Wi-Fi signals database construction using chebyshev wavelets for indoor positioning systems

Nowadays fast and accurate positioning of assets and people is as a crucial part of many businesses, such as, warehousing, manufacturing and logistics. Applications that offer different services based on mobile user location gaining more and more attention. Some of the most common applications include location-based advertising, directory assistance, point-to-point navigation, asset tracking, emergency and fleet management. While outdoors mostly covered by the Global Positioning System, there is no one versatile solution for indoor positioning. For the past decade Wi-Fi fingerprinting based indoor positioning systems gained a lot of attention by enterprises as an affordable and flexible solution to track their assets and resources more effectively. The concept behind Wi-Fi fingerprinting is to create signal strength database of the area prior to the actual positioning. This process is known as a calibration carried out manually and the indoor positioning system accuracy highly depends on a calibration intensity. Unfortunately, this procedure requires huge amount of time, manpower and effort, which makes extensive deployment of indoor positioning system a challenging task. approach of constructing signal strength database from a minimal number of measurements using Chebyshev wavelets approximation. The main objective of the research is to minimize the calibration workload while providing high positioning accuracy. The field tests as well as computer simulation results showed significant improvement in signal strength prediction accuracy compared to existing approximation algorithms. Furthermore, the proposed algorithm can recover missing signal values with much smaller number of on-site measurements compared to conventional calibration algorithm

Wi-Fi signal strength vs. magnetic fields for indoor positioning systems

In this research we compare Wi-Fi received signal strength indication and magnetic field based real-time location systems (RTLS) from various perspectives such as system complexity, accuracy and stability. To evaluate the performance of these systems we built several test fields with different types of environments. We will compare both approaches side-by-side and answer such issues as optimal calibration step (measurement interval), location accuracy, effect of minor and major environment changes to fingerprint DB and overall system accuracy

Wi-Fi signal strengths database construction for indoor positioning systems using Wi-Fi RFID

Nowadays, fingerprinting based Wi-Fi positioning systems successfully provide location information to mobile users. Main idea behind fingerprinting is to build signal strength database of target area prior to location estimation. This process is called calibration. Indoor positioning system accuracy highly depends on calibration (sampling) intensity. This procedure requires huge amount of time and effort, and makes large-scale deployments of indoor positioning systems non-trivial. Newly constructed database may no longer be valid if there are any major changes in the target site. In this research we present a new approach of constructing fingerprint database. We propose a hybrid calibration procedure that combines signal sampling process with path-loss prediction algorithm. Instead of manual signal sampling, proposed method requires several Wi-Fi RFID tags to be installed in a target site. Advantage of such tag is that it can be read directly by commercial Wi-Fi access points from long distance. Several RFID tags mounted in target area will monitor the signal strength levels continuously and send scan data to the server. Whenever there are significant changes in signal levels detected, server will initiate database reconstruction procedure. Compared to existing calibration procedure our method requires only few signal samples from RFID tags to be collected and rest of the database is recovered using path-loss prediction algorithm

Wi-Fi Signals Database Construction using Chebyshev Wavelets for Indoor Positioning Systems

Nowadays fast and accurate positioning of assets and people is as a crucial part of many businesses, such as, warehousing, manufacturing and logistics. Applications that offer different services based on mobile user location gaining more and more attention. Some of the most common applications include location-based advertising, directory assistance, point-to-point navigation, asset tracking, emergency and fleet management. While outdoors mostly covered by the Global Positioning System, there is no one versatile solution for indoor positioning. For the past decade Wi-Fi fingerprinting based indoor positioning systems gained a lot of attention by enterprises as an affordable and flexible solution to track their assets and resources more effectively. The concept behind Wi-Fi fingerprinting is to create signal strength database of the area prior to the actual positioning. This process is known as a calibration carried out manually and the indoor positioning system accuracy highly depends on a calibration intensity. Unfortunately, this procedure requires huge amount of time, manpower and effort, which makes extensive deployment of indoor positioning system a challenging task.  approach of constructing signal strength database from a minimal number of measurements using Chebyshev wavelets approximation. The main objective of the research is to minimize the calibration workload while providing high positioning accuracy.  The field tests as well as computer simulation results showed significant improvement in signal strength prediction accuracy compared to existing approximation algorithms. Furhtermore, the proposed algorithm can recover missing signal values with much smaller number of on-site measurements compared to conventional calibration algorithm

Wi-Fi received signal strength-based hyperbolic location estimation for indoor positioning systems

Nowadays, Wi-Fi fingerprinting-based positioning systems provide enterprises the ability to track their various resources more efficiently and effectively. The main idea behind fingerprinting is to build signal strength database of target area prior to location estimation. This process is called calibration and the positioning accuracy highly depends on calibration intensity. Unfortunately, calibration procedure requires huge amount of time and effort, and makes large scale deployments of Wi-Fi based indoor positioning systems non-trivial. In this research we present a novel location estimation algorithm for Wi-Fi based indoor positioning systems. The proposed algorithm combines signal sampling and hyperbolic location estimation techniques to estimate the location of mobile users. The algorithm achieves cost-efficiency by reducing the number of fingerprint measurements while providing reliable location accuracy. Moreover, it does not require any additional hardware upgrades to the existing network infrastructure. Experimental results show that the proposed algorithm with easy-to-build signal strength database performs more accurate than conventional signal strength-based methods

Simple motion pursuit differential game of many pursuers and one evader on convex compact set

We study a differential game of many pursuers and single evader in nonempty closed bounded convex subset of ℝn. In this game, all players cannot leave the given set. Control parameters of all players are subjected to geometric constraints. Maximum speeds of all players are equal to 1. Pursuit is said to be completed if geometric position of at least one pursuer coincides with that of the evader. Pursuers try to complete the pursuit. Problem is to find estimate for guaranteed pursuit time. To solve the problem, first, we study the same problem in an n-dimensional cube. Then, we reduce the main problem to the game in the cube. To this end, we use method of fictitious pursuers. In this paper, we improve the estimate for guaranteed pursuit time from O(n3) to O(n2)

Wi-fi fingerprint database construction using Chebyshev wavelet functions

In this research we propose a new approach of constructing Wi-Fi fingerprints database using Chebyshev wavelet functions. Proposed algorithm combines the reference data collection procedure with path-loss prediction. Our algorithm requires only a few samples to be collected in a given region, and thus significantly reduces the calibration time. Next, the collected reference samples are used to construct area specific path-loss prediction function using Chebyshev wavelets. Once we have the path-loss function, we can predict the signal strength values at any point of the target area. One of the advantages of proposed algorithm is that it does not require any detailed information about target area, i.e. location of wireless access points, number of walls (floors, obstructions) between the transmitter and reference points and etc