Localization Error Research Articles

Proposal of a wireless sensor network for footstep localization and optimization of its location using bio-inspired metaheuristics

This paper introduces a proposal for a wireless network of smart sensors designed to capture the vibrations produced by the impact of a person’s footsteps on a dispersive and damped floor. These vibrations are utilized to pinpoint the location of impacts using a heuristic algorithm, enabling operation at a low data transmission/reception rate. Traditionally, the placement of sensors within a room has not been given significant consideration in the context of localization; however, our findings indicate that a detailed analysis of both the number and location of sensors can substantially enhance the accuracy of footstep localization. In this study, we have optimized the arrangement and quantity of sensors through the application of bio-inspired metaheuristics, aiming to minimize localization errors across the room. Upon evaluating various bio-inspired metaheuristic optimization algorithms, we identified the one that yielded the lowest estimation errors for the room as a whole. Our experimental tests demonstrate that such optimization significantly enhances the efficacy of the localization algorithm, resulting in a reduction of localization error ranging from 18.24% to 46.78% across different trajectories.

An assessment of 2-D and 3-D interest point detectors in volumetric images

Many interest point detectors have been designed so far to work in two dimensional (2-D) images. However, expansion of these detectors into the third dimension for three dimensional (3-D) images can refine their representational power. This paper presents how the Harris corner, LoG filtering-based blob, and salient regions detectors can be expanded to find interest points in volumetric images handling multiple slices collectively. Performances of 2-D and 3-D detector implementations were assessed both qualitatively and quantitatively with value combinations of different parameters using metrics such as F1-score, localization error, and repeatability in binary images of twenty 3-D object models from the Princeton Shape Benchmark (PSB). Computation of F1-score and localization error depended on some manually marked ground truth points, while repeatability measurement was according to the proximity of the detected point sets. The 3-D detectors were evaluated as more successful in capturing distinctive and sparse interest points on 3-D object surfaces in qualitative analyses. Despite having greater computational complexities, most of the 3-D detectors yielded better average F1-score, localization accuracy, and repeatability given uniqueness constraint on the matched points in quantitative analyses. Therefore, the 3-D detectors appear preferable when longer working durations or sparser representations would not constitute any disadvantage.

MF-LIO: integrating multi-feature LiDAR inertial odometry with FPFH loop closure in SLAM

Abstract Simultaneous localization and mapping (SLAM) is a pivotal technology in autonomous vehicle navigation and a significant research area in robotics. Addressing the inaccuracies in point cloud registration of traditional LiDAR SLAM, which lead to localization and mapping errors, we propose a novel LiDAR inertial odometry approach integrating inertial measurement units and a multi-feature joint registration strategy. Initially, we introduce an innovative ground segmentation method and feature categorization strategy, enhancing ground detection mechanisms and optimizing the feature extraction process. Subsequently, our multi-feature joint registration method computes the pose transformations between current frames and the local map. Finally, we employ a global registration method based on fast point feature histograms feature descriptors for coarse alignment, providing initial estimates for the generalized iterative closest point algorithm, thus efficiently and accurately mitigating cumulative errors. Extensive evaluations on the KITTI dataset and real-world campus environment demonstrate that our approach significantly surpasses existing advanced LiDAR SLAM solutions, achieving over a 24% improvement in pose estimation accuracy.

Optimal station placement based on grey wolf optimizer for regional target localization

The accuracy of target passive localization is influenced by the placement of signal receiving stations; therefore, many studies have been performed to optimize station placement. However, most of the present placement methods focus on the localization error of one target, and if the exact position of the target cannot be determined, but only the range of the target activity is known, how to study the localization station placement in a region is a problem that needs to be solved. This paper proposes a grey wolf optimization algorithm based on the regional target error model to solve the optimal station placement problem. Firstly, a regional target localization error model is established using the measured TDOA, and the overall error matrix within a region is derived. Then, by taking the trace of the error matrix as a criterion, the objective function is established to find the optimal location of the receiving station by grey wolf optimizer. The optimization parameters are also improved to increase the global search ability of the algorithm. Finally, the feasibility and reliability of the overall error model and the grey wolf algorithm proposed are verified by experiments from multiple perspectives. The station placement method proposed in this paper can effectively solve the localization problem of targets that are only known to be in a general activity region in advance, which is more realistic.

