A distributed localization method for mobile nodes

Abstract In Wireless Sensor Networks(WSNs), the location services are the basis of many application scenarios. However, for the range-based localization method, the localization accuracy and the system robustness of the distributed localization system are difficult to guarantee, due to the uncertainty of the distance estimation and position calculation are affected by the node state and communication uncertainty. In this paper, we propose the distributed localization method based on anchor node selection and Particle Filter optimization. In this method, we analyze the uncertainty of error propagation in the Least-squares method and find that there is a proportional relation between localization error and uncertainty propagation. According to this relationship, we propose the corresponding optimization criterion methods of anchor nodes. To optimize the initial localization results, we present the distributed localization method based on anchor node optimal selection and Particle Filter. Simulation results show that the methods we proposed could effectively improve the localization accuracy of the mobile nodes and the robustness of the system.

Download Full-text

Three-Dimensional Localization Algorithm of WSN Nodes Based on RSSI-TOA and Single Mobile Anchor Node

Journal of Electrical and Computer Engineering ◽

10.1155/2019/4043106 ◽

2019 ◽

Vol 2019 ◽

pp. 1-8 ◽

Cited By ~ 5

Author(s):

Lieping Zhang ◽

Zhenyu Yang ◽

Shenglan Zhang ◽

Huanhuan Yang

Keyword(s):

Three Dimensional ◽

Estimation Method ◽

Wireless Sensor ◽

Time Of Arrival ◽

Localization Algorithm ◽

Anchor Node ◽

Localization Accuracy ◽

Localization Method ◽

Mobile Anchor ◽

Mobile Anchor Node

Aimed at the shortcomings of low localization accuracy of the fixed multianchor method, a three-dimensional localization algorithm for wireless sensor network nodes is proposed in this paper, which combines received signal strength indicator (RSSI) and time of arrival (TOA) ranging information and single mobile anchor node. A mobile anchor node was introduced in the proposed three-dimensional localization algorithm for wireless sensor networks firstly, and the mobile anchor node moves according to the Gauss–Markov three-dimensional mobility model. Then, based on the idea of using RSSI ranging in the near end and TOA ranging in the far end, a ranging method combining RSSI and TOA ranging information is proposed to obtain the precise distance between the anchor node and the unknown node. Finally, the maximum-likelihood estimation method is used to estimate the position of unknown nodes based on the obtained ranging values. The MATLAB simulation results show that the proposed algorithm had a higher localization accuracy and lower localization energy consumption compared with the traditional RSSI localization method or TOA localization method.

Download Full-text

Localization and Non-Localization Node Identification Using Dream in MANET

SMART MOVES JOURNAL IJOSCIENCE ◽

10.24113/ijoscience.v2i2.77 ◽

2016 ◽

Vol 2 (2) ◽

Author(s):

Amit Singh ◽

Nitin Mishra ◽

Angad Singh

Keyword(s):

Ad Hoc Network ◽

Ad Hoc ◽

Mobile Ad Hoc Network ◽

Fundamental Problem ◽

Identification Algorithm ◽

Mobile Nodes ◽

Position Information ◽

Location Services ◽

Wireless Router ◽

Mobile Ad Hoc

A Wireless Mobile Ad-hoc Network consists of variety of mobile nodes that temporally kind a dynamic infrastructure less network. To modify communication between nodes that don’t have direct radio contact, every node should operate as a wireless router and potential forward knowledge traffic of behalf of the opposite node. In MANET Localization is a fundamental problem. Current localization algorithm mainly focuses on checking the localizability of a network and/or how to localize as many nodes as possible. It could provide accurate position information foe kind of expanding application. Localization provide information about coverage, deployment, routing, location, services, target tracking and rescue If high mobility among the mobile nodes occurs path failure breaks. Hence the location information cannot be predicted. Here we have proposed a localization based algorithm which will help to provide information about the localized and non-localized nodes in a network. In the proposed approach DREAM protocol and AODV protocol are used to find the localizability of a node in a network. DREAM protocol is a location protocol which helps to find the location of a node in a network whereas AODV is a routing protocol it discover route as and when necessary it does not maintain route from every node to every other. To locate the mobile nodes in a n/w an node identification algorithm is used. With the help of this algorithm localized and non-localized node can be easily detected in respect of radio range. This method helps to improve the performance of a module and minimize the location error and achieves improved performance in the form of UDP packet loss, received packet and transmitted packets, throughput, routing overhead, packet delivery fraction. All the simulation done through the NS-2 module and tested the mobile ad-hoc network.

