scholarly journals Indoor Positioning Algorithm Based on Maximum Correntropy Unscented Information Filter

2021 ◽  
Vol 10 (7) ◽  
pp. 441
Author(s):  
Li Ma ◽  
Ning Cao ◽  
Xiaoliang Feng ◽  
Minghe Mao
Keyword(s):  
Information Entropy ◽  
Gaussian Noise ◽  
Indoor Positioning ◽  
Indoor Environments ◽  
State Matrix ◽  
Positioning Systems ◽  
Information Filter ◽  
Non Gaussian ◽  
Positioning Algorithm ◽  
Information Vector

In view of the fact that indoor positioning systems are usually affected by non-Gaussian noise in complex indoor environments, this paper tests the data in the actual scene and analyzes the distribution characteristics of noise, and proposes a new indoor positioning algorithm based on maximum correntropy unscented information filter (MCUIF). The proposed indoor positioning algorithm includes three steps: First, the estimation of the state matrix and the corresponding covariance matrix are predicted through the unscented transformation (UT). Second, the observed information is reconstructed by using a nonlinear regression method on the basis of the maximum correntropy criterion (MCC). Third, the contribution of information vector is gained by non-Gaussian measurement and the predicted information vector is corrected by the contribution of information vector. Finally, the gain of information filtering is got by the information entropy state matrix and the information entropy measurement matrix to calculate the position coordinates of the unknown nodes. This algorithm enhances the robustness of the MCUIF to non-Gaussian noise in complex indoor environments. The results from the indoor positioning experiments show that MCUIF is better than the traditional methods in state estimation and position location of the unknown nodes.

scholarly journals Positioning Techniques in Indoor Environments Based on Stochastic Modeling of UWB Round Trip Time Measurements

2021 ◽  
Author(s):  
Paolo Carbone
Keyword(s):  
Particle Filter ◽  
Gaussian Noise ◽  
Additive White Gaussian Noise ◽  
Least Square ◽  
Indoor Environments ◽  
Round Trip Time ◽  
Round Trip ◽  
Positioning Systems ◽  
Trip Time ◽  
Non Gaussian

<div><div><div><p>In this paper, a technique for modeling propagation of Ultra Wide Band (UWB) signals in indoor or outdoor environments is proposed, supporting the design of a positioning systems based on Round Trip Time (RTT) measurements and on a particle filter. By assuming that nonlinear pulses are transmitted in an Additive White Gaussian Noise Channel, and detected using a threshold based receiver, it is shown that RTT measurements may be affected by a non-Gaussian noise. RTT noise properties are analyzed, and the effects of non-Gaussian noise on the performance of a RTT based positioning system are investigated. To this aim, a classical Least Square, an extended Kalman Filter and a Particle Filter are compared when used to detect a slowly moving target in presence of the modeled noise. It is shown that, in a realistic indoor environment, the Particle Filter solution may be a competitive solution, at a price of increased computational complexity. Experimental verifications validate the presented approach.</p></div></div></div>

scholarly journals Positioning Techniques in Indoor Environments Based on Stochastic Modeling of UWB Round Trip Time Measurements

2021 ◽  
Author(s):  
Paolo Carbone
Keyword(s):  
Particle Filter ◽  
Gaussian Noise ◽  
Additive White Gaussian Noise ◽  
Least Square ◽  
Indoor Environments ◽  
Round Trip Time ◽  
Round Trip ◽  
Positioning Systems ◽  
Trip Time ◽  
Non Gaussian

<div><div><div><p>In this paper, a technique for modeling propagation of Ultra Wide Band (UWB) signals in indoor or outdoor environments is proposed, supporting the design of a positioning systems based on Round Trip Time (RTT) measurements and on a particle filter. By assuming that nonlinear pulses are transmitted in an Additive White Gaussian Noise Channel, and detected using a threshold based receiver, it is shown that RTT measurements may be affected by a non-Gaussian noise. RTT noise properties are analyzed, and the effects of non-Gaussian noise on the performance of a RTT based positioning system are investigated. To this aim, a classical Least Square, an extended Kalman Filter and a Particle Filter are compared when used to detect a slowly moving target in presence of the modeled noise. It is shown that, in a realistic indoor environment, the Particle Filter solution may be a competitive solution, at a price of increased computational complexity. Experimental verifications validate the presented approach.</p></div></div></div>

Robustness in Fingerprinting-Based Indoor Positioning Systems

2018 ◽  
pp. 88-141 ◽  
Author(s):  
Shih-Hau Fang
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Signal Strength ◽  
Histogram Equalization ◽  
Indoor Positioning ◽  
Received Signal Strength ◽  
Location Estimation ◽  
Location Based Services ◽  
Indoor Environments ◽  
Positioning Systems