Single-hydrophone-based passive localization for vertically moving targets in shallow water

In this article, we consider underwater passive localization for vertically moving targets in shallow water. A method that utilizes parameters derived from interference structures is introduced, which enables the implementation of both range and depth estimations for targets with a single hydrophone. Underwater moving targets radiate broadband continuous spectrum signals by forming multipath signals through interface reflections, resulting in stable interference structures in the time-frequency domain of the received signals. Ray theory is utilized to derive the trajectory equations of the interference fringes. Considering the periodic characteristics of interference structures, a specialized homomorphic filter is designed to effectively restore the peaks of the fringes to reduce localization errors. Furthermore, with a priori information on the target's initial depth, the Hough Transform is used to extract the target's location information within interference fringes formed by direct signal and surface-reflected signal. Addressing the issue of multi-peaks in parameter domain, parameter information across multiple fringes is fused. The effectiveness and robustness of the proposed method have been demonstrated through numerical simulations, lake trial, and error assessments, even when underwater environments information is not precisely known.

Deep Learning Method for Precise Landmark Identification and Structural Assessment of Whole-Spine Radiographs.

This study measured parameters automatically by marking the point for measuring each parameter on whole-spine radiographs. Between January 2020 and December 2021, 1017 sequential lateral whole-spine radiographs were retrospectively obtained. Of these, 819 and 198 were used for training and testing the performance of the landmark detection model, respectively. To objectively evaluate the program's performance, 690 whole-spine radiographs from four other institutions were used for external validation. The combined dataset comprised radiographs from 857 female and 850 male patients (average age 42.2 ± 27.3 years; range 20-85 years). The landmark localizer showed the highest accuracy in identifying cervical landmarks (median error 1.5-2.4 mm), followed by lumbosacral landmarks (median error 2.1-3.0 mm). However, thoracic landmarks displayed larger localization errors (median 2.4-4.3 mm), indicating slightly reduced precision compared with the cervical and lumbosacral regions. The agreement between the deep learning model and two experts was good to excellent, with intraclass correlation coefficient values >0.88. The deep learning model also performed well on the external validation set. There were no statistical differences between datasets in all parameters, suggesting that the performance of the artificial intelligence model created was excellent. The proposed automatic alignment analysis system identified anatomical landmarks and positions of the spine with high precision and generated various radiograph imaging parameters that had a good correlation with manual measurements.

Wave packets in AdS/CFT correspondence

In this paper, we construct a general bulk wave packet in the AdS/CFT correspondence. This wave packet can be described both in bulk and conformal field theory (CFT) descriptions. Then, we compute the time evolution of the energy density of this wave-packet state on the vacuum in the CFT picture of AdS3/CFT2. We find that the energy density of the wave packet is localized at two points, which means that the bulk wave packet corresponds to two light-like particle-like objects in the CFT picture. Our result implies that the entanglement wedge reconstruction given by Almheiri [Bulk locality and quantum error correction in AdS/CFT, is invalid. Published by the American Physical Society 2024

Iris Recognition based on Statistically Bound Spatial Domain Zero Crossing and Neural Networks

Purpose The iris pattern is an important biological feature of the human body. The recognition of an individual based on an iris pattern is gaining more popularity due to the uniqueness of the pattern among the people. Iris recognition systems have received attention very much due to their rich iris texture which gives robust standards for identifying individuals. Notwithstanding this, there are several challenges in unrestricted recognition environments. Methods This article discusses a highly error-resistant technique to implement a biometric recognition system based on the iris portion of the human eye. All iris recognition algorithms of the current day face a major problem of localization errors and the enormous time involved in this localization process. Spatial domain zero crossing may be the simplest and least complex method for localization. Yet, it has not been used due to its high sensitivity to erroneous edges, as a consequence of which more complex and time-consuming algorithms have taken its place. Appropriate statistical bounds imposed on this process help this method to be the least erroneous and time-consuming. Errors were reduced to 0.022% using this approach on the CASIA v1 & v2 datasets. Time consumption in this stage was the least compared to other algorithms. At the comparison stage, most algorithms use multiple comparisons to account for translation and rotation errors. This is time-consuming and very resource-hungry. Results The current approach discusses a robust method based on a single comparison, which works with a correct recognition of over 99.78% which is clearly demonstrated by tests. Conclusions The technique is to use a neural network trained to recognize special statistical and regional parameters unique to every person’s iris. The algorithm also gives sufficient attention to consider illumination errors, elliptical pupils, excess eyelash errors and bad contrast.

Imprecise perception of hand position during early motor adaptation.

