Reducing GPS Impreciseness by Odometer Sensor Reading to Improve Localization Accuracy

Twelve blindfolded subjects localized two different pure tones, randomly played by eight sound sources in the horizontal plane. Either subjects could get information supplied by their pinnae (external ear) and their head movements or not. We found that pinnae, as well as head movements, had a marked influence on auditory localization performance with this type of sound. Effects of pinnae and head movements seemed to be additive; the absence of one or the other factor provoked the same loss of localization accuracy and even much the same error pattern. Head movement analysis showed that subjects turn their face towards the emitting sound source, except for sources exactly in the front or exactly in the rear, which are identified by turning the head to both sides. The head movement amplitude increased smoothly as the sound source moved from the anterior to the posterior quadrant.

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Localization accuracy of farmland wireless sensor network localization algorithm based on received signal strength indicator

Ingénierie des systèmes d information ◽

10.3166/isi.23.5.69-80 ◽

2018 ◽

Vol 23 (5) ◽

pp. 69-80 ◽

Cited By ~ 1

Author(s):

Yisheng MIAO ◽

Huarui WU ◽

Huaji ZHU ◽

Yuling SONG

Keyword(s):

Wireless Sensor Network ◽

Sensor Network ◽

Signal Strength ◽

Received Signal Strength ◽

Wireless Sensor ◽

Localization Algorithm ◽

Received Signal Strength Indicator ◽

Sensor Network Localization ◽

Localization Accuracy ◽

Network Localization

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A Comparative Study of Computational Intelligence Algorithms for Sensor Localization

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327909666181206103304 ◽

2019 ◽

Vol 9 (2) ◽

pp. 224-236

Author(s):

Vaishali R. Kulkarni ◽

Veena Desai ◽

Raghavendra Kulkarni

Keyword(s):

Firefly Algorithm ◽

Heuristic Algorithms ◽

Computing Time ◽

Sensor Nodes ◽

Convergence Time ◽

Localization Error ◽

Location Awareness ◽

Cultural Algorithm ◽

Localization Accuracy ◽

Multi Stage

Background & Objective: Location of sensors is an important information in wireless sensor networks for monitoring, tracking and surveillance applications. The accurate and quick estimation of the location of sensor nodes plays an important role. Localization refers to creating location awareness for as many sensor nodes as possible. Multi-stage localization of sensor nodes using bio-inspired, heuristic algorithms is the central theme of this paper. Methodology: Biologically inspired heuristic algorithms offer the advantages of simplicity, resourceefficiency and speed. Four such algorithms have been evaluated in this paper for distributed localization of sensor nodes. Two evolutionary computation-based algorithms, namely cultural algorithm and the genetic algorithm, have been presented to optimize the localization process for minimizing the localization error. The results of these algorithms have been compared with those of swarm intelligence- based optimization algorithms, namely the firefly algorithm and the bee algorithm. Simulation results and analysis of stage-wise localization in terms of number of localized nodes, computing time and accuracy have been presented. The tradeoff between localization accuracy and speed has been investigated. Results: The comparative analysis shows that the firefly algorithm performs the localization in the most accurate manner but takes longest convergence time. Conclusion: Further, the cultural algorithm performs the localization in a very quick time; but, results in high localization error.

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Imitation learning-based framework for learning 6-D linear compliant motions

Autonomous Robots ◽

10.1007/s10514-021-09971-y ◽

2021 ◽

Author(s):

Markku Suomalainen ◽

Fares J. Abu-dakka ◽

Ville Kyrki

Keyword(s):

Contact Force ◽

Free Space ◽

Prior Information ◽

Goal Area ◽

Imitation Learning ◽

Learning From Demonstration ◽

End Effector ◽

Localization Accuracy ◽

Novel Method ◽

Contact Tasks

AbstractWe present a novel method for learning from demonstration 6-D tasks that can be modeled as a sequence of linear motions and compliances. The focus of this paper is the learning of a single linear primitive, many of which can be sequenced to perform more complex tasks. The presented method learns from demonstrations how to take advantage of mechanical gradients in in-contact tasks, such as assembly, both for translations and rotations, without any prior information. The method assumes there exists a desired linear direction in 6-D which, if followed by the manipulator, leads the robot’s end-effector to the goal area shown in the demonstration, either in free space or by leveraging contact through compliance. First, demonstrations are gathered where the teacher explicitly shows the robot how the mechanical gradients can be used as guidance towards the goal. From the demonstrations, a set of directions is computed which would result in the observed motion at each timestep during a demonstration of a single primitive. By observing which direction is included in all these sets, we find a single desired direction which can reproduce the demonstrated motion. Finding the number of compliant axes and their directions in both rotation and translation is based on the assumption that in the presence of a desired direction of motion, all other observed motion is caused by the contact force of the environment, signalling the need for compliance. We evaluate the method on a KUKA LWR4+ robot with test setups imitating typical tasks where a human would use compliance to cope with positional uncertainty. Results show that the method can successfully learn and reproduce compliant motions by taking advantage of the geometry of the task, therefore reducing the need for localization accuracy.