Download Full-text

An Improved Localization Method for the Transition between Autonomous Underwater Vehicle Homing and Docking

Sensors ◽

10.3390/s21072468 ◽

2021 ◽

Vol 21 (7) ◽

pp. 2468

Author(s):

Ri Lin ◽

Feng Zhang ◽

Dejun Li ◽

Mingwei Lin ◽

Gengli Zhou ◽

...

Keyword(s):

Data Exchange ◽

Autonomous Underwater Vehicle ◽

Autonomous Underwater Vehicles ◽

Optimization Method ◽

Short Baseline ◽

Localization Accuracy ◽

Localization Method ◽

Mapping Algorithm ◽

Localization And Mapping ◽

Update Rate

Docking technology for autonomous underwater vehicles (AUVs) involves energy supply, data exchange and navigation, and plays an important role to extend the endurance of the AUVs. The navigation method used in the transition between AUV homing and docking influences subsequent tasks. How to improve the accuracy of the navigation in this stage is important. However, when using ultra-short baseline (USBL), outliers and slow localization updating rates could possibly cause localization errors. Optical navigation methods using underwater lights and cameras are easily affected by the ambient light. All these may reduce the rate of successful docking. In this paper, research on an improved localization method based on multi-sensor information fusion is carried out. To improve the localization performance of AUVs under motion mutation and light variation conditions, an improved underwater simultaneous localization and mapping algorithm based on ORB features (IU-ORBSALM) is proposed. A nonlinear optimization method is proposed to optimize the scale of monocular visual odometry in IU-ORBSLAM and the AUV pose. Localization tests and five docking missions are executed in a swimming pool. The localization results indicate that the localization accuracy and update rate are both improved. The 100% successful docking rate achieved verifies the feasibility of the proposed localization method.

Download Full-text

Method of Optimization for Target Localization Model Parameters Based on LSSVR

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.268-270.934 ◽

2011 ◽

Vol 268-270 ◽

pp. 934-939

Author(s):

Xue Wen He ◽

Gui Xiong Liu ◽

Hai Bing Zhu ◽

Xiao Ping Zhang

Keyword(s):

Particle Swarm Optimization ◽

Fitness Function ◽

Particle Swarm ◽

Parameters Optimization ◽

Target Localization ◽

Likelihood Method ◽

Support Vector ◽

Swarm Optimization ◽

Localization Accuracy ◽

Localization Method

Aiming at improving localization accuracy in Wireless Sensor Networks (WSN) based on Least Square Support Vector Regression (LSSVR), making LSSVR localization method more practicable, the mechanism of effects of the kernel function for target localization based on LSSVR is discussed based on the mathematical solution process of LSSVR localization method. A novel method of modeling parameters optimization for LSSVR model using particle swarm optimization is proposed. Construction method of fitness function for modeling parameters optimization is researched. In addition, the characteristics of particle swarm parameters optimization are analyzed. The computational complexity of parameters optimization is taken into consideration comprehensively. Experiments of target localization based on CC2430 show that localization accuracy using LSSVR method with modeling parameters optimization increased by 23%~36% in compare with the maximum likelihood method(MLE) and the localization error is close to the minimum with different LSSVR modeling parameters. Experimental results show that adapting a reasonable fitness function for modeling parameters optimization using particle swarm optimization could enhance the anti-noise ability significantly and improve the LSSVR localization performance.

Download Full-text

Using UHF-RFID Signals for Robot Localization Inside Pipelines

10.36227/techrxiv.14658795 ◽

2021 ◽

Author(s):

Amal Gunatilake ◽

Karthick Thiyagarajan ◽

sarath kodagoda ◽

Lasitha Piyathilaka ◽

Poojaben Darji

Keyword(s):