Localizing one's body parts is important for movement control and motor learning. Recent studies have shown that the precision with which people localize their hand places constraints on motor adaptation. Although these studies have assumed that hand localization remains equally precise across learning, we show that precision decreases rapidly during early motor learning. In three experiments, healthy young participants (n = 92) repeatedly adapted to a 45° visuomotor rotation for a cycle of two to four reaches, followed by a cycle of two to four reaches with veridical feedback. Participants either used an aiming strategy that fully compensated for the rotation (experiment 1), or always aimed directly at the target, so that adaptation was implicit (experiment 2). We omitted visual feedback for the last reach of each cycle, after which participants localized their unseen hand. We observed an increase in the variability of angular localization errors when subjects used a strategy to counter the visuomotor rotation (experiment 1). This decrease in precision was less pronounced in the absence of reaiming (experiment 2), and when subjects knew that they would have to localize their hand on the upcoming trial, and could thus focus on hand position (experiment 3). We propose that strategic reaiming decreases the precision of perceived hand position, possibly due to attention to vision rather than proprioception. We discuss how these dynamics in precision during early motor learning could impact on motor control and shape the interplay between implicit and strategy-based motor adaptation.NEW & NOTEWORTHY Recent studies indicate that the precision with which people localize their hand limits implicit visuomotor learning. We found that localization precision is not static, but decreases early during learning. This decrease is pronounced when people apply a reaiming strategy to compensate for a visuomotor perturbation and is partly resistant to allocation of attention to the hand. We propose that these dynamics in position sense during learning may influence how implicit and strategy-based motor adaption interact.

TDOA-based UWB indoor 1D localization via weighted sliding window filtering

Accurate localization of a person or object is a significant aspect in the field of industrial internet of things (IoT). Ultra-wideband (UWB) localization system, a promising technology, has been widely studied due to its high accuracy in 2D and 3D scenarios. Moreover, in narrow corridors and deep-well environments, the 1D localization is also an important application with the advantage of resource saving, however which attracts less attention. In this paper, we focus on the problem of 1D localization and propose a practical localization model with time difference of arrivals (TDOA). To solve the nonlinear 1D localization problem, we design an algorithm that integrates the dichotomous method and Newton's iterative method, which realizes an efficient solution. Meanwhile, in order to cope with the random errors in the real scenes, we introduce a sliding window algorithm to filter out the undesirable solutions. By observing the localization error map, we design a weighted sliding window algorithm to achieve further improvement of the localization accuracy. In addition, we derive the Cramér-Rao lower bound (CRLB) for 1D localization of TDOA. Simulations and practical experiments show that our proposed algorithm greatly benefits the localization accuracy of 1D TDOA and provides strong support for its practical application.

Exploiting Anchor Links for NLOS Combating in UWB Localization

UWB (Ultra-wideband) has been shown to be a promising technology to provide accurate positioning for the Internet of Things. However, its performance significantly degrades in practice due to Non-Line-Of-Sight (NLOS) issues. Various approaches have implicitly or explicitly explored the problem. In this article, we propose RefLoc , which leverages the unique benefits of UWB to address the NLOS problem. While we find that NLOS links can vary significantly in the same environment, LOS links possess similar features that can be captured by the high bandwidth of UWB. Specifically, the high-level idea of RefLoc is to first identify links among anchors with known positions and leverage those links as references for tag link identification. To achieve this, we address the practical challenges of deriving anchor link status, extracting qualified link features, and inferring tag links with anchor links. We implement RefLoc on commercial hardware and conduct extensive experiments in different environments. The evaluation results show that RefLoc achieves an average NLOS identification accuracy of 96% in various environments, improving the state-of-the-art by 10%, and reduces 80% localization error with little overhead.

Performance of Rotation‐Symmetric Bosonic Codes in a Quantum Repeater Network

Performance of Rotation‐Symmetric Bosonic Codes in a Quantum Repeater Network

EEG source imaging of hand movement-related areas: an evaluation of the reconstruction and classification accuracy with optimized channels

