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22 pages, 18439 KiB

Open AccessArticle

JamCatcher: A Mobile Jammer Localization Scheme for Advanced Metering Infrastructure in Smart Grid

by Taimin Zhang, Xiaoyu Ji, Zhou Zhuang and Wenyuan Xu

Sensors 2019, 19(4), 909; https://doi.org/10.3390/s19040909 - 21 Feb 2019

Cited by 10 | Viewed by 4121

Abstract

As the core component of the smart grid, advanced metering infrastructure (AMI) is responsible for automated billing, demand response, load forecasting, management, etc. The jamming attack poses a serious threat to the AMI communication networks, especially the neighborhood area network where wireless technologies [...] Read more.

As the core component of the smart grid, advanced metering infrastructure (AMI) is responsible for automated billing, demand response, load forecasting, management, etc. The jamming attack poses a serious threat to the AMI communication networks, especially the neighborhood area network where wireless technologies are widely adopted to connect a tremendous amount of smart meters. An attacker can easily build a jammer using a software-defined radio and jam the wireless communications between smart meters and local controllers, causing failures of on-line monitoring and state estimation. Accurate jammer localization is the first step for defending AMIs against jamming attacks. In this paper, we propose JamCatcher, a mobile jammer localization scheme for defending the AMI. Unlike existing jammer localization schemes, which only consider stationary jammers and usually require a high density of anchor nodes, the proposed scheme utilizes a tracker and can localize a mobile jammer with sparse anchor nodes. The time delay of data transmission is also considered, and the jammer localization process is divided into two stages, i.e., far-field chasing stage and near-field capturing stage. Different localization algorithms are developed for each stage. The proposed method has been tested with data from both simulation and real-world experiment. The results demonstrate that JamCatcher outperforms existing jammer localization algorithms with a limited number of anchor nodes in the AMI scenario. Full article

(This article belongs to the Special Issue Applications of Wireless Sensors in Localization and Tracking)

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21 pages, 3511 KiB

Open AccessArticle

Parameter Estimation Based on Sigmoid Transform in Wideband Bistatic MIMO Radar System under Impulsive Noise Environment

by Li Li, Nicolas H. Younan and Xiaofei Shi

Sensors 2019, 19(2), 232; https://doi.org/10.3390/s19020232 - 9 Jan 2019

Cited by 7 | Viewed by 3401

Abstract

Since second-order statistics-based methods rely heavily on Gaussianity assumption and fractional lower-order statistics-based methods depend on a priori knowledge of non-Gaussian noise, there remains a void in wideband bistatic multiple-input/multiple-output (MIMO) radar systems under impulsive noise. In this paper, a novel method based [...] Read more.

Since second-order statistics-based methods rely heavily on Gaussianity assumption and fractional lower-order statistics-based methods depend on a priori knowledge of non-Gaussian noise, there remains a void in wideband bistatic multiple-input/multiple-output (MIMO) radar systems under impulsive noise. In this paper, a novel method based on Sigmoid transform was used to estimate target parameters, which do not need a priori knowledge of the noise in an impulsive noise environment. Firstly, a novel wideband ambiguity function, termed Sigmoid wideband ambiguity function (Sigmoid-WBAF), is proposed to estimate the Doppler stretch and time delay by searching the peak of the Sigmoid-WBAF. A novel Sigmoid correlation function is proposed. Furthermore, a new MUSIC algorithm based on the Sigmoid correlation function (Sigmoid-MUSIC) is proposed to estimate the direction-of-departure (DOD) and direction-of-arrival (DOA). Then, the boundness of the Sigmoid-WBAF to the symmetric alpha stable (

S α S

) noise, the feasibility analysis of the Sigmoid-WBAF, and complexity analysis of the Sigmoid-WBAF and Sigmoid-MUSIC are presented to evaluate the performance of the proposed method. In addition, the Cramér–Rao bound for parameter estimation was derived and computed in closed form, which shows that better performance was achieved. Simulation results and theoretical analyses are presented to verify the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Applications of Wireless Sensors in Localization and Tracking)

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21 pages, 3628 KiB

Open AccessArticle

A Novel Positioning System Based on Coverage Area Pruning in Wireless Sensor Networks

by Shih-Chang Huang and Fu-Gong Li

Sensors 2018, 18(12), 4469; https://doi.org/10.3390/s18124469 - 17 Dec 2018

Cited by 4 | Viewed by 2793

Abstract

Wireless sensor networks are commonly applied in environmental monitoring applications. The crucial factor in such applications is to accurately retrieve the location of a monitoring event. Although many technologies have been proposed for target positioning, the devices used in such methods require better [...] Read more.