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Accuracy analysis of BLE beacon-based localization in smart buildings

Journal of Ambient Intelligence and Smart Environments ◽

10.3233/ais-210607 ◽

2021 ◽

pp. 1-20

Author(s):

Rosen Ivanov

Keyword(s):

Use Case ◽

Accuracy Analysis ◽

Localization Algorithm ◽

Computing Power ◽

Smart Buildings ◽

Localization Accuracy ◽

Positioning Systems ◽

Deployment Strategy ◽

Accurate Localization ◽

Localization Algorithms

The majority of services that deliver personalized content in smart buildings require accurate localization of their clients. This article presents an analysis of the localization accuracy using Bluetooth Low Energy (BLE) beacons. The aim is to present an approach to create accurate Indoor Positioning Systems (IPS) using algorithms that can be implemented in real time on platforms with low computing power. Parameters on which the localization accuracy mostly depends are analyzed: localization algorithm, beacons’ density, deployment strategy, and noise in the BLE channels. An adaptive algorithm for pre-processing the signals from the beacons is proposed, which aims to reduce noise in beacon’s data and to capture visitor’s dynamics. The accuracy of five range-based localization algorithms in different use case scenarios is analyzed. Three of these algorithms are specially designed to be less sensitive to noise in radio channels and require little computing power. Experiments conducted in a simulated and real environment show that using proposed algorithms the localization accuracy less than 1 m can be obtained.

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Improved Visual Localization via Graph Filtering

Journal of Imaging ◽

10.3390/jimaging7020020 ◽

2021 ◽

Vol 7 (2) ◽

pp. 20

Author(s):

Carlos Lassance ◽

Yasir Latif ◽

Ravi Garg ◽

Vincent Gripon ◽

Ian Reid

Keyword(s):

Image Retrieval ◽

Large Scale ◽

Visual Localization ◽

Localization Accuracy ◽

Additional Information ◽

Feature Representations ◽

The Mean ◽

Classical Image ◽

Vision Based Localization ◽

Improve Reliability

Vision-based localization is the problem of inferring the pose of the camera given a single image. One commonly used approach relies on image retrieval where the query input is compared against a database of localized support examples and its pose is inferred with the help of the retrieved items. This assumes that images taken from the same places consist of the same landmarks and thus would have similar feature representations. These representations can learn to be robust to different variations in capture conditions like time of the day or weather. In this work, we introduce a framework which aims at enhancing the performance of such retrieval-based localization methods. It consists in taking into account additional information available, such as GPS coordinates or temporal proximity in the acquisition of the images. More precisely, our method consists in constructing a graph based on this additional information that is later used to improve reliability of the retrieval process by filtering the feature representations of support and/or query images. We show that the proposed method is able to significantly improve the localization accuracy on two large scale datasets, as well as the mean average precision in classical image retrieval scenarios.

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Probability-based diagnostic imaging with corrected weight distribution for damage detection of stiffened composite panel

Structural Health Monitoring ◽

10.1177/14759217211033967 ◽

2021 ◽

pp. 147592172110339

Author(s):

Guoqiang Liu ◽

Binwen Wang ◽

Li Wang ◽

Yu Yang ◽

Xiaguang Wang

Keyword(s):

Distribution Function ◽

Diagnostic Imaging ◽

Weight Distribution ◽

Composite Structures ◽

Damage Identification ◽

Guided Wave ◽

Composite Panel ◽

Damage Localization ◽

Localization Accuracy ◽

Direct Interpretation

Due to no requirement for direct interpretation of the guided wave signal, probability-based diagnostic imaging (PDI) algorithm is especially suitable for damage identification of complex composite structures. However, the weight distribution function of PDI algorithm is relatively inaccurate. It can reduce the damage localization accuracy. In order to improve the damage localization accuracy, an improved PDI algorithm is proposed. In the proposed algorithm, the weight distribution function is corrected by the acquired relative distances from defects to all actuator–sensor pairs and the reduction of the weight distribution areas. The validity of the proposed algorithm is assessed by identifying damages at different locations on a stiffened composite panel. The results show that the proposed algorithm can identify damage of a stiffened composite panel accurately.