Indoor positioning systems have received increasing attention for supporting location-based services in indoor environments. Received signal strength (RSS), mostly utilized in Wi-Fi fingerprinting systems, is known to be unreliable due to two reasons: orientation mismatch and variations in hardware. This chapter introduces an approach based on histogram equalization to compensate for orientation mismatch in robust Wi-Fi localization. The proposed method involves converting the temporal-spatial radio signal strength into a reference function (i.e., equalizing the histogram). This chapter also introduces an enhanced positioning feature, which is called delta-fused principal strength, to enhance the robustness of Wi-Fi localization against the problem of heterogeneous hardware. This algorithm computes the pairwise delta RSS and then integrates with RSS using principal component analysis. The proposed methods effectively and efficiently improve the robustness of location estimation in the presence of mismatch orientation and hardware variations, respectively.

scholarly journals Indoor Positioning on Disparate Commercial Smartphones Using Wi-Fi Access Points Coverage Area

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4351 ◽  
Author(s):  
Ashraf ◽  
Hur ◽  
Park
Keyword(s):  
Human Mobility ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Indoor Environments ◽  
Coverage Area ◽  
Positioning Accuracy ◽  
Positioning Systems ◽  
Access Points ◽  
Intersection Area ◽  
The Impact

The applications of location-based services require precise location information of a user both indoors and outdoors. Global positioning system’s reduced accuracy for indoor environments necessitated the initiation of Indoor Positioning Systems (IPSs). However, the development of an IPS which can determine the user’s position with heterogeneous smartphones in the same fashion is a challenging problem. The performance of Wi-Fi fingerprinting-based IPSs is degraded by many factors including shadowing, absorption, and interference caused by obstacles, human mobility, and body loss. Moreover, the use of various smartphones and different orientations of the very same smartphone can limit its positioning accuracy as well. As Wi-Fi fingerprinting is based on Received Signal Strength (RSS) vector, it is prone to dynamic intrinsic limitations of radio propagation, including changes over time, and far away locations having similar RSS vector. This article presents a Wi-Fi fingerprinting approach that exploits Wi-Fi Access Points (APs) coverage area and does not utilize the RSS vector. Using the concepts of APs coverage area uniqueness and coverage area overlap, the proposed approach calculates the user’s current position with the help of APs’ intersection area. The experimental results demonstrate that the device dependency can be mitigated by making the fingerprinting database with the proposed approach. The experiments performed at a public place proves that positioning accuracy can also be increased because the proposed approach performs well in dynamic environments with human mobility. The impact of human body loss is studied as well.

scholarly journals GEOFENCING-BASED LOCALIZATION FOR 3D DATA ACQUISITION NAVIGATION

2016 ◽  
Vol XLI-B4 ◽  
pp. 319-324
Author(s):  
M. Nakagawa ◽  
T. Kamio ◽  
H. Yasojima ◽  
T. Kobayashi
Keyword(s):  
Data Acquisition ◽  
Indoor Positioning ◽  
Outdoor Environment ◽  
Indoor Environments ◽  
Positioning Systems ◽  
3D Data ◽  
Localization And Mapping ◽  
Sensor Position ◽  
Position Data ◽  
Infrastructure Inspection

Users require navigation for many location-based applications using moving sensors, such as autonomous robot control, mapping route navigation and mobile infrastructure inspection. In indoor environments, indoor positioning systems using GNSSs can provide seamless indoor-outdoor positioning and navigation services. However, instabilities in sensor position data acquisition remain, because the indoor environment is more complex than the outdoor environment. On the other hand, simultaneous localization and mapping processing is better than indoor positioning for measurement accuracy and sensor cost. However, it is not easy to estimate position data from a single viewpoint directly. Based on these technical issues, we focus on geofencing techniques to improve position data acquisition. In this research, we propose a methodology to estimate more stable position or location data using unstable position data based on geofencing in indoor environments. We verify our methodology through experiments in indoor environments.

scholarly journals Evolution of Indoor Positioning Technologies: A Survey

2017 ◽  
Vol 2017 ◽  
pp. 1-21 ◽  
Author(s):  
Ramon F. Brena ◽  
Juan Pablo García-Vázquez ◽  
Carlos E. Galván-Tejada ◽  
David Muñoz-Rodriguez ◽  
Cesar Vargas-Rosales ◽  
...  
Keyword(s):  
Classification Scheme ◽  
Communication Technologies ◽  
Indoor Positioning ◽  
Wide Field ◽  
Market Opportunity ◽  
Indoor Environments ◽  
Positioning Systems ◽  
Limited Perspective ◽  
Wide Range ◽  
Key Technologies

Indoor positioning systems (IPS) use sensors and communication technologies to locate objects in indoor environments. IPS are attracting scientific and enterprise interest because there is a big market opportunity for applying these technologies. There are many previous surveys on indoor positioning systems; however, most of them lack a solid classification scheme that would structurally map a wide field such as IPS, or omit several key technologies or have a limited perspective; finally, surveys rapidly become obsolete in an area as dynamic as IPS. The goal of this paper is to provide a technological perspective of indoor positioning systems, comprising a wide range of technologies and approaches. Further, we classify the existing approaches in a structure in order to guide the review and discussion of the different approaches. Finally, we present a comparison of indoor positioning approaches and present the evolution and trends that we foresee.

scholarly journals GEOFENCING-BASED LOCALIZATION FOR 3D DATA ACQUISITION NAVIGATION