Wireless sensor networks are commonly applied in environmental monitoring applications. The crucial factor in such applications is to accurately retrieve the location of a monitoring event. Although many technologies have been proposed for target positioning, the devices used in such methods require better computational abilities or special hardware that is unsuitable for sensor networks with limited ability. Therefore, a range-free positioning algorithm, named coverage area pruning positioning system (CAPPS), is proposed in this study. First, the proposed CAPPS approach determines the area that includes the target approximately by using sensor nodes that can detect the target. Next, CAPPS uses sensor nodes that cannot detect the target to prune the area to improve positioning accuracy. The radio coverage variation is evaluated in a practical scenario, and a heuristic mechanism is proposed to reduce false positioning probability. Simulation results show that the size of the positioning area computed by CAPPS is smaller than that computed using distance vector hop, angle of arrival, and received signal strength indicator by approximately 98%, 97%, and 93%, respectively. In the radio variation scenario, the probability of determining an area excluding the target can be reduced from 50%–95% to 10%–30% by applying the proposed centroid point mechanism. Full article

(This article belongs to the Special Issue Applications of Wireless Sensors in Localization and Tracking)

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20 pages, 4561 KiB

Open AccessArticle

Research on RSS Data Optimization and DFL Localization for Non-Empty Environments

by Wenyu Mao, Rongxuan Shen, Ke Wang, Guoliang Gong, Yi Xiao and Huaxiang Lu

Sensors 2018, 18(12), 4419; https://doi.org/10.3390/s18124419 - 13 Dec 2018

Cited by 2 | Viewed by 2706

Abstract

Device-free localization (DFL) is a new technique which can estimate the target location through analyzing the shadowing effect on surrounding radio frequency (RF) links. In a relatively complex environment, the influences of random disturbance and the multipath effect are more serious. There are [...] Read more.

Device-free localization (DFL) is a new technique which can estimate the target location through analyzing the shadowing effect on surrounding radio frequency (RF) links. In a relatively complex environment, the influences of random disturbance and the multipath effect are more serious. There are kinds of noises and disturbances in the received signal strength (RSS) data of RF links and the data itself can even be distorted, which will seriously affect the DFL accuracy. Most of the common filtering methods adopted in DFL field are not targeted and the filtering effects are unstable. This paper researches the characteristics of RSS data with random disturbances and proposes two-dimensional double correlation (TDDC) distributed wavelet filtering. It can filter out the random disturbances and noise while preserving the RSS fluctuations which are helpful for the DFL, thus improving the quality of RSS data and localization accuracy. Furthermore, RSS variation rules for the links are different in complex environments and hence, it is difficult for the collected training samples to cover all possible patterns. Therefore, a single machine learning model with poor generalization ability finds it difficult to achieve ideal localization results. In this paper, the Adaboost.M2 ensemble learning model based on the Gini decision tree (GDTE) is proposed to improve the generalization ability for unknown patterns. Extensive experiments performed in two different drawing rooms demonstrate that the TDDC distributed wavelet filtering and the GDTE localization model have obvious advantages compared with other methods. The localization accuracy rates of 87% and 95% can be achieved respectively in the two environments. Full article

(This article belongs to the Special Issue Applications of Wireless Sensors in Localization and Tracking)

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28 pages, 11776 KiB

Open AccessArticle

VINS-MKF: A Tightly-Coupled Multi-Keyframe Visual-Inertial Odometry for Accurate and Robust State Estimation

by Chaofan Zhang, Yong Liu, Fan Wang, Yingwei Xia and Wen Zhang

Sensors 2018, 18(11), 4036; https://doi.org/10.3390/s18114036 - 19 Nov 2018

Cited by 10 | Viewed by 4989

Abstract

State estimation is crucial for robot autonomy, visual odometry (VO) has received significant attention in the robotics field because it can provide accurate state estimation. However, the accuracy and robustness of most existing VO methods are degraded in complex conditions, due to the [...] Read more.

