC 3866061, “Reducing Methane Emissions: Implementing Data-Science-Informed Operation and Maintenance Work Practices Using Continuous Monitoring Technology,” by Kathleen Jenkins, Carol A. Brereton, and Alex MacGregor, Qube Technologies, et al. The paper has not been peer reviewed. _ Diverse methods are used by oil and gas operators for methane leak detection and repair (LDAR). Deploying continuous monitoring (CM) point-sensor technologies to an oil and gas facility allows operators to implement novel operation and maintenance work practices to respond efficiently to methane emissions. The authors examine how data-science-driven work practices can result in substantial reduction in methane emissions compared with other LDAR methods. CM Technology CM point sensors track changes in parts per million of methane in the air. When several fixed sensors are deployed at a site, a large area can be surveilled. Using proprietary algorithms based on a plume-dispersion model, the volume and site rate of the methane emission are calculated. The probable location of the emission is estimated and, together with the volume, is shown on a Web-based dashboard that displays the changes in site rate through time. Alerts generated by the CM system allow the operator to respond to leaks more quickly than when waiting for data from an intermittent inspection to provide emission data. CM was deployed by a Permian Basin operator with a desire to operate cleanly, with transparency. The devices were installed with the operator beginning in March 2022 and continue to be active at the time of writing. Alerts generated by the system are delivered to operations personnel, including control-room operators, and investigated by field operators. When the source is identified, repairs are scheduled or completed on the spot and recorded for review. The operator has expanded the deployments throughout the field and uses the data to identify and repair leaks. Theory and Methods The CM devices were deployed around the fence line at several production facilities in the Permian Basin. The optimal number and placement of sensors was determined by analyzing publicly available wind data and sources of emissions at the facility. The CM devices have an inlet port that allows the ambient air to enter the device and flow across a metal oxide sensor. Metal oxide sensors measure the change in conductivity as the air flows across them as a result of the methane adhering to the metal. The change in conductivity, directly correlated with the parts per million of methane in the air, is recorded. Also measured are temperature and wind data, read by temperature sensors and anemometers on each device. This data is sent through a mobile telecommunications network to cloud-based servers that apply an emission-estimation model based on a plume dispersion and a localization algorithm to the data. Cloud services facilitate the transfer of information from devices to Web-based software. Each methane sensor is calibrated in the laboratory before installation by exposing it to known levels of methane. In this process, the temperature and humidity are systematically varied while the methane concentrations are changed and the reported conductivity is recorded. The resulting coefficients of conductivity are stored with the serial number of the device and sent to the device through the mobile network when it is deployed in the field.

Detection of Access Point Spoofing in the Wi-Fi Fingerprinting Based Positioning

Indoor positioning based on Wi-Fi signals has gained a lot of attention lately. There are many advantages related to the use of Wi-Fi signals for positioning, including the availability of Wi-Fi access points in indoor environments and the integration of Wi-Fi transceivers into consumer devices. However, since Wi-Fi uses an unlicensed spectrum, anyone can create their own access points. Therefore, it is possible to affect the function of the localization system by spoofing signals from access points and thus alter positioning accuracy. Previously published works focused mainly on the evaluation of spoofing on localization systems and the detection of anomalies when updating the radio map. Spoofing mitigation solutions were proposed; however, their application to systems that use off-the-shelf items is not straightforward. In this paper filtering algorithms are proposed to minimize the impact of access point spoofing. The filtering was applied with a combination of the widely used K-Nearest Neighbours (KNN) localization algorithm and their performance is evaluated using the UJIIndoorLoc dataset. During the evaluation, the spoofing of Access Points was performed in two different scenarios and the number of spoofed access points ranged from 1 to 10. Based on the achieved results proposed SFKNN provided good detection of the spoofing and helped to reduce the mean localization error by 2–5 m, especially when the number of spoofed access points was higher.

TRAJECTORY PLANNING FOR SEARCH-AND-RESCUE UAVs USING A GREEDY ALGORITHM

This article presents a trajectory-planning program for research-and-rescue UAVs based on the use of a local optimization greedy algorithm. Trajectories are generated over a search domain characterized by a probabilistic score map. For multiple-UAV systems, the program can assign each device to a search mission based on the probabilistic property or the geometry of the search domain. Some parameters such as the maximum travelled distance and trajectory resolusion could be input into the program. In this study, the program was tested to run for a two-UAV system over a given probabilistic score map. The input trajectory paremerters were selected based on the properties of each UAV and the requirement of a search-and-rescue mission. The program may be seamlessly integrated with UAV flight control software, enabling a direct translation of the obtained trajectories into autonomous mission execution.

APPROACH TO DETECTION OF ANOMALOUS NETWORK TRAFFIC USING LOF AND HBOS ALGORITHMS

The article is devoted to the problem of detecting anomalies in modern computer networks, which is one of the main threats to cyber security. With the development of Internet technologies, the number of devices and the volume of network traffic are constantly increasing, which leads to an increase in the risk of various cyber threats, such as DDoS attacks, zero-day attacks, and exploitation of protocol vulnerabilities. Abnormal network traffic can result from malicious activity and technical malfunctions, such as configuration errors or hardware failures. Specialised algorithms and methods of analysing large volumes of data are used to detect such threats. The paper considers the main methods of detecting anomalies in network traffic, including classical approaches and modern deep and machine learning methods. Special attention is paid to the efficiency of using methods based on convolutional neural networks, long-term memory and their combinations to detect anomalies. An analysis of the disadvantages and advantages of various approaches to detecting anomalous traffic, such as high computational requirements and the complexity of setting up models, is performed. Still, their effectiveness in analysing large volumes of data is noted. One of the main methods used for anomaly analysis is the local outlier algorithm, which compares the density of objects with their neighbours, allowing for the detection of anomalies in regional segments of the data. Another method is histogram-based outlier estimation, which is faster and more efficient using one-dimensional histograms for each variable. The work also explores the application of unsupervised machine learning methods, which allows for analysing network traffic in real time without the need for prior labelling of data. The article also considers the prospects of further testing the proposed methods in real networks. The combined use of LOF and HBOS balances anomaly detection accuracy and data processing speed, essential to ensure continuous system operation in high-load networks. The implementation of similar solutions in actual conditions requires further research, particularly regarding optimising the use of computing resources and adapting methods to the specific conditions of the network environment. Thus, the paper presents a thorough analysis of modern approaches to detecting anomalies in network traffic and substantiates the feasibility of their application in actual conditions to increase the effectiveness of cyber security.

An enhanced DV-hop localization algorithm based on hop distance correction and multi-strategy modified Aquila Optimizer in HWSNs

An enhanced DV-hop localization algorithm based on hop distance correction and multi-strategy modified Aquila Optimizer in HWSNs