scholarly journals A Two-Level WiFi Fingerprint-Based Indoor Localization Method for Dangerous Area Monitoring

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4243 ◽  
Author(s):  
Fei Li ◽  
Min Liu ◽  
Yue Zhang ◽  
Weiming Shen
Keyword(s):  
Indoor Localization ◽  
Search Space ◽  
High Accuracy ◽  
Cluster Center ◽  
Support Vector ◽  
Localization Accuracy ◽  
Localization Method ◽  
Fingerprint Database ◽  
Positioning Algorithm ◽  
Area Monitoring

Localization technologies play an important role in disaster management and emergence response. In areas where the environment does not change much after an accident or in the case of dangerous areas monitoring, indoor fingerprint-based localization can be used. In such scenarios, a positioning system needs to have both a high accuracy and a rapid response. However, these two requirements are usually conflicting since a fingerprint-based indoor localization system with high accuracy usually has complex algorithms and needs to process a large amount of data, and therefore has a slow response. This problem becomes even worse when both the size of monitoring area and the number of reference nodes increase. To address this challenging problem, this paper proposes a two-level positioning algorithm in order to improve both the accuracy and the response time. In the off-line stage, a fingerprint database is divided into several sub databases by using an affinity propagation clustering (APC) algorithm based on Shepard similarity. The online stage has two steps: (1) a coarse positioning algorithm is adopted to find the most similar sub database by matching the cluster center with the fingerprint of the node tested, which will narrow the search space and consequently save time; (2) in the sub database area, a support vector regression (SVR) algorithm with its parameters being optimized by particle swarm optimization (PSO) is used for fine positioning, thus improving the online positioning accuracy. Both experiment results and actual implementations proved that the proposed two-level localization method is more suitable than other methods in term of algorithm complexity, storage requirements and localization accuracy in dangerous area monitoring.

scholarly journals A Low Complexity Asynchronous UWB TDOA Localization Method

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Liyuan Song ◽  
Hongliang Zou ◽  
Tingting Zhang
Keyword(s):  
Performance Metrics ◽  
Indoor Localization ◽  
Low Complexity ◽  
High Accuracy ◽  
Localization Accuracy ◽  
Localization Method ◽  
Covered Area ◽  
Optimal Deployment ◽  
The Relationship ◽  
System Designs

Impulse-radio ultrawideband (IR-UWB) is a promising technique for indoor localization due to its high accuracy and robustness against multipath interferences. In this paper, to deal with the synchronization challenges among anchors in traditional time-difference-of-arrival (TDOA) localization systems, we propose an asynchronous TDOA (ATDOA) localization method. Based on the ranging error model, we derive the theoretical lower bounds as the performance metrics of localization accuracy. Compared with the ideal TDOA method, ATDOA degrades on localization accuracy for eliminating the high accuracy synchronization requirements, which is pretty much attractive in energy and complexity limited scenarios. Based on the performance analysis, we show that there exists optimal anchor deployment in ATDOA that minimizes the localization errors. We also formulate the relationship between this optimal deployment and the size of the covered area, which is meaningful in both theoretical analysis and practical system designs.

Method of Optimization for Target Localization Model Parameters Based on LSSVR

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.

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

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 875 ◽  
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.

scholarly journals mPILOT-Magnetic Field Strength Based Pedestrian Indoor Localization

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2283 ◽  
Author(s):  
Imran Ashraf ◽  
Soojung Hur ◽  
Yongwan Park
Keyword(s):  
Magnetic Flux ◽  
Indoor Localization ◽  
Search Space ◽  
Initial Position ◽  
Received Signal Strength Indicator ◽  
Strength Based ◽  
Flux Intensity ◽  
Smartphone Sensors ◽  
Path Lengths ◽  
Fingerprint Database

An indoor localization system based on off-the-shelf smartphone sensors is presented which employs the magnetometer to find user location. Further assisted by the accelerometer and gyroscope, the proposed system is able to locate the user without any prior knowledge of user initial position. The system exploits the fingerprint database approach for localization. Traditional fingerprinting technology stores data intensity values in database such as RSSI (Received Signal Strength Indicator) values in the case of WiFi fingerprinting and magnetic flux intensity values in the case of geomagnetic fingerprinting. The down side is the need to update the database periodically and device heterogeneity. We solve this problem by using the fingerprint database of patterns formed by magnetic flux intensity values. The pattern matching approach solves the problem of device heterogeneity and the algorithm’s performance with Samsung Galaxy S8 and LG G6 is comparable. A deep learning based artificial neural network is adopted to identify the user state of walking and stationary and its accuracy is 95%. The localization is totally infrastructure independent and does not require any other technology to constraint the search space. The experiments are performed to determine the accuracy in three buildings of Yeungnam University, Republic of Korea with different path lengths and path geometry. The results demonstrate that the error is 2–3 m for 50 percentile with various buildings. Even though many locations in the same building exhibit very similar magnetic attitude, the algorithm achieves an accuracy of 4 m for 75 percentile irrespective of the device used for localization.