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Path Planning for Mobile-Anchor Based Wireless Sensor Networks Localization: Obstacle-Presence Schemes

Sensors ◽

10.3390/s21113697 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3697

Author(s):

Dogan Yildiz ◽

Serap Karagol

Keyword(s):

Path Planning ◽

Negative Impact ◽

Wireless Sensor ◽

Localization Accuracy ◽

Planning Models ◽

Main Challenge ◽

Current Location ◽

Proposed Model ◽

Logical Method ◽

Weighted Centroid Localization

In many Wireless Sensor Network (WSN) applications, the location of the nodes in the network is required. A logical method to find Unknown Nodes (UNNs) in the network is to use one or several mobile anchors (MAs) equipped with GPS units moving between UNNs and periodically broadcast their current location. The main challenge at this stage is to design an optimum path to estimate the locations of UNNs as accurately as possible, reach all nodes in the network, and complete the localization process as quickly as possible. This article proposes a new path planning approach for MA-based localization called Nested Hexagon Curves (NHexCurves). The proposed model’s performance is compared with the performance of five existing static path planning models using Weighted Centroid Localization (WCL) and Accuracy Priority Trilateration (APT) localization techniques in the obstacle-presence scenario. With the obstacle-handling trajectories used for the models, the negative impact of the obstacle on the localization is reduced. The proposed model provides full coverage and high localization accuracy in the obstacle-presence scenario. The simulation results show the advantages of the proposed path planning model with the H-curve model over existing schemes.

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EEG Source Imaging (ESI) utility in clinical practice

Biomedical Engineering / Biomedizinische Technik ◽

10.1515/bmt-2019-0128 ◽

2020 ◽

Vol 65 (6) ◽

pp. 673-682

Author(s):

Pegah Khosropanah ◽

Eric Tatt-Wei Ho ◽

Kheng-Seang Lim ◽

Si-Lei Fong ◽

Minh-An Thuy Le ◽

...

Keyword(s):

Focal Epilepsy ◽

Cost Effective ◽

Head Model ◽

Computational Technique ◽

Epileptogenic Zone ◽

Clinical Tool ◽

Source Imaging ◽

Localization Accuracy ◽

Eeg Source Imaging ◽

Surgical Evaluation

AbstractEpilepsy surgery is an important treatment modality for medically refractory focal epilepsy. The outcome of surgery usually depends on the localization accuracy of the epileptogenic zone (EZ) during pre-surgical evaluation. Good localization can be achieved with various electrophysiological and neuroimaging approaches. However, each approach has its own merits and limitations. Electroencephalography (EEG) Source Imaging (ESI) is an emerging model-based computational technique to localize cortical sources of electrical activity within the brain volume, three-dimensionally. ESI based pre-surgical evaluation gives an overall clinical yield of 73–91%, depending on choice of head model, inverse solution and EEG electrode density. It is a cost effective, non-invasive method which provides valuable additional information in presurgical evaluation due to its high localizing value specifically in MRI-negative cases, extra or basal temporal lobe epilepsy, multifocal lesions such as tuberous sclerosis or cases with multiple hypotheses. Unfortunately, less than 1% of surgical centers in developing countries use this method as a part of pre-surgical evaluation. This review promotes ESI as a useful clinical tool especially for patients with lesion-negative MRI to determine EZ cost-effectively with high accuracy under the optimized conditions.

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Improving the performance of a radio-frequency localization system in adverse outdoor applications

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-021-02001-6 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Marcelo N. de Sousa ◽

Ricardo Sant’Ana ◽

Rigel P. Fernandes ◽

Julio Cesar Duarte ◽

José A. Apolinário ◽

...

Keyword(s):

Random Forest ◽

Ray Tracing ◽

Real World ◽

Practical Implication ◽

Real Life ◽

Simulated Data ◽

Real Data ◽

Gradient Boosting ◽

Real World Data ◽

Localization Accuracy

AbstractIn outdoor RF localization systems, particularly where line of sight can not be guaranteed or where multipath effects are severe, information about the terrain may improve the position estimate’s performance. Given the difficulties in obtaining real data, a ray-tracing fingerprint is a viable option. Nevertheless, although presenting good simulation results, the performance of systems trained with simulated features only suffer degradation when employed to process real-life data. This work intends to improve the localization accuracy when using ray-tracing fingerprints and a few field data obtained from an adverse environment where a large number of measurements is not an option. We employ a machine learning (ML) algorithm to explore the multipath information. We selected algorithms random forest and gradient boosting; both considered efficient tools in the literature. In a strict simulation scenario (simulated data for training, validating, and testing), we obtained the same good results found in the literature (error around 2 m). In a real-world system (simulated data for training, real data for validating and testing), both ML algorithms resulted in a mean positioning error around 100 ,m. We have also obtained experimental results for noisy (artificially added Gaussian noise) and mismatched (with a null subset of) features. From the simulations carried out in this work, our study revealed that enhancing the ML model with a few real-world data improves localization’s overall performance. From the machine ML algorithms employed herein, we also observed that, under noisy conditions, the random forest algorithm achieved a slightly better result than the gradient boosting algorithm. However, they achieved similar results in a mismatch experiment. This work’s practical implication is that multipath information, once rejected in old localization techniques, now represents a significant source of information whenever we have prior knowledge to train the ML algorithm.

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