The hand motor activity can be identified and converted into commands for controlling machines through a brain-computer interface (BCI) system. Electroencephalography (EEG) based BCI systems employ electrodes to measure the electrical brain activity projected at the scalp and discern patterns. However, the volume conduction problem attenuates the electric potential from the brain to the scalp and introduces spatial mixing to the signals. EEG source imaging (ESI) techniques can be applied to alleviate these issues and enhance the spatial segregation of information. Despite this potential solution, the use of ESI has not been extensively applied in BCI systems, largely due to accuracy concerns over reconstruction accuracy when using low-density EEG (ldEEG), which is commonly used in BCIs. To overcome these accuracy issues in low channel counts, recent studies have proposed reducing the number of EEG channels based on optimized channel selection. This work presents an evaluation of the spatial and temporal accuracy of ESI when applying optimized channel selection towards ldEEG number of channels. For this, a simulation study of source activity related to hand movement has been performed using as a starting point an EEG system with 339 channels. The results obtained after optimization show that the activity in the concerned areas can be retrieved with a spatial accuracy of 3.99, 10.69, and 14.29 mm (localization error) when using 32, 16, and 8 channel counts respectively. In addition, the use of optimally selected electrodes has been validated in a motor imagery classification task, obtaining a higher classification performance when using 16 optimally selected channels than 32 typical electrode distributions under 10–10 system, and obtaining higher classification performance when combining ESI methods with the optimal selected channels.

A Study on Scan Trajectory Based Effective Localization Method Using Mobile Anchor in Wireless Sensor Networks

A Wireless Sensor Network (WSN) is a collection of different nodes working together in a networked fashion in an ordered way. For many applications, sensor location in wireless sensor networks (WSN) is crucial. It is expensive to equip each sensor node with a GPS receiver. Numerous methods, including range-based and range-free methods, have been put forth in the past to determine the locations of sensor nodes that are randomly distributed. The majority of them make use of unique nodes known as anchor nodes, which are thought to be aware of their own positions. These anchor nodes supply the information that other sensors use to calculate their locations. Massive numbers of sensor nodes are used in wireless sensor networks (WSNs) to gather data about the immediate environment. However, this data is useless until the precise location of the data collection points is known. Sensor node localization in wireless sensor networks (WSNs) is important for a number of applications. The current research study develops a path planning technique to apply mobile anchors to the localization systems that are currently in use. The two mobile anchors supported by the effective trajectories, or effective SCAN, is the focus of the proposed research project. To localise the sensor node and transmit data, this method involves setting the mobile anchors to move in the opposite direction. The advantages of this dual movable anchor are demonstrated by the research study in terms of decreased localization time, localization error, and other factors.

A Time Headway Control Scheme for Virtually Coupled Heavy Haul Freight Trains

Abstract Virtual coupling of railway trains is an emerging technology that has the potential to significantly increase railway operational efficiency by reducing the train following distance from absolute braking distances to relative braking distances. Current research in this topic is mainly focused on passenger trains and uses distance-based headways. This paper studied virtual coupling for heavy haul freight trains and demonstrated that the distance headway scheme was challenging and sometimes impractical for heavy haul trains to achieve virtual coupling. A time-based headway scheme was then proposed to set the follower train to be a certain time behind the schedule of the leader train rather than a distance headway. The time-based headway required the follower train to reproduce the leader train's operational status at the same track location. This also allowed the follower train to copy any optimized train driving strategies from the leader train. Demonstrative simulations were carried out without the consideration of communication errors and train localization errors. The results show that a conventional distance headway simulation had maximum distance and speed errors of 716 m (36%, reference 2 km) and 24 km/h (66%, reference 36 km/h), respectively. A time-based headway simulation reduced the maximum distance and speed errors to 0.07 m (0%, reference 2 km) and 0.1 km/h (9%, reference 1.18 km/h), respectively.

Ice Shape Convergence in Multistep Ice Accretion Simulations

Local errors in the geometrical description of the ice front are amplified in multistep simulations over conformal meshes due to the coupling of aerodynamics, water impingement, ice accretion, and grid deformation. Small perturbations in the initial phase of ice formation possibly result in dramatically different ice shapes, which can hinder the stability of the multistep procedure. This problem is analyzed by investigating the combined effects of space and time discretization on the ice growth over airfoils and three-dimensional wings. We propose an automatic procedure for selecting the time interval for the update of the aerodynamics and particle impingement. A local growth limiter λ is introduced here to bound the local ice thickness growth to be comparable to the local grid spacing on the surface, resulting in an automatic adaptive time step to be used in the multistep simulation. Examples are provided for three-dimensional cases under both rime and glaze conditions over straight and swept wings. These examples highlight the different accretion mechanisms of the two ice regimes and preliminarily indicate that ice-shape convergence can be achieved for low values of λ.