Particle Filter ◽

Cost Effective ◽

Measurement Model ◽

Underground Water ◽

Water Utilities ◽

Robot Localization ◽

Uhf Rfid ◽

Localization Accuracy ◽

Feature Based ◽

Particle Filter Algorithm

<div>Underground water pipes are important to any country’s infrastructure. Overtime, the metallic pipes are prone to corrosion, which can lead to water leakage and pipe bursts. In order to prolong the service life of those assets, water utilities in Australia apply protective pipe linings. Long-term monitoring and timely intervention are crucial for maintaining those lining assets. However, the water utilities do not possess the comprehensive technology to achieve it. The main reasons for lacking such technology are the unavailability of sensors and accurate robot localization technologies. Feature based localization methods such as SLAM has limited use as the application of liners alters the features and the environment. Encoder based localization is not accurate enough to observe the evolution of defects over a long period of time requiring unique defect correspondence. This motivates us to explore accurate contact-less and wireless based localization methods. We propose a cost-effective localization method using UHFRFID signals for robot localization inside pipelines based on Gaussian process combined particle filter. Experiments carried out in field extracted pipe samples from the Sydney water pipe network show that using the RSSI and Phase data together in the measurement model with particle filter algorithm improves the localization accuracy up to 15 centimeters precision.</div>

Download Full-text

Core point pixel-level localization by fingerprint features in spatial domain

Mathematical Biosciences and Engineering ◽

10.3934/mbe.2022032 ◽

2022 ◽

Vol 19 (1) ◽

pp. 707-737

Author(s):

Xueyi Ye ◽

◽

Yuzhong Shen ◽

Maosheng Zeng ◽

Yirui Liu ◽

...

Keyword(s):

Singular Point ◽

Reference Point ◽

False Positive ◽

Spatial Domain ◽

Fingerprint Recognition ◽

Localization Accuracy ◽

Localization Method ◽

Core Point ◽

Point Detection ◽

Fingerprint Alignment

<abstract> <p>Singular point detection is a primary step in fingerprint recognition, especially for fingerprint alignment and classification. But in present there are still some problems and challenges such as more false-positive singular points or inaccurate reference point localization. This paper proposes an accurate core point localization method based on spatial domain features of fingerprint images from a completely different viewpoint to improve the fingerprint core point displacement problem of singular point detection. The method first defines new fingerprint features, called furcation and confluence, to represent specific ridge/valley distribution in a core point area, and uses them to extract the innermost Curve of ridges. The summit of this Curve is regarded as the localization result. Furthermore, an approach for removing false Furcation and Confluence based on their correlations is developed to enhance the method robustness. Experimental results show that the proposed method achieves satisfactory core localization accuracy in a large number of samples.</p> </abstract>

Download Full-text

A Novel Passive Indoor Localization Method by Fusion CSI Amplitude and Phase Information

Sensors ◽

10.3390/s19040875 ◽

2019 ◽

Vol 19 (4) ◽

pp. 875 ◽

Cited By ~ 10

Author(s):

Xiaochao Dang ◽

Xiong Si ◽

Zhanjun Hao ◽

Yaning Huang

Keyword(s):

Indoor Localization ◽

Rapid Development ◽

Location Based Services ◽

Signal Interference ◽

Indoor Environments ◽

Phase Information ◽

Localization Accuracy ◽

Localization Method ◽

Indoor Location ◽

Conference Room

With the rapid development of wireless network technology, wireless passive indoor localization has become an increasingly important technique that is widely used in indoor location-based services. Channel state information (CSI) can provide more detailed and specific subcarrier information, which has gained the attention of researchers and has become an emphasis in indoor localization technology. However, existing research has generally adopted amplitude information for eigenvalue calculations. There are few research studies that have used phase information from CSI signals for localization purposes. To eliminate the signal interference existing in indoor environments, we present a passive human indoor localization method named FapFi, which fuses CSI amplitude and phase information to fully utilize richer signal characteristics to find location. In the offline stage, we filter out redundant values and outliers in the CSI amplitude information and then process the CSI phase information. A fusion method is utilized to store the processed amplitude and phase information as a fingerprint database. The experimental data from two typical laboratory and conference room environments were gathered and analyzed. The extensive experimental results demonstrate that the proposed algorithm is more efficient than other algorithms in data processing and achieves decimeter-level localization accuracy.

Download Full-text

A Method of Multiple Dynamic Objects Identification and Localization Based on Laser and RFID

Sensors ◽

10.3390/s20143948 ◽

2020 ◽

Vol 20 (14) ◽

pp. 3948 ◽

Cited By ~ 2

Author(s):

Wenpeng Fu ◽

Ran Liu ◽

Heng Wang ◽

Rashid Ali ◽

Yongping He ◽

...