State estimation is crucial for robot autonomy, visual odometry (VO) has received significant attention in the robotics field because it can provide accurate state estimation. However, the accuracy and robustness of most existing VO methods are degraded in complex conditions, due to the limited field of view (FOV) of the utilized camera. In this paper, we present a novel tightly-coupled multi-keyframe visual-inertial odometry (called VINS-MKF), which can provide an accurate and robust state estimation for robots in an indoor environment. We first modify the monocular ORBSLAM (Oriented FAST and Rotated BRIEF Simultaneous Localization and Mapping) to multiple fisheye cameras alongside an inertial measurement unit (IMU) to provide large FOV visual-inertial information. Then, a novel VO framework is proposed to ensure the efficiency of state estimation, by adopting a GPU (Graphics Processing Unit) based feature extraction method and parallelizing the feature extraction thread that is separated from the tracking thread with the mapping thread. Finally, a nonlinear optimization method is formulated for accurate state estimation, which is characterized as being multi-keyframe, tightly-coupled and visual-inertial. In addition, accurate initialization and a novel MultiCol-IMU camera model are coupled to further improve the performance of VINS-MKF. To the best of our knowledge, it’s the first tightly-coupled multi-keyframe visual-inertial odometry that joins measurements from multiple fisheye cameras and IMU. The performance of the VINS-MKF was validated by extensive experiments using home-made datasets, and it showed improved accuracy and robustness over the state-of-art VINS-Mono. Full article

(This article belongs to the Special Issue Applications of Wireless Sensors in Localization and Tracking)

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14 pages, 2023 KiB

Open AccessArticle

Indoor Smartphone Localization Based on LOS and NLOS Identification

by Hyeon Jeong Jo and Seungku Kim

Sensors 2018, 18(11), 3987; https://doi.org/10.3390/s18113987 - 16 Nov 2018

Cited by 28 | Viewed by 4055

Abstract

Accurate localization technology is essential for providing location-based services. Global positioning system (GPS) is a typical localization technology that has been used in various fields. However, various indoor localization techniques are required because GPS signals cannot be received in indoor environments. Typical indoor [...] Read more.

Accurate localization technology is essential for providing location-based services. Global positioning system (GPS) is a typical localization technology that has been used in various fields. However, various indoor localization techniques are required because GPS signals cannot be received in indoor environments. Typical indoor localization methods use the time of arrival, angle of arrival, or the strength of the wireless communication signal to determine the location. In this paper, we propose an indoor localization scheme using signal strength that can be easily implemented in a smartphone. The proposed algorithm uses a trilateration method to estimate the position of the smartphone. The accuracy of the trilateration method depends on the distance estimation error. We first determine whether the propagation path is line-of-sight (LOS) or non-line-of-sight (NLOS), and distance estimation is performed accordingly. This LOS and NLOS identification method decreases the distance estimation error. The proposed algorithm is implemented as a smartphone application. The experimental results show that distance estimation error is significantly reduced, resulting in accurate localization. Full article

(This article belongs to the Special Issue Applications of Wireless Sensors in Localization and Tracking)

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16 pages, 4417 KiB

Open AccessArticle

A Situation-Aware Indoor Localization (SAIL) System Using a LF and RF Hybrid Approach

by Jung Kwang Park, Jeeyoung Kim and Soon Ju Kang

Sensors 2018, 18(11), 3864; https://doi.org/10.3390/s18113864 - 10 Nov 2018

Cited by 11 | Viewed by 3564

Abstract

Recently, studies focusing on identifying user’s current location for use in a wide variety of differentiated location-based and localization services have steadily increased. In particular, location awareness using wireless communication is gaining attention in indoor environments composed of many obstacles, where GPS signals [...] Read more.