2016 ◽  
Vol XLI-B4 ◽  
pp. 319-324
Author(s):  
M. Nakagawa ◽  
T. Kamio ◽  
H. Yasojima ◽  
T. Kobayashi
Keyword(s):  
Data Acquisition ◽  
Indoor Positioning ◽  
Outdoor Environment ◽  
Indoor Environments ◽  
Positioning Systems ◽  
3D Data ◽  
Localization And Mapping ◽  
Sensor Position ◽  
Position Data ◽  
Infrastructure Inspection

Users require navigation for many location-based applications using moving sensors, such as autonomous robot control, mapping route navigation and mobile infrastructure inspection. In indoor environments, indoor positioning systems using GNSSs can provide seamless indoor-outdoor positioning and navigation services. However, instabilities in sensor position data acquisition remain, because the indoor environment is more complex than the outdoor environment. On the other hand, simultaneous localization and mapping processing is better than indoor positioning for measurement accuracy and sensor cost. However, it is not easy to estimate position data from a single viewpoint directly. Based on these technical issues, we focus on geofencing techniques to improve position data acquisition. In this research, we propose a methodology to estimate more stable position or location data using unstable position data based on geofencing in indoor environments. We verify our methodology through experiments in indoor environments.

scholarly journals An Enhanced Indoor Positioning Technique Based on a Novel Received Signal Strength Indicator Distance Prediction and Correction Model

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 719
Author(s):  
Mohammed Nagah Amr ◽  
Hussein M. ELAttar ◽  
Mohamed H. Abd El Azeem ◽  
Hesham El Badawy
Keyword(s):  
Signal Strength ◽  
Indoor Positioning ◽  
Low Energy ◽  
Indoor Environments ◽  
Positioning Accuracy ◽  
Correction Model ◽  
Positioning Technique ◽  
Unknown Node ◽  
Positioning Algorithm ◽  
Distance Prediction

Indoor positioning has become a very promising research topic due to the growing demand for accurate node location information for indoor environments. Nonetheless, current positioning algorithms typically present the issue of inaccurate positioning due to communication noise and interferences. In addition, most of the indoor positioning techniques require additional hardware equipment and complex algorithms to achieve high positioning accuracy. This leads to higher energy consumption and communication cost. Therefore, this paper proposes an enhanced indoor positioning technique based on a novel received signal strength indication (RSSI) distance prediction and correction model to improve the positioning accuracy of target nodes in indoor environments, with contributions including a new distance correction formula based on RSSI log-distance model, a correction factor (Beta) with a correction exponent (Sigma) for each distance between unknown node and beacon (anchor nodes) which are driven from the correction formula, and by utilizing the previous factors in the unknown node, enhanced centroid positioning algorithm is applied to calculate the final node positioning coordinates. Moreover, in this study, we used Bluetooth Low Energy (BLE) beacons to meet the principle of low energy consumption. The experimental results of the proposed enhanced centroid positioning algorithm have a significantly lower average localization error (ALE) than the currently existing algorithms. Also, the proposed technique achieves higher positioning stability than conventional methods. The proposed technique was experimentally tested for different received RSSI samples’ number to verify its feasibility in real-time. The proposed technique’s positioning accuracy is promoted by 80.97% and 67.51% at the office room and the corridor, respectively, compared with the conventional RSSI trilateration positioning technique. The proposed technique also improves localization stability by 1.64 and 2.3-fold at the office room and the corridor, respectively, compared to the traditional RSSI localization method. Finally, the proposed correction model is totally possible in real-time when the RSSI sample number is 50 or more.

scholarly journals An F-Score-Weighted Indoor Positioning Algorithm Integrating WiFi and Magnetic Field Fingerprints

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Sinem Bozkurt Keser ◽  
Ahmet Yazici ◽  
Serkan Gunal
Keyword(s):  
Magnetic Field ◽  
Likelihood Estimation ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Single Type ◽  
Positioning System ◽  
Real World Data ◽  
Positioning Systems ◽  
Signal Type ◽  
Positioning Algorithm

Indoor positioning systems have attracted much attention with the recent development of location-based services. Although global positioning system (GPS) is a widely accepted and accurate outdoor localization system, there is no such a solution for indoor areas. Therefore, various systems are proposed for the indoor positioning problem. Fingerprint-based positioning is one of the widely used methods in this area. WiFi-received signal strength (RSS) is a frequently used signal type for the fingerprint-based positioning system. Since WiFi signal distribution is nonstationary, accuracy is insufficient. Therefore, the performance of indoor positioning systems can be enhanced using multiple signal types. However, the positioning performance of each signal type varies depending on the characteristics of the environment. Considering the variability of the performances of different signal types, an F-score-weighted indoor positioning algorithm, which integrates WiFi-RSS and MF fingerprints, is proposed in this study. In the proposed approach, the positioning is first performed by maximum likelihood estimation for both WiFi-RSS and magnetic field signal values to calculate the F-score of each signal type. Then, each signal type is combined using F-score values as a weight to estimate a position. The experiments are performed using a publicly available dataset that contains real-world data. Experimental results reveal that the proposed algorithm is efficient in achieving accurate indoor positioning and consolidates the system performance compared to using a single type of signal.

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