Keyword(s):

Particle Filter ◽

Indoor Environment ◽

Pearson Correlation ◽

Object Identification ◽

Radial Velocities ◽

Laser Range Finder ◽

Range Finder ◽

Localization Accuracy ◽

Laser Range ◽

Dynamic Objects

In an indoor environment, object identification and localization are paramount for human-object interaction. Visual or laser-based sensors can achieve the identification and localization of the object based on its appearance, but these approaches are computationally expensive and not robust against the environment with obstacles. Radio Frequency Identification (RFID) has a unique tag ID to identify the object, but it cannot accurately locate it. Therefore, in this paper, the data of RFID and laser range finder are fused for the better identification and localization of multiple dynamic objects in an indoor environment. The main method is to use the laser range finder to estimate the radial velocities of objects in a certain environment, and match them with the object’s radial velocities estimated by the RFID phase. The method also uses a fixed time series as “sliding time window” to find the cluster with the highest similarity of each RFID tag in each window. Moreover, the Pearson correlation coefficient (PCC) is used in the update stage of the particle filter (PF) to estimate the moving path of each cluster in order to improve the accuracy in a complex environment with obstacles. The experiments were verified by a SCITOS G5 robot. The results show that this method can achieve an matching rate of 90.18% and a localization accuracy of 0.33m in an environment with the presence of obstacles. This method effectively improves the matching rate and localization accuracy of multiple objects in indoor scenes when compared to the Bray-Curtis (BC) similarity matching-based approach as well as the particle filter-based approach.

Download Full-text

A Multiscale Constraints Method Localization of 3D Facial Feature Points

Computational and Mathematical Methods in Medicine ◽

10.1155/2015/178102 ◽

2015 ◽

Vol 2015 ◽

pp. 1-6

Author(s):

Hong-an Li ◽

Yongxin Zhang ◽

Zhanli Li ◽

Huilin Li

Keyword(s):

Facial Feature ◽

Feature Point ◽

Distribution Model ◽

Feature Points ◽

Widespread Application ◽

Point Distribution ◽

Localization Accuracy ◽

Localization Method ◽

Relative Angle ◽

Point Distribution Model

It is an important task to locate facial feature points due to the widespread application of 3D human face models in medical fields. In this paper, we propose a 3D facial feature point localization method that combines the relative angle histograms with multiscale constraints. Firstly, the relative angle histogram of each vertex in a 3D point distribution model is calculated; then the cluster set of the facial feature points is determined using the cluster algorithm. Finally, the feature points are located precisely according to multiscale integral features. The experimental results show that the feature point localization accuracy of this algorithm is better than that of the localization method using the relative angle histograms.

Download Full-text

AGV Localization System Based on Ultra-Wideband and Vision Guidance

Electronics ◽

10.3390/electronics9030448 ◽

2020 ◽

Vol 9 (3) ◽

pp. 448 ◽

Cited By ~ 2

Author(s):

Xiaohao Hu ◽

Zai Luo ◽

Wensong Jiang

Keyword(s):

Monocular Vision ◽

Ultra Wideband ◽

Localization Algorithm ◽

Localization Accuracy ◽

Localization Method ◽

Localization System ◽

Indoor Location ◽

Accurate Localization ◽

Vision Localization ◽

Automated Storage

Aiming at the problems of low localization accuracy and complicated localization methods of the automatic guided vehicle (AGV) in the current automatic storage and transportation process, a combined localization method based on the ultra-wideband (UWB) and the visual guidance is proposed. Both the UWB localization method and the monocular vision localization method are applied to the indoor location of the AGV. According to the corner points of an ArUco code fixed on the AGV body, the monocular vision localization method can solve the pose information of the AGV by the PnP algorithm in real-time. As an auxiliary localization method, the UWB localization method is called to locate the AGV coordinates. The distance from the tag on the AGV body to the surrounding anchors is measured by the time of flight (TOF) ranging algorithm, and the actual coordinates of the AGV are calculated by the trilateral centroid localization algorithm. Then, the localization data of the UWB is corrected by the mean compensation method to obtain a consistent and accurate localization trajectory. The experiment result shows that this localization system has an error of 15mm, which meets the needs of AGV location in the process of automated storage and transportation.

Download Full-text