Recently, studies focusing on identifying user’s current location for use in a wide variety of differentiated location-based and localization services have steadily increased. In particular, location awareness using wireless communication is gaining attention in indoor environments composed of many obstacles, where GPS signals cannot reach. Previously, studies have focused mostly on location precision, which resulted in using many beacon nodes, not considering the initial installation and maintenance costs, communication robustness, or power consumption. This makes it difficult to apply existing methods to various fields, especially in mobile nodes (i.e., wearable devices, mobile tags, etc.) with limited battery capacity. In this paper, we propose a hybrid situation-aware indoor localization (SAIL) system for real-time indoor localization using a combination of low frequency (LF) and Bluetooth Low Energy (BLE) 4.0. This approach allows us to work with limited battery capacity mobile devices, and identify tagged mobile nodes and their current location in relevance to the anchor node. In our experiment, we attached one anchor node at the entrance to indoor areas such as office or factory settings. Using our hybrid SAIL system, we were able to detect the passing of a mobile node through the entrance and recognize whether the node is entering or exiting the room by calculating the direction of movement as well as the distance from the entrance. This allowed us to distinguish the precise position in an indoor environment with the margin of error being 0.5 m. The signal attenuation due to obstacles is overcome by using LF communication in the 125-kHz band. This approach enables us to reduce the number of initially installed anchor nodes as well as the power consumption of the mobile node. We propose an indoor position recognition system, namely, the hybrid SAIL system, that can be applied to mobile nodes with limited battery capacity by reducing the system complexity and power consumption. Full article

(This article belongs to the Special Issue Applications of Wireless Sensors in Localization and Tracking)

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19 pages, 2852 KiB

Open AccessArticle

A Bias Compensation Method for Distributed Moving Source Localization Using TDOA and FDOA with Sensor Location Errors

by Zhixin Liu, Rui Wang and Yongjun Zhao

Sensors 2018, 18(11), 3747; https://doi.org/10.3390/s18113747 - 2 Nov 2018

Cited by 11 | Viewed by 3358

Abstract

Current bias compensation methods for distributed localization consider the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements noise, but ignore the negative influence by the sensor location uncertainties on source localization accuracy. Therefore, a new bias compensation method for [...] Read more.

Current bias compensation methods for distributed localization consider the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements noise, but ignore the negative influence by the sensor location uncertainties on source localization accuracy. Therefore, a new bias compensation method for distributed localization is proposed to improve the localization accuracy in this paper. This paper derives the theoretical bias of maximum likelihood estimation when the sensor location errors and positioning measurements noise both exist. Using the rough estimate result by MLE to subtract the theoretical bias can obtain a more accurate source location estimation. Theoretical analysis and simulation results indicate that the theoretical bias derived in this paper matches well with the actual bias in moderate noise level so that it can prove the correctness of the theoretical derivation. Furthermore, after bias compensation, the estimate accuracy of the proposed method achieves a certain improvement compared with existing methods. Full article

(This article belongs to the Special Issue Applications of Wireless Sensors in Localization and Tracking)

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27 pages, 6828 KiB

Open AccessArticle

GROF: Indoor Localization Using a Multiple-Bandwidth General Regression Neural Network and Outlier Filter

by Zhang Chen and Jinlong Wang

Sensors 2018, 18(11), 3723; https://doi.org/10.3390/s18113723 - 1 Nov 2018

Cited by 13 | Viewed by 3892

Abstract

In recent years, a variety of methods have been developed for indoor localization utilizing fingerprints of received signal strength (RSS) that are location dependent. Nevertheless, the RSS is sensitive to environmental variations, in that the resulting fluctuation severely degrades the localization accuracy. Furthermore, [...] Read more.

In recent years, a variety of methods have been developed for indoor localization utilizing fingerprints of received signal strength (RSS) that are location dependent. Nevertheless, the RSS is sensitive to environmental variations, in that the resulting fluctuation severely degrades the localization accuracy. Furthermore, the fingerprints survey course is time-consuming and labor-intensive. Therefore, the lightweight fingerprint-based indoor positioning approach is preferred for practical applications. In this paper, a novel multiple-bandwidth generalized regression neural network (GRNN) with the outlier filter indoor positioning approach (GROF) is proposed. The GROF method is based on the GRNN, for which we adopt a new kind of multiple-bandwidth kernel architecture to achieve a more flexible regression performance than that of the traditional GRNN. In addition, an outlier filtering scheme adopting the k-nearest neighbor (KNN) method is embedded into the localization module so as to improve the localization robustness against environmental changes. We discuss the multiple-bandwidth spread value training process and the outlier filtering algorithm, and demonstrate the feasibility and performance of GROF through experiment data, using a Universal Software Radio Peripheral (USRP) platform. The experimental results indicate that the GROF method outperforms the positioning methods, based on the standard GRNN, KNN, or backpropagation neural network (BPNN), both in localization accuracy and robustness, without the extra training sample requirement. Full article

(This article belongs to the Special Issue Applications of Wireless Sensors in Localization and Tracking)