scholarly journals Endpoints-Clipping CSI Amplitude for SVM-Based Indoor Localization

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3689 ◽  
Author(s):  
Zhanjun Hao ◽  
Yan Yan ◽  
Xiaochao Dang ◽  
Chenguang Shao
Keyword(s):  
Indoor Localization ◽  
Spatial Clustering ◽  
Support Vector ◽  
Localization Algorithm ◽  
Channel State ◽  
Positioning Accuracy ◽  
State Information ◽  
Inaccurate Data ◽  
Svm Algorithm ◽  
Fingerprint Database

With the wide application of Channel State Information (CSI) in the field of sensing, the accuracy of positioning accuracy of indoor fingerprint positioning is increasingly necessary. The flexibility of the CSI signals may lead to an increase in fingerprint noise and inaccurate data classification. This paper presents an indoor localization algorithm based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Endpoints-Clipping (EC) CSI amplitude, and Support Vector Machine (EC-SVM). In the offline phase, the CSI amplitude information collected through the three channels is combined and clipped using the EC, and then a fingerprint database is obtained. In the online phase, the SVM is used to train the data in the fingerprint database, and the corresponding relationship is found with the CSI data collected in real time to perform matching and positioning. The experimental results show that the positioning accuracy of the EC-SVM algorithm is superior to the state-of-art indoor CSI-based localization technique.

scholarly journals Mobile Drone Localization in Indoor Environment Based on Passive RFID

2020 ◽  
Vol 14 (05) ◽  
pp. 4
Author(s):  
Mohamed Hadi Habaebi ◽  
Rashid Khamis Omar ◽  
Md Rafiqul Islam
Keyword(s):  
Radio Frequency Identification ◽  
Information Exchange ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Localization Accuracy ◽  
Localization Method ◽  
Mobile Objects ◽  
Frequency Identification ◽  
Passive Rfid ◽  
Rfid Readers

<p class="AEEEAbstract">Radio Frequency Identification (RFID) is an information exchange technology based on RF communication. It provides solution to track and localize mobile objects in the indoor environment. Localization of mobile objects in an indoor environment garnered a significant attention due to the variety of applications needing higher degree of localization accuracy. RSS-based localization techniques are the major tools for tracking applications due to their simplicity. In this paper, a trilateration method for indoor localization is proposed. This method provides a solution for the drone tracking problem by collecting the RSS values between RFID tagged drone and reader, and estimate its location. The localization method is implemented in MATLAB by multiple readers; 4 RFID readers and 8 RFID readers. The performance of the localization method is also compared with other RFID localization previously reported in the literature. The simulation results in the case of 8 RFID readers demonstrate more accurate results than 4 RFID readers by minimizing the localization error from 0.84606 to 0.40079m. The results also indicate an improved localization performance of tracking a tagged drone in indoor environment by 42.8% when 8RFID readers are placed in the localization area.</p>

scholarly journals High-accuracy localization for indoor group users based on extended Kalman filter

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771881272 ◽  
Author(s):  
Tian Wang ◽  
Yuzhu Liang ◽  
Yaxin Mei ◽  
Muhammad Arif ◽  
Chunsheng Zhu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Indoor Localization ◽  
High Accuracy ◽  
Mutual Distance ◽  
Distance Information ◽  
Localization Accuracy ◽  
Accuracy Requirement ◽  
Time Operation ◽  
Real Time Operation

Indoor localization has attracted increasing research attentions in the recent years. However, many important issues still need to be further studied to keep pace with new requirements and technical progress, such as real-time operation, high accuracy, and energy efficiency. In order to meet the high localization accuracy requirement and the high localization dependable requirement in some scenarios, we take the users as a group to utilize the mutual distance information among them to get better localization performance. Moreover, we design a mobile group localization method based on extended kalman filter and believable factor of non-localized nodes, which can alleviate the influence caused by environmental noisy and unstable wireless signals to improve the localization accuracy. Besides, we implement a real system based on ZigBee technique and perform experiments on the campus of Huaqiao University. Experimental results and theoretical analysis validate the effectiveness of the proposed method.

