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

A Fast-rate WLAN Measurement Tool for Improved Miss-rate in Indoor Navigation

Recently, location-based services (LBS) have steered attention to indoor positioning systems (IPS). WLAN-based IPSs relying on received signal strength (RSS) measurements such as fingerprinting are gaining popularity due to proven high accuracy of their results. Typically, sets of RSS measurements at selected locations from several WLAN access points (APs) are used to calibrate the system. Retrieval of such measurements from WLAN cards are commonly at one-Hz rate. Such measurement collection is needed for offline radio-map surveying stage which aligns fingerprints to locations, and for online navigation stage, when collected measurements are associated with the radio-map for user navigation. As WLAN network is not originally designed for positioning, an RSS measurement miss could have a high impact on the fingerprinting system. Additionally, measurement fluctuations require laborious signal processing, and surveying process can be very time consuming. This paper proposes a fast-rate measurement collection method that addresses previously mentioned problems by achieving a higher probability of RSS measurement collection during a given one-second window. This translates to more data for statistical processing and faster surveying. The fast-rate collection approach is analyzed against the conventional measurement rate in a proposed testing methodology that mimics real-life scenarios related to IPS surveying and online navigation

Modelling of Indoor Positioning Systems Based on Location Fingerprinting

In recent years, localization systems for indoor vicinity using the present wireless local area (WLAN) network infrastructure have been proposed. Such positioning systems create the usage of location fingerprinting instead of direction or time of arrival techniques for deciding the location of mobile users. However experimental study associated to such localization systems have been proposed, high attenuation and signal scattering related to greater density of wall attenuation still affecting the indoor positioning performance. This paper presents an analytical model for minimizing high signal attenuation effect for WLAN fingerprinting indoor positioning systems. The model employs the probabilistic algorithm that using signal relation method

Maximum convergence algorithm for WiFi based indoor positioning system

WiFi-based indoor positioning is widely exploited thanks to the existing WiFi infrastructure in buildings and built-in sensors in smartphones. The techniques for indoor positioning require the high-density training data to archive high accuracy with high computation complexity. In this paper, the approach for indoor positioning systems which is called the maximum convergence algorithm is proposed to find the accurate location by the strongest receiver signal in the small cluster and K nearest neighbours (KNN) of other clusters. Also, the K-mean clustering is deployed for each access point to reduce the computation complexity of the offline databases. Moreover, the pedestrian dead reckoning (PDR) method and Kalman filter with the information from the received signal strength (RSS) and inertial sensors are applied to the WiFi fingerprinting to increase the efficiency of the mobile object's position. The different experiments are performed to compare the proposed algorithm with the others using KNN and PDR. The recommended framework demonstrates significant proceed based on the results. The average precision of this system can be lower than 1.02 meters when testing in the laboratory environment with an area of 7x7 m using three access points

Sisätilapaikannus älykotijärjestelmissä

Bluetooth ja Wlan ovat radiotaajuisia signaaleja (radio frequency, RF) käyttäviä yhteysteknologioita, joita voidaan hyödyntää sisätilapaikannuksessa. Bluetooth ja Wlan tukevat vastaanotetun signaalitehon (received signal strength, RSS) mittaamista, jonka avulla voidaan selvittää käyttäjälaitteen etäisyys tukiasemasta tai muusta staattisesta lähettimestä. Molemmilla teknologioilla saavutetaan parhaimmillaan alle metrin tarkkuus paikannuksessa hyödyntämällä erilaisia laitteistokonfiguraatioita ja paikannusalgoritmeja. Tutkielmassa vertaillaan teknologioiden paikannusominaisuuksia sekä laitteiston soveltumista älykotijärjestelmiin. Tutkimuksessa havaittiin, että paikannustarkkuus yltää molemmilla teknologioilla toivotulle tasolle, jotta paikannus eri huoneiden välillä on mahdollista. Bluetooth low energy (BLE) -teknologian säädettävät parametrit ovat eduksi paikantamisessa ja tekevät teknologiasta soveltuvan monenlaisiin käyttöympäristöihin ja paikannustarpeisiin. Skannausintervallia ja -ikkunaa muuttamalla saadaan optimoitua energiankulutusta sekä säädettyä paikannustarkkuutta halutulle tasolle. Wlan-laitteet eivät usein tue signaaliparametrien säätämistä, eikä niissä ole paikannusta varten suunniteltuja ominaisuuksia. BLE-teknologia kuluttaa huomattavasti vähemmän virtaa kuin Wlan-teknologia ja sopii siten paremmin käytettäväksi kannettavissa paikannuslaitteissa sekä asennettuna paikoissa, joissa verkkovirtapistoketta ei ole lähellä. BLE- ja Wlan-paikannuslaitteistot ovat yksinkertaisia ja vaativat vähintään yhden lähetinlaitteen sekä yhden vastaanottimen. BLE-laitteisto on pienemmän kokonsa ansiosta käytännöllisempi kuin Wlan-laitteisto ja se on helpompi asentaa pois näkyvistä. Molemmat teknologiat toimivat sisätilapaikannuksessa älykotijärjestelmissä, mutta Bluetooth hyötyy BLE:n tarjoamista paikannukseen kohdistuvista optimoinneista ja ominaisuuksista, jotka Wlan-teknologialta puuttuvat. BLE-teknologian korkeampi energiatehokkuus sekä laitteiston alhaisemmat hankintakustannukset ja parempi käytännöllisyys saavat teknologian soveltumaan sisätilapaikannukseen älykotijärjestelmissä paremmin kuin Wlan-teknologian