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18 pages, 1177 KiB

Open AccessArticle

Design of a Hybrid Indoor Location System Based on Multi-Sensor Fusion for Robot Navigation

by Yongliang Shi, Weimin Zhang, Zhuo Yao, Mingzhu Li, Zhenshuo Liang, Zhongzhong Cao, Hua Zhang and Qiang Huang

Sensors 2018, 18(10), 3581; https://doi.org/10.3390/s18103581 - 22 Oct 2018

Cited by 25 | Viewed by 6487

Abstract

In the case of a single scene feature, the positioning of an indoor service robot takes a long time, and localization errors are likely to occur. A new method for a hybrid indoor localization system according to multi-sensor fusion is proposed to solve [...] Read more.

In the case of a single scene feature, the positioning of an indoor service robot takes a long time, and localization errors are likely to occur. A new method for a hybrid indoor localization system according to multi-sensor fusion is proposed to solve these problems. The localization process is divided in two stages: rough positioning and precise positioning. By virtue of the K nearest neighbors based on possibility (KNNBP) algorithm first created in the present study, the rough position of a robot is determined according to the received signal strength indicator (RSSI) of Wi-Fi. Then, the hybrid particle filter localization (HPFL) algorithm improved on the basis of adaptive Monte Carlo localization (AMCL) is adopted to get the precise localization, which integrates various information, including the rough position and information from Lidar, a compass, an occupancy grid map, and encoders. The experiments indicated that the positioning error was 0.05 m; the success rate of localization was 96% with even 3000 particles, and the global positioning time was 1.9 s. However, under the same conditions, the success rate of AMCL was approximately 40%, the required time was approximately 25.6 s, and the positioning accuracy was the same. This indicates that the hybrid indoor location system is efficient and accurate. Full article

(This article belongs to the Special Issue Applications of Wireless Sensors in Localization and Tracking)

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16 pages, 891 KiB

Open AccessArticle

Exploiting Fine-Grained Subcarrier Information for Device-Free Localization in Wireless Sensor Networks

by Yan Guo, Dongping Yu and Ning Li

Sensors 2018, 18(9), 3110; https://doi.org/10.3390/s18093110 - 14 Sep 2018

Cited by 5 | Viewed by 3058

Abstract

Device-free localization (DFL) that aims to localize targets without carrying any electronic devices is addressed as an emerging and promising research topic. DFL techniques estimate the locations of transceiver-free targets by analyzing their shadowing effects on the radio signals that travel through the [...] Read more.

Device-free localization (DFL) that aims to localize targets without carrying any electronic devices is addressed as an emerging and promising research topic. DFL techniques estimate the locations of transceiver-free targets by analyzing their shadowing effects on the radio signals that travel through the area of interest. Recently, compressive sensing (CS) theory has been applied in DFL to reduce the number of measurements by exploiting the inherent spatial sparsity of target locations. In this paper, we propose a novel CS-based multi-target DFL method to leverage the frequency diversity of fine-grained subcarrier information. Specifically, we build the dictionaries of multiple channels based on the saddle surface model and formulate the multi-target DFL as a joint sparse recovery problem. To estimate the location vector, an iterative location vector estimation algorithm is developed under the multitask Bayesian compressive sensing (MBCS) framework. Compared with the state-of-the-art CS-based multi-target DFL approaches, simulation results validate the superiority of the proposed algorithm. Full article

(This article belongs to the Special Issue Applications of Wireless Sensors in Localization and Tracking)

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23 pages, 7886 KiB

Open AccessArticle

A Residual Analysis-Based Improved Particle Filter in Mobile Localization for Wireless Sensor Networks

by Long Cheng, Liang Feng and Yan Wang

Sensors 2018, 18(9), 2945; https://doi.org/10.3390/s18092945 - 4 Sep 2018

Cited by 5 | Viewed by 3520

Abstract

Wireless sensor networks (WSNs) have become a popular research subject in recent years. With the data collected by sensors, the information of a monitored area can be easily obtained. As a main contribution of WSN localization is widely applied in many fields. However, [...] Read more.