scholarly journals Indoor Floor Localization Based on Multi-Intelligent Sensors

2020 ◽  
Vol 10 (1) ◽  
pp. 6
Author(s):  
Min Zhao ◽  
Danyang Qin ◽  
Ruolin Guo ◽  
Xinxin Wang
Keyword(s):  
Machine Learning ◽  
Indoor Localization ◽  
Collaborative Work ◽  
Magnetic Data ◽  
Data Mapping ◽  
Localization Accuracy ◽  
Database Construction ◽  
Intelligent Sensors ◽  
Majority Principle ◽  
Fingerprint Database

With the continuous expansion of the market of indoor localization, the requirements of indoor localization technology are becoming higher and higher. Existing indoor floor localization (IFL) systems based on Wi-Fi signal and barometer data are susceptible to external environment changes, resulting in large errors. A method for indoor floor localization using multiple intelligent sensors (MIS-IFL) is proposed to decrease the localization errors, which consists of a fingerprint database construction phase and a floor localization phase. In the fingerprint database construction phase, data acquisition is performed using magnetometer sensor, accelerator sensor and gyro sensor in the smartphone. In the floor localization phase, an active pattern recognition is performed through the collaborative work of multiple intelligent sensors and machine learning classifiers. Then floor localization is performed using magnetic data mapping, Euclidean closest approximation and majority principle. Finally, the inter-floor detection link based on machine learning is added to improve the overall localization accuracy of MIS-IFL. The experimental results show that the performance of the proposed method is superior to the existing IFL.

scholarly journals A Survey of Recent Indoor Localization Scenarios and Methodologies

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8086
Author(s):  
Tian Yang ◽  
Adnane Cabani ◽  
Houcine Chafouk
Keyword(s):  
Indoor Localization ◽  
Measurement Data ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Bayesian Filtering ◽  
K Nearest Neighbors ◽  
Localization Accuracy ◽  
Trip Time ◽  
Network Methods ◽  
Received Signal Strength Indication

Recently, various novel scenarios have been studied for indoor localization. The trilateration is known as a classic theoretical model of geometric-based indoor localization, with uniform RSSI data that can be transferred directly into distance ranges. Then, a trilateration solution can be algebraically acquired from theses ranges, in order to fix user’s actual location. However, the collected RSSI or other measurement data should be further processed and classified to lower the localization error rate, instead of using the raw data influenced by multi-path effect, multiple nonlinear interference and noises. In this survey, a large number of existing techniques are presented for different indoor network structures and channel conditions, divided as LOS (light-of-sight) and NLOS (non light-of-sight). Besides, the input measurement data such as RSSI (received signal strength indication), TDOA (time difference of arrival), DOA (distance of arrival), and RTT (round trip time) are studied towards different application scenarios. The key localization techniques like RSSI-based fingerprinting technique are presented using supervised machine learning methods, namely SVM (support vector machine), KNN (K nearest neighbors) and NN (neural network) methods, especially in an offline training phase. Other unsupervised methods as isolation forest, k-means, and expectation maximization methods are utilized to further improve the localization accuracy in online testing phase. For Bayesian filtering methods, apart from the basic linear Kalman filter (LKF) methods, nonlinear stochastic filters such as extended KF, cubature KF, unscented KF and particle filters are introduced. These nonlinear methods are more suitable for dynamic localization models. In addition to the localization accuracy, the other important performance features and evaluation aspects are presented in our paper: scalability, stability, reliability, and the complexity of proposed algorithms is compared in this survey. Our paper provides a comprehensive perspective to compare the existing techniques and related practical localization models, with the aim of improving localization accuracy and reducing the complexity of the system.

An Indoor Localization of WiFi Based on Support Vector Machines

2014 ◽  
Vol 926-930 ◽  
pp. 2438-2441 ◽  
Author(s):  
Feng Yu ◽  
Ming Hua Jiang ◽  
Jing Liang ◽  
Xiao Qin ◽  
Ming Hu ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Basis Function ◽  
Indoor Localization ◽  
Signal Strength ◽  
Kernel Functions ◽  
Support Vector ◽  
Localization Accuracy ◽  
Svm Algorithm ◽  
Vector Machines ◽  
Wifi Signal

The recent growing interest for indoor localization-based services has created a need for more accurate and real-time indoor localization solutions. Indoor localization based on existing WiFi signal strength is becoming increasingly prevalent and ubiquity. In this paper, we utilize the information of the signal strength received from the surrounding access points (APs) to determine the user localization. The propose algorithm based on support vector machines (SVM) algorithm, and comparing with three kernel functions, radial basis function (RBF) performs best of all. Experimental results indicate that the proposed algorithm leads to improvement on localization accuracy.

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