An Implementation Approach and Performance Analysis of Image Sensor Based Multilateral Indoor Localization and Navigation System

Optical camera communication (OCC) exhibits considerable importance nowadays in various indoor camera based services such as smart home and robot-based automation. An android smart phone camera that is mounted on a mobile robot (MR) offers a uniform communication distance when the camera remains at the same level that can reduce the communication error rate. Indoor mobile robot navigation (MRN) is considered to be a promising OCC application in which the white light emitting diodes (LEDs) and an MR camera are used as transmitters and receiver respectively. Positioning is a key issue in MRN systems in terms of accuracy, data rate, and distance. We propose an indoor navigation and positioning combined algorithm and further evaluate its performance. An android application is developed to support data acquisition from multiple simultaneous transmitter links. Experimentally, we received data from four links which are required to ensure a higher positioning accuracy

IMPROVED INDOOR POSITIONING USING FINGERPRINT TECHNIQUE AND WEIGHTED K-NEAREST NEIGHBOUR

Global Navigation Satellite Systems are not effective when there is no direct line of sight between the user and the satellites, such as indoor environments and dense urban areas. Today, location-based services are used significantly due to their utility and ease of access. The fingerprint method is one of the common methods of determining the location in indoor environments. In this research, the indoor positioning system based on the fingerprint algorithm with a wireless network has been implemented. The positioning system based on the method of nearest neighbour and weighted K-nearest neighbour with two access points has been implemented in two different scenarios. The output accuracy of each technique has been compared to each other. The main goal of this article is to compare the accuracy of positioning with the fingerprint method using the mentioned algorithms and to find the most suitable mode and algorithm for determining the indoor position in most places. The improved weighted nearest neighbour method will have an almost acceptable result in all scenarios and also in the first scenario with dense and regular reference points the weighted K-nearest neighbour method with RMSE=0.2812(m) has provided the best result. In the second scenario with scattered and irregular reference points the weighted K-nearest neighbour with RMSE=0.6735(m) has given lower accuracy result

Fingerprinting Based Indoor Localization Considering the Dynamic Nature of Wi-Fi Signals

Current localization techniques in the outdoors cannot work well in indoors. The Wi-Fi fingerprinting technique is an emerging localization technique for indoor environments. However, in this technique, the dynamic nature of WiFi signals affects the accuracy of the measurements. In this paper, we use the affinity propagation clustering method to decrease the computation complexity in location estimation. Then, we use the least variance of Received Signal Strength (RSS) measured among Access Points (APs) in each cluster. Also, we assign lower weights to alter APs for each point in a cluster, to represent the level of similarity to Test Point (TP) by considering the dynamic nature of signals in indoor environments. A method for updating the radio map and improving the results is then proposed to decrease the cost of constructing the radio map. Simulation results show that the proposed method has 22.5% improvement in average in localization results, considering one altering AP in the layout, compared to the case when only RSS subset sampling is considered for localization because of altering APs