Wireless sensor networks (WSNs) have become a popular research subject in recent years. With the data collected by sensors, the information of a monitored area can be easily obtained. As a main contribution of WSN localization is widely applied in many fields. However, when the propagation of signals is obstructed there will be some severe errors which are called Non-Line-of-Sight (NLOS) errors. To overcome this difficulty, we present a residual analysis-based improved particle filter (RAPF) algorithm. Because the particle filter (PF) is a powerful localization algorithm, the proposed algorithm adopts PF as its main body. The idea of residual analysis is also used in the proposed algorithm for its reliability. To test the performance of the proposed algorithm, a simulation is conducted under several conditions. The simulation results show the superiority of the proposed algorithm compared with the Kalman Filter (KF) and PF. In addition, an experiment is designed to verify the effectiveness of the proposed algorithm in an indoors environment. The localization result of the experiment also confirms the fact that the proposed algorithm can achieve a lower localization error compared with KF and PF. Full article

(This article belongs to the Special Issue Applications of Wireless Sensors in Localization and Tracking)

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24 pages, 1104 KiB

Open AccessArticle

High-Dimensional Probabilistic Fingerprinting in Wireless Sensor Networks Based on a Multivariate Gaussian Mixture Model

by Yan Li, Simon Williams, Bill Moran, Allison Kealy and Guenther Retscher

Sensors 2018, 18(8), 2602; https://doi.org/10.3390/s18082602 - 8 Aug 2018

Cited by 19 | Viewed by 4253

Abstract

The extensive deployment of wireless infrastructure provides a low-cost way to track mobile users in indoor environment. This paper demonstrates a prototype model of an accurate and reliable room location awareness system in a real public environment in which three typical problems arise. [...] Read more.

The extensive deployment of wireless infrastructure provides a low-cost way to track mobile users in indoor environment. This paper demonstrates a prototype model of an accurate and reliable room location awareness system in a real public environment in which three typical problems arise. Firstly, a massive number of access points (APs) can be sensed leading to a high-dimensional classification problem. Secondly, heterogeneous devices record different received signal strength (RSS) levels because of the variations in chip-set and antenna attenuation. Thirdly, APs are not necessarily visible in every scanning cycle leading to missing data issue. This paper presents a probabilistic Wi-Fi fingerprinting method in a hidden Markov model (HMM) framework for mobile user tracking. To account for spatial correlation of the signal strengths from multiple APs, a Multivariate Gaussian Mixture Model (MVGMM) was fitted to model the probability distribution of RSS measurements in each cell. Furthermore, the unseen property of invisible AP was investigated in this research, and demonstrated the efficiency as a beneficial information to differentiate between cells. The proposed system is able to achieve comparable localisation performance. Filed test results achieve a reliable 97% localisation room level accuracy of multiple mobile users in a real university campus Wi-Fi network. Full article

(This article belongs to the Special Issue Applications of Wireless Sensors in Localization and Tracking)

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17 pages, 5311 KiB

Open AccessArticle

An Outlier Detection Method Based on Mahalanobis Distance for Source Localization

by Qingli Yan, Jianfeng Chen and Lieven De Strycker

Sensors 2018, 18(7), 2186; https://doi.org/10.3390/s18072186 - 7 Jul 2018

Cited by 16 | Viewed by 5076

Abstract

This paper addresses the problem of localization accuracy degradation caused by outliers of the angle of arrival (AOA). The problem of outlier detection of the AOA is converted into the detection of the estimated source position sets, which are obtained by the proposed [...] Read more.

This paper addresses the problem of localization accuracy degradation caused by outliers of the angle of arrival (AOA). The problem of outlier detection of the AOA is converted into the detection of the estimated source position sets, which are obtained by the proposed division and greedy replacement method. The Mahalanobis distance based on robust mean and covariance matrix estimation method is then introduced to identify the outliers from the position sets. Finally, the weighted least squares method based on the reliable probabilities and distances is proposed for source localization. The simulation and experimental results show that the proposed method outperforms representative methods when unreliable AOAs are present. Full article

(This article belongs to the Special Issue Applications of Wireless Sensors in Localization and Tracking)

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18 pages, 1201 KiB

Open AccessArticle

GSOS-ELM: An RFID-Based Indoor Localization System Using GSO Method and Semi-Supervised Online Sequential ELM

by Fagui Liu and Dexiang Zhong

Sensors 2018, 18(7), 1995; https://doi.org/10.3390/s18071995 - 21 Jun 2018

Cited by 16 | Viewed by 3430

Abstract

With the rapid development of indoor positioning technology, radio frequency identification (RFID) technology has become the preferred solution due to its advantages of non-line-of-sight, non-contact and rapid identification. However, the accuracy of existing RFID indoor positioning algorithms is easily affected by the tag [...] Read more.

