Efficient Techniques and Algorithms for Improving Indoor Localization Precision on WLAN Networks Applications (original) (raw)
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Wireless Indoor Localization Using Fingerprinting Technique
Journal of Advanced Research in Dynamical and Control Systems, 2020
Indoor positioning has gained more interest as one of the upcoming applications due to its use in a variety of services. Multiple technologies such as Bluetooth, Wi-Fi, RFID. However, Wi-Fi based localization in indoor environment offers significant advantages utilizing installed wireless infrastructures and good performances with low cost. With this study, we aim to provide a compromise between accurate positioning and feasibility of the system for practical applications. For this purpose, we minimize the fluctuations of Wi-Fi received signal strength (RSS) by filtering and we combine two approaches to locate a mobile user. At first, we implemeted the traditional fingerprinting technique that uses a real time matching of pre-recorded received signal strength (RSS) from the location data of the user transmitted to nearby access points (AP). Secondly, we used distance-based trilateration technique which determines positions using three known access points. The combination of the two methods provides enhancement of accuracy and wide indoor locating coverage. Regardless the locating data number, experiment confirmed a significant and a consistent performance in term of execution time and accuracy.
A comparative survey of WLAN location fingerprinting methods
Proceedings - 6th Workshop on Positioning, Navigation and Communication, WPNC 2009, 2009
The term "location fingerprinting" covers a wide variety of methods for determining receiver position using databases of radio signal strength measurements from different sources. In this work we present a survey of location fingerprinting methods, including deterministic and probabilistic methods for static estimation, as well as filtering methods based on Bayesian filter and Kalman filter. We present a unified mathematical formulation of radio map database and location estimation, point out the equivalence of some methods from the literature, and present some new variants. A set of tests in an indoor positioning scenario using WLAN signal strengths is performed to determine the influence of different calibration and location method parameters. In the tests, the probabilistic method with the kernel function approximation of signal strength histograms was the best static positioning method. Moreover, all filters improved the results significantly over the static methods.
Location Fingerprinting Technique for WLAN Device-Free Indoor Localization System
Wireless Personal Communications, 2016
Device-free indoor localization (DFIL) system can locate the position of human body in the indoor environment by observing the changes in the received signal strength indicator (RSSI) of the wireless local area network (WLAN). The accuracy of a DFIL system is depreciated, as the change in the indoor environment due to furniture and other infrastructure movement. This paper investigates the development of testbed of the WLAN network for measuring the RSSI in various indoor environment, as the initial step for designing the fingerprinting-based algorithms for WLAN network. The database of RSSI fingerprint is created initially and then a fingerprint-based algorithm is developed for locating the position of a human body in the indoor environment. The localization algorithm tests the minimum distance in the RSSI values related to the different test points in the indoor environment. This work further demonstrates that how the fingerprints of RSSI are collected and which network configurations generate the most reliable RSSI measurement. For the first phase of designing the testbed, the configurations of different equipment and various tools are elaborated in the indoor environment. For the second phase the RSSI is measured in different propagation indoor environment. The extensive experiments were performed that allow quantification of how changes in an environment affect accuracy. Thus, it is demonstrated that each link offers a viable approach to developing a more robust system for device-free localization that is less susceptible to changes in the environment.
Measurements and Analysis of Fingerprinting Structures for WLAN Localization Systems
ETRI Journal, 2016
the weighted k-nearest neighbor pattern recognition technique. Frequency domain channel measurements in the IEEE 802.11 band taken on a university campus were used to evaluate the accuracy of the fingerprinting types and their robustness to human-induced motion perturbations of the channel. The localization performance was analyzed, and the results are described using the spatial and temporal radio propagation characteristics. In particular, we introduce the coherence region to explain the spatial properties and investigate the impact of the Doppler spread in time-varying channels on the time coherence of RF fingerprint structures.
Energies, 2019
The increase of the technology related to radio localization and the exponential rise in the data traffic demanded requires a large number of base stations to be installed. This increase in the base stations density also causes a sharp rise in energy consumption of cellular networks. Consequently, energy saving and cost reduction is a significant factor for network operators in the development of future localization networks. In this paper, a localization method based on ray-tracing and fingerprinting techniques is presented. Simulation tools based on high frequencies are used to characterize the channel propagation and to obtain the ray-tracing data. Moreover, the fingerprinting technique requires a costly initial learning phase for cell fingerprint generation (radio-map). To estimate the localization of mobile stations, this paper compares power levels and delay between rays as cost function with different distance metrics. The experimental results show that greater accuracy can b...
The past few years have seen wide spread adoption of outdoor positioning services, mainly GPS, being incorporated into everyd ay devices such as smartphones and tablets. While outdoor positioning has been well received by the public, its indoor counterpart has been mostly limited to private use due to its higher costs and complexity for setting up the proper environment . The objective of this research is to provide an affordable mean for indoor localization using wireless local area network (WLAN) Wi-Fi technology. We combined two different Wi-Fi approaches to locate a user. The first method involves the use of matching the pre-recorded received signal strength (RSS) from nearby access points (AP), to the data transmitted from the user on the fly. This is commonly known as "fingerprint matching". The second approach is a distance-based trilateration approach using three known AP coordinates detected on the user"s device to derive the position. The combination of the two steps enhances the accuracy of the user position in an indoor environment allowing location-based services (LBS) such as mobile augmented reality (M AR) to be deployed more effectively in the indoor environment. The mapping of the RSS map can also prove useful to IT planning personnel for covering locations with no Wi-Fi coverage (ie. dead spots). The experiments presented in this research helps provide a foundation for the integration of indoor with outdoor positioning to create a seamless transition experience for users.
Using unlocated fingerprints in generation of WLAN maps for indoor positioning
Record - IEEE PLANS, Position Location and Navigation Symposium, 2012
This paper presents five methods for generation of WLAN maps for indoor positioning using crowdsourced fingerprints. A fingerprint is assumed to contain identifiers of WLAN access points, received signal strength values and, if the fingerprint is collected outdoors, a GPS position. The proposed methods use the fingerprints' information to generate a WLAN map that contains estimated access point locations. Two of the proposed methods use RSS values in access point location estimation. In our evaluation with simulations and with real data, the Access Point Least Squares method, which does not use RSS information, is the fastest and its accuracy is as good as more complex methods that use RSS information.
Impact of the number of access points in indoor fingerprinting localization
2010
In the paper the solution for indoor positioning based on IEEE 802.11 platform is proposed and experimentally verified. The paper investigates an impact of the number of access points on the localization accuracy in fingerprinting method. The solution is based on deterministic approach. The properties of the solution are tested by experimental measurements in real environment.
Indoor Location Estimation Utilizing Wi-Fi Signals
International Journal of Emerging Trends in Engineering Research, 2020
Global Positioning System is commonly been used for locating a position of a specific structure in finding geographical coordinates of a target area. Though, this application is still having a restricted in term of the signals, might not well operated and ineffective for indoor usage. The study aim is to develop positioning and localization systems by using Wi-Fi signal. Estimation was made based on the measurement of wireless distance for estimation the user's coordinates. Analysis of views called the fingerprint algorithm is used in this study. The algorithm involved two phases over an offline and the online phases of the survey. Unidentified user's coordinates will be in the online phase by comparative databases collected in the survey phase. MATLAB Graphical User Interface and Android has been used to develop a user interface for simulation purposes. Several analyses were performed to define the precision and efficiency of occurred error as the number of access points and the traffic environment. Finally, the user required to provide several inputs e.g. the exact location and the RSS from AP's number at the present location. The simulation-based software will evaluate the estimation location and positioning of the user and will match to user's precise location.