Algorithm for Dynamic Fingerprinting Radio Map Creation Using IMU Measurements

Abstract

While a vast number of location-based services appeared lately, indoor positioning solutions are developed to provide reliable position information in environments where traditionally used satellite-based positioning systems cannot provide access to accurate position estimates. Indoor positioning systems can be based on many technologies; however, radio networks and more precisely Wi-Fi networks seem to attract the attention of a majority of the research teams. The most widely used localization approach used in Wi-Fi-based systems is based on fingerprinting framework. Fingerprinting algorithms, however, require a radio map for position estimation. This paper will describe a solution for dynamic radio map creation, which is aimed to reduce the time required to build a radio map. The proposed solution is using measurements from IMUs (Inertial Measurement Units), which are processed with a particle filter dead reckoning algorithm. Reference points (RPs) generated by the implemented dead reckoning algorithm are then processed by the proposed reference point merging algorithm, in order to optimize the radio map size and merge similar RPs. The proposed solution was tested in a real-world environment and evaluated by the implementation of deterministic fingerprinting positioning algorithms, and the achieved results were compared with results achieved with a static radio map. The achieved results presented in the paper show that positioning algorithms achieved similar accuracy even with a dynamic map with a low density of reference points.

Automated Calibration of RSS Fingerprinting Based Systems Using a Mobile Robot and Machine Learning

“…In the most common crowdsourcing methods intended for use in smartphone-based systems, the user location is usually derived based on the smartphone sensors reading, for example, from inertial units [ 16 ] or magnetometers [ 9 ]. In [ 16 ], the user is localised using a dead-reckoning algorithm with pulling the positioning results to characteristic points of the environment like doorways or narrow corridors. A similar approach is used in [ 6 , 17 ], where the characteristic landmarks used to improve the accuracy are building entrances, doorways and corridor intersections.…”

“…The easiest way is to directly use the gathered fingerprints as the radio map as in [ 17 , 21 , 25 , 26 ]. Another simple solution is to average the fingerprints gathered in the same or very close locations [ 6 , 16 ].…”

Proposal for a Localization System for an IoT Ecosystem

“…Lately, a lot of research has been oriented toward positioning using data from low-cost IMUs [25]. Previously, we described the implementation of PDR with a particle filter [26], as well as its possible use for dynamic radio map creation [27], which is the main reason for the implementation of PDR into the integrated localization system. Using this method, it is possible to extend the coverage area of the implemented fingerprinting localization in the system without time-consuming and labor-intensive measurements.…”

“…The radio map update is performed by the map fusion algorithm, which will add the data to the radio map and perform radio map optimization, i.e., the merging of reference points in the radio map in a case when the physical distance or RSS values are very similar. More details on the PF-PDR algorithm and map fusion algorithm can be found in [27].…”

Smart Sensor Technologies for IoT

“…In [ 10 ], the solution for a dynamic radio map collection is introduced. The proposed solution is based on simultaneous measurements of Received Signal Strength (RSS) from Wi-Fi networks and the Inertial Measurement Unit (IMU) data collection.…”