With the rapid development of indoor positioning technology, radio frequency identification (RFID) technology has become the preferred solution due to its advantages of non-line-of-sight, non-contact and rapid identification. However, the accuracy of existing RFID indoor positioning algorithms is easily affected by the tag density and algorithm efficiency, and their environmental robustness is not strong enough. In this paper, we have introduced an RFID positioning algorithm based on the Glowworm Swarm Optimization (GSO) fused with semi-supervised online sequential extreme learning machine (SOS-ELM), which is called the GSOS-ELM algorithm. The GSOS-ELM algorithm automatically adjusts the regularization weights of the SOS-ELM algorithm through the GSO algorithm, so that it can quickly obtain the optimal regularization weights under different initial conditions; at the same time, the semi-supervised characteristics of the GSOS-ELM algorithm can significantly reduce the number of labeled reference tags and reduce the cost of positioning systems. In addition, the online learning phase of the GSOS-ELM algorithm can continuously update the system to perceive changes in the environment and resist the environmental interference. We have carried out experiments to study the influence factors and validate the performance, both the simulation and testbed experiment results show that compared with other algorithms, our proposed GSOS-ELM localization system can achieve more accurate positioning results and has certain adaptability to the changes of the environment. Full article

(This article belongs to the Special Issue Applications of Wireless Sensors in Localization and Tracking)

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14 pages, 799 KiB

Open AccessArticle

A Hybrid DV-Hop Algorithm Using RSSI for Localization in Large-Scale Wireless Sensor Networks

by Omar Cheikhrouhou, Ghulam M. Bhatti and Roobaea Alroobaea

Sensors 2018, 18(5), 1469; https://doi.org/10.3390/s18051469 - 8 May 2018

Cited by 87 | Viewed by 5527

Abstract

With the increasing realization of the Internet-of-Things (IoT) and rapid proliferation of wireless sensor networks (WSN), estimating the location of wireless sensor nodes is emerging as an important issue. Traditional ranging based localization algorithms use triangulation for estimating the physical location of only [...] Read more.

With the increasing realization of the Internet-of-Things (IoT) and rapid proliferation of wireless sensor networks (WSN), estimating the location of wireless sensor nodes is emerging as an important issue. Traditional ranging based localization algorithms use triangulation for estimating the physical location of only those wireless nodes that are within one-hop distance from the anchor nodes. Multi-hop localization algorithms, on the other hand, aim at localizing the wireless nodes that can physically be residing at multiple hops away from anchor nodes. These latter algorithms have attracted a growing interest from research community due to the smaller number of required anchor nodes. One such algorithm, known as DV-Hop (Distance Vector Hop), has gained popularity due to its simplicity and lower cost. However, DV-Hop suffers from reduced accuracy due to the fact that it exploits only the network topology (i.e., number of hops to anchors) rather than the distances between pairs of nodes. In this paper, we propose an enhanced DV-Hop localization algorithm that also uses the RSSI values associated with links between one-hop neighbors. Moreover, we exploit already localized nodes by promoting them to become additional anchor nodes. Our simulations have shown that the proposed algorithm significantly outperforms the original DV-Hop localization algorithm and two of its recently published variants, namely RSSI Auxiliary Ranging and the Selective 3-Anchor DV-hop algorithm. More precisely, in some scenarios, the proposed algorithm improves the localization accuracy by almost 95%, 90% and 70% as compared to the basic DV-Hop, Selective 3-Anchor, and RSSI DV-Hop algorithms, respectively. Full article

(This article belongs to the Special Issue Applications of Wireless Sensors in Localization and Tracking)

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12 pages, 1802 KiB

Open AccessArticle

Mixed Incoherent Far-Field and Near-Field Source Localization under Uniform Circular Array

by Xiaolong Su, Zhen Liu, Xin Chen and Xiang Li

Sensors 2018, 18(5), 1432; https://doi.org/10.3390/s18051432 - 4 May 2018

Cited by 9 | Viewed by 3726

Abstract

A high-accuracy algorithm is presented for the localization of mixed incoherent near-field and far-field narrow-band sources under uniform circular array (UCA). Herein, considering that it is difficult to classify the mixed sources, we first decouple mixed sources’ angles and ranges by calculating centro-symmetric [...] Read more.

