Indoor navigation is challenging due to unavailability of satellites-based signals indoors.
WiFi and inertial navi- gation systems INS are used for positioning and attitude determination in a wide range of applications.
Over the last few years, a number of low-cost inertial sensors have become available. Although they exhibit large errors, WiFi measurements can be used to correct the drift weakening the navigation based on this technology. On the other hand, INS sensors can interact with the WiFi positioning system as they provide high-accuracy real-time navigation.
A structure based on a Kalman filter and a particle filter is proposed. It fuses the heterogeneous information coming from those two independent technologies. Finally, the benefits of the proposed architecture are evaluated and compared with the pure WiFi and INS positioning systems. Including such information requires new filters as the Kalman filter is Mobile positioning becomes of increasing interest for the not adapted for this.
Particle filters [6—8], based on Monte- wireless telecom operators. Indeed, many applications re- Carlo simulations, are emerging to solve the problems of po- quire an accurate location information of the mobile sition estimation. They can provide con- door environments, the received signals are too weak to pro- tinuous position, velocity, and also orientation estimates, vide an accurate location using those technologies.
Currently, which are accurate for a short term, but are subject to drift given that many buildings are equipped with WLAN access due to noise of the sensor [9, 10].
WiFi and inertial navigation systems (INS) are used for positioning and attitude determination in a wide range of applications. Advanced Integration of WiFi and Inertial Navigation Systems for Indoor Mobile Positioning. Frédéric Evennou 1 Email author and ; François Marx 1; EURASIP Journal on Advances in Signal Processing . Satellite navigation and positioning. Location technology. Multi-constellation GNSS: GPS, GLONASS, Galileo and Beidou. Inertial navigation systems. Positioning using signals of opportunity, WiFi, DAB etc. Integration of navigation sensors. Robust navigation in GNSS denied environments, such as indoor positioning. Location based services. WiFi and inertial navi-gation systems (INS) are used for positioning and attitude determination in a wide range of applications. Over the last few years, Inertial navigation systems (INS) are one of the most widely used dead-reckoning systems. They can provide con-.
Filtering techniques will points shopping malls, museums, hospitals, airports, etc. The Kalman filter is already used in many mine user location in these indoor environments. The location technique Moreover, the strength of the INS system should annihilate is based on the measurement of the received signal strength the weaknesses of WiFi and vice versa.
Those heterogenous RSS and the well-known fingerprinting method [2, 3].
The but complementary technologies should lead to an enhanced accuracy depends on the number of positions registered in system in terms of positioning performance as well as avail- the database.
Besides, signal fluctuations over time introduce ability of the positioning service over a larger area. A simple temporal averaging filter does not give sat- provide a position estimate which is degraded over the time isfying results.
Kalman filtering [4, 5] is commonly used in but can be reliable over a certain period. How- This paper presents in its second section the basic tech- ever, more information can be used to improve the accu- niques leading to a first estimate of the position of a WiFi racy.
In the following sections, we choose to use a map of the device thanks to the associated network.
The third section environment. Combining those estimated ranges with a 2. Many outdoor systems are based on time measurements, that Further investigations showed that introducing the es- is, the mobile equipment and the network are synchronized.WiFi and inertial navigation systems (INS) are used for positioning and attitude determination in a wide range of applications.
Over the last few years, a number of low-cost inertial sensors have become available. Although they exhibit large errors, WiFi measurements can be used to correct the drift weakening the navigation based on this technology.
Seamless outdoor/indoor navigation with WIFI/GPS aided low cost Inertial Navigation System. Block diagram of the seamless outdoor–indoor navigation with step detection aided IMU/GPS/WIFI integrated system. F. MarxAdvanced integration of WIFI and inertial navigation systems for indoor mobile positioning.
EURASIP J. . All inertial navigation systems suffer from integration drift: small errors in the measurement of acceleration and angular velocity are integrated into progressively larger errors in velocity, which are compounded into still greater errors in position. EVENNOU F, MARX F.
Advanced integration of WiFi and inertial navigation systems for indoor mobile positioning [J]. EURASIP Journal on Applied Signal Processing, , Article ID: .
pertaining to strapdown inertial navigation systems and integration with GPS . However, much of this literature is difficult to follow for the uninitiated.
A novel particle filter approach for indoor positioning by fusing WiFi and inertial sensors.
F. MarxAdvanced integration of WiFi and inertial navigation systems for indoor mobile positioning. L. OjedaMap matching and heuristic elimination of gyro drift for personal navigation systems in GPS-denied conditions.