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.

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 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 Wireless Sensor Network Target Localization Algorithm Based on Two- and Three-Dimensional Delaunay Partitions

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Chenguang Shao
Keyword(s):  
Three Dimensional ◽  
Target Localization ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Target Node ◽  
Localization Accuracy ◽  
Localization Method ◽  
Anchor Nodes ◽  
2D And 3D ◽  
The Relationship

The target localization algorithm is critical in the field of wireless sensor networks (WSNs) and is widely used in many applications. In the conventional localization method, the location distribution of the anchor nodes is fixed and cannot be adjusted dynamically according to the deployment environment. The resulting localization accuracy is not high, and the localization algorithm is not applicable to three-dimensional (3D) conditions. Therefore, a Delaunay-triangulation-based WSN localization method, which can be adapted to two-dimensional (2D) and 3D conditions, was proposed. Based on the location of the target node, we searched for the triangle or tetrahedron surrounding the target node and designed the localization algorithm in stages to accurately calculate the coordinate value of the target. The relationship between the number of target nodes and the number of generated graphs was analysed through numerous experiments, and the proposed 2D localization algorithm was verified by extending it the 3D coordinate system. Experimental results revealed that the proposed algorithm can effectively improve the flexibility of the anchor node layout and target localization accuracy.

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 Large-Scale MIMO-Based RSSI Localization with Low-Complexity RFID Infrastructure

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3933
Author(s):  
Mohammed El-Absi ◽  
Feng Zheng ◽  
Ashraf Abuelhaija ◽  
Ali Al-haj Abbas ◽  
Klaus Solbach ◽  
...  
Keyword(s):  
Radio Frequency Identification ◽  
Large Scale ◽  
Indoor Localization ◽  
Channel Modeling ◽  
Low Complexity ◽  
Small Scale ◽  
Indoor Environments ◽  
Wave Band ◽  
Localization Algorithm ◽  
Localization Accuracy

Indoor localization based on unsynchronized, low-complexity, passive radio frequency identification (RFID) using the received signal strength indicator (RSSI) has a wide potential for a variety of internet of things (IoTs) applications due to their energy-harvesting capabilities and low complexity. However, conventional RSSI-based algorithms present inaccurate ranging, especially in indoor environments, mainly because of the multipath randomness effect. In this work, we propose RSSI-based localization with low-complexity, passive RFID infrastructure utilizing the potential benefits of large-scale MIMO technology operated in the millimeter-wave band, which offers channel hardening, in order to alleviate the effect of small-scale fading. Particularly, by investigating an indoor environment equipped with extremely simple dielectric resonator (DR) tags, we propose an efficient localization algorithm that enables a smart object equipped with large-scale MIMO exploiting the RSSI measurements obtained from the reference DR tags in order to improve the localization accuracy. In this context, we also derive Cramer–Rao lower bound of the proposed technique. Numerical results evidence the effectiveness of the proposed algorithms considering various arbitrary network topologies, and results are compared with an existing algorithm, where the proposed algorithms not only produce higher localization accuracy but also achieve a greater robustness against inaccuracies in channel modeling.

scholarly journals Enhance RSS-Based Indoor Localization Accuracy by Leveraging Environmental Physical Features

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Peng Xiang ◽  
Peng Ji ◽  
Dian Zhang
Keyword(s):  
Indoor Localization ◽  
Radio Signal ◽  
Signal Strength ◽  
Indoor Environments ◽  
Wireless Devices ◽  
Positioning Accuracy ◽  
Localization Accuracy ◽  
Localization Method ◽  
Physical Features ◽  
Additional Hardware

Indoor localization technologies based on Radio Signal Strength (RSS) attract many researchers’ attentions, since RSS can be easily obtained by wireless devices without additional hardware. However, such technologies are apt to be affected by indoor environments and multipath phenomenon. Thus, the accuracy is very difficult to improve. In this paper, we put forward a method, which is able to leverage various other resources in localization. Besides the traditional RSS information, the environmental physical features, e.g., the light, temperature, and humidity information, are all utilized for localization. After building a comprehensive fingerprint map for the above information, we propose an algorithm to localize the target based on Naïve Bayesian. Experimental results show that the successful positioning accuracy can dramatically outperform traditional pure RSS-based indoor localization method by about 39%. Our method has the potential to improve all the radio frequency (RF) based localization approaches.

Effect of CSI Imperfection on Cognitive Underlay Transmission over Nakagami-m Fading Channel

2015 ◽  
Vol 1 ◽  
pp. 59
Author(s):  
Nguyen Hong Giang ◽  
Vo Nguyen Quoc Bao ◽  
Hung Nguyen-Le
Keyword(s):  
Fading Channels ◽  
System Performance ◽  
Performance Metrics ◽  
Maximal Ratio Combining ◽  
Relay Nodes ◽  
Channel State ◽  
Primary Network ◽  
Secondary Network ◽  
System Designs ◽  
Approximate Expressions

This paper analyzes the performance of a cognitive underlay system over Nakagami-m fading channels, where maximal ratio combining (MRC) is employed at secondary destination and relay nodes. Under the condition of imperfect channel state information (CSI) of interfering channels, system performance metrics for the primary network and for the secondary network are formulated into exact and approximate expressions, which can be served as theoretical guidelines for system designs. To verify the performance analysis, several analytical and simulated results of the system performance are provided under various system and channel settings.

scholarly journals Dynamic Indoor Localization Using Maximum Likelihood Particle Filtering

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1090
Author(s):  
Wenxu Wang ◽  
Damián Marelli ◽  
Minyue Fu
Keyword(s):  
Maximum Likelihood ◽  
Particle Filtering ◽  
Indoor Localization ◽  
Real Data ◽  
High Accuracy ◽  
Classical Particle ◽  
Popular Approach ◽  
Filtering Method ◽  
Essential Idea ◽  
Number Of Particles

A popular approach for solving the indoor dynamic localization problem based on WiFi measurements consists of using particle filtering. However, a drawback of this approach is that a very large number of particles are needed to achieve accurate results in real environments. The reason for this drawback is that, in this particular application, classical particle filtering wastes many unnecessary particles. To remedy this, we propose a novel particle filtering method which we call maximum likelihood particle filter (MLPF). The essential idea consists of combining the particle prediction and update steps into a single one in which all particles are efficiently used. This drastically reduces the number of particles, leading to numerically feasible algorithms with high accuracy. We provide experimental results, using real data, confirming our claim.

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