A high-accuracy algorithm is presented for the localization of mixed incoherent near-field and far-field narrow-band sources under uniform circular array (UCA). Herein, considering that it is difficult to classify the mixed sources, we first decouple mixed sources’ angles and ranges by calculating centro-symmetric sensors’ phase differences. Then, as the phase differences including only sources’ angles can be transformed as indefinite equations, each source’s azimuth angle and elevation angle are obtained by performing the least squares method. After that, on the basis of the estimated angles of the mixed sources, one-dimensional (1-D) multiple signal classification (MUSIC) method and corresponding spatial spectrum are utilized to identify the mixed sources and estimate the ranges of the near-field sources. Finally, simulation and comparison results verify the superior performance of our proposed algorithm. Full article

(This article belongs to the Special Issue Applications of Wireless Sensors in Localization and Tracking)

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16 pages, 7237 KiB

Open AccessArticle

Two New Shrinking-Circle Methods for Source Localization Based on TDoA Measurements

by Mingzhi Luo, Xiang Chen, Shuai Cao and Xu Zhang

Sensors 2018, 18(4), 1274; https://doi.org/10.3390/s18041274 - 20 Apr 2018

Cited by 12 | Viewed by 4819

Abstract

Time difference of arrival (TDoA) measurement is a promising approach for target localization based on a set of nodes with known positions, with high accuracy and low complexity. Common localization algorithms include the maximum-likelihood, non-linear least-squares and weighted least-squares methods. These methods have [...] Read more.

Time difference of arrival (TDoA) measurement is a promising approach for target localization based on a set of nodes with known positions, with high accuracy and low complexity. Common localization algorithms include the maximum-likelihood, non-linear least-squares and weighted least-squares methods. These methods have shortcomings such as high computational complexity, requiring an initial guess position, or having difficulty in finding the optimal solution. From the point of view of geometrical analysis, this study proposes two new shrinking-circle methods (SC-1 and SC-2) to solve the TDoA-based localization problem in a two-dimensional (2-D) space. In both methods, an optimal radius is obtained by shrinking the radius with a dichotomy algorithm, and the position of the target is determined by the optimal radius. The difference of the two methods is that a distance parameter is defined in SC-1, while an error function is introduced in SC-2 to guide the localization procedure. Simulations and indoor-localization experiments based on acoustic transducers were conducted to compare the performance differences between the proposed methods, algorithms based on weighted least-squares as well as the conventional shrinking-circle method. The experimental results demonstrate that the proposed methods can realize high-precision target localization based on TDoA measurements using three nodes, and have the advantages of speed and high robustness. Full article

(This article belongs to the Special Issue Applications of Wireless Sensors in Localization and Tracking)

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Target Localization via Integrated and Segregated Ranging Based on RSS and TOA Measurements

by Slavisa Tomic and Marko Beko

Sensors 2019, 19(2), 230; https://doi.org/10.3390/s19020230 - 9 Jan 2019

Cited by 19 | Viewed by 3189

Abstract

This work addresses the problem of target localization in adverse non-line-of-sight (NLOS) environments by using received signal strength (RSS) and time of arrival (TOA) measurements. It is inspired by a recently published work in which authors discuss about a critical distance below and [...] Read more.

This work addresses the problem of target localization in adverse non-line-of-sight (NLOS) environments by using received signal strength (RSS) and time of arrival (TOA) measurements. It is inspired by a recently published work in which authors discuss about a critical distance below and above which employing combined RSS-TOA measurements is inferior to employing RSS-only and TOA-only measurements, respectively. Here, we revise state-of-the-art estimators for the considered target localization problem and study their performance against their counterparts that employ each individual measurement exclusively. It is shown that the hybrid approach is not the best one by default. Thus, we propose a simple heuristic approach to choose the best measurement for each link, and we show that it can enhance the performance of an estimator. The new approach implicitly relies on the concept of the critical distance, but does not assume certain link parameters as given. Our simulations corroborate with findings available in the literature for line-of-sight (LOS) to a certain extent, but they indicate that more work is required for NLOS environments. Moreover, they show that the heuristic approach works well, matching or even improving the performance of the best fixed choice in all considered scenarios. Full article

(This article belongs to the Special Issue Applications of Wireless Sensors in Localization and Tracking)

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