Indoor Location Sensing with Invariant Wi-Fi Received Signal Strength Fingerprinting

Abstract

A method of location fingerprinting based on the Wi-Fi received signal strength (RSS) in an indoor environment is presented. The method aims to overcome the RSS instability due to varying channel disturbances in time by introducing the concept of invariant RSS statistics. The invariant RSS statistics represent here the RSS distributions collected at individual calibration locations under minimal random spatiotemporal disturbances in time. The invariant RSS statistics thus collected serve as the reference pattern classes for fingerprinting. Fingerprinting is carried out at an unknown location by identifying the reference pattern class that maximally supports the spontaneous RSS sensed from individual Wi-Fi sources. A design guideline is also presented as a rule of thumb for estimating the number of Wi-Fi signal sources required to be available for any given number of calibration locations under a certain level of random spatiotemporal disturbances. Experimental results show that the proposed method not only provides 17% higher success rate than conventional ones but also removes the need for recalibration. Furthermore, the resolution is shown finer by 40% with the execution time more than an order of magnitude faster than the conventional methods. These results are also backed up by theoretical analysis.

A New Approach for Wi-Fi-Based People Localization in a Long Narrow Space

“…Generally, there are four types of measurements used for Wi-Fi and smartphone-based indoor positioning: time of arrival (ToA) [8,9], time difference of arrival (TDoA) [10,11], angle of arrival (AoA) [12], and received signal strength indicator (RSSI) based models [13][14][15]. The RSSI-based model is more popular than the other three [16,17] and therefore is employed for SLDC. The distance between a customer and an AP can be calculated by a commonly used path loss model [18,19] as shown below.…”

Sequence-Based Indoor Localization with Channel Status Information

“…Currently, there exist two types of wireless localization methods: range-based localization [ 11 , 12 , 13 , 14 , 15 , 16 , 17 ] and range-free localization [ 18 , 19 , 20 ], according to whether there is actually the distance or angle of measured nodes in the localization process. The range-based localization mechanisms determine the localization of unknown nodes by measuring the actual point-to-point distance or angle information between nodes and using the trilateration, triangulation or maximum likelihood estimation methods, based on the time of arrival (TOA) [ 11 ], time difference of arrival (TDOA) [ 12 , 13 ], angle of arrival (AOA) [ 14 ], signal-based signature distance [ 15 ], received signal strength indicator (RSSI) [ 16 , 17 ], and so on. In the above four algorithms, TOA, TDOA and AOA require additional hardware devices, while RSSI does not because the interface of most nodes has the function of receiving radio frequency (RF) signals [ 21 ] and can test the signal strength.…”

A Sensor Fusion Framework for Indoor Localization Using Smartphone Sensors and Wi-Fi RSSI Measurements

“…To overcome these challenges, it is necessary to develop alternative localization systems for indoor localization. Existing indoor localization systems are based on ultra-wideband (UWB) technology [3], pedestrian dead reckoning (PDR) [4], radio frequency identification (RFID) [5], Bluetooth [6], visible light communication (VLC) [7], Zigbee [8] and Wi-Fi systems [9]. Each of these techniques achieves high position accuracy for