Fast loop closure detection using probabilistic integration of pose and appearance similarity

Loop closure detection is a key technique for robots to minimize the accumulated localization and mapping errors after long-time explorations of simultaneous localization and mapping. However, the requirement for efficiency and accuracy performance for mobile robot applications is not well satisfied. In this article, we propose a fast and accurate loop closure detection method by exploiting both pose-based and appearance-based information in a probabilistic manner, inspired by the complementarity between the pose-based and the appearance-based information. Our approach formulates a probability framework combing the pose-based loop closure detection probability and the appearance-based loop closure detection probability. In the proposed framework, the pose-based loop closure detection model is firstly derived from the nonlinear optimization model of odometry. Then the appearance-based loop similarity and the pose-based loop similarity are combined into a joint framework to improve the loop closure detection performance. We implemented our approach using C++ and ROS and thoroughly tested it on the publicly available datasets. The experiments presented in this article suggest that the proposed method can achieve high efficiency and accuracy performance on loop closure detection.

High-speed PGC demodulation model and method with subnanometer displacement resolution in a fiber-optic micro-probe laser interferometer

As the key of embedded displacement measurement, a fiber-optic micro-probe laser interferometer (FMI) is of great interest in developing high-end equipment as well as precision metrology. However, conventional phase-generated carrier (PGC) approaches are for low-speed scenes and local error analysis, usually neglecting the global precision analysis and dynamic effect of system parameters under high-speed measurement, thus hindering their broad applications. We present a high-speed PGC demodulation model and method to achieve subnanometer displacement measurement precision in FMI. This model includes a global equivalent resolution analysis and revelation of the demodulation error mechanism. Utilizing this model, the failure issues regarding the PGC demodulation method under high speed and large range are addressed. Furthermore, an ultra-precision PGC demodulation algorithm based on the combination of static and dynamic delay adaptive regulation is proposed to enable high-speed and large-range displacement measurement. In this paper, the proposed model and algorithm are validated through simulation and experimental tests. The results demonstrate a displacement resolution of 0.1 nm with a standard deviation of less than 0.5 nm when measuring at a high velocity of 1.5 m/s—nearly a tenfold increase of the latest study.

Kinematic accuracy reliability evaluation method of space deployable mechanism based on dynamic model

Space deployable mechanisms are widely used in aerospace missions. The uncertainties in the geometric or physical parameters seriously affect the kinematic accuracy of the mechanism during the deployment process. In this paper, a kinematic accuracy reliability evaluation method of the space deployable mechanism based on dynamic model and adaptive hybrid surrogate models is presented. Firstly, the dynamic model of the space deployable mechanism is established based on the Lagrange method, and the motion error function considering the factors such as the mass, dimension and load is established. In order to improve the efficiency, an adaptive hybrid surrogate model reliability evaluation method based on improved weight measurement method is further proposed. The weight measurement method considers the global errors and local errors of the models. Finally, the Monte Carlo method is used to verify the accuracy of the hybrid surrogate model method. The relative error of the failure probability calculated by the proposed method is within 2.5 %, indicating that the proposed method has high calculation accuracy and efficiency.

Improving Needle Tip Tracking and Detection in Ultrasound-Based Navigation System Using Deep Learning-Enabled Approach.

Ultrasound-guided percutaneous interventions have numerous advantages over traditional techniques. Accurate needle placement in the target anatomy is crucial for successful intervention, and reliable visual information is essential to achieve this. However, previous studies have revealed several challenges, such as the variability in needle echogenicity and the common misalignment of the ultrasound beam and the needle. Advanced techniques have been developed to optimize needle visualization, including hardware-based and image-processing-based methods. This paper proposes a novel strategy of integrating ultrasound-based deep learning approaches into an optical navigation system to enhance needle visualization and improve tip positioning accuracy. Both the tracking and detection algorithms are optimized utilizing optical tracking information. The information is introduced into the tracking network to define the search patch update strategy and form a trajectory reference to correct tracking results. In the detection network, the original image is processed according to the needle insertion position and current position given by the optical localization system to locate a coarse region, and the depth-score criterion is adopted to optimize detection results. Extensive experiments demonstrate that our approach achieves promising tip tracking and detection performance with tip localization errors of 1.11 ± 0.59 mm and 1.17 ± 0.70 mm, respectively. Moreover, we establish a paired dataset consisting of ultrasound images and their corresponding spatial tip coordinates acquired from the optical tracking system and conduct real puncture experiments to verify the effectiveness of the proposed methods. Our approach significantly improves needle visualization and provides physicians with visual guidance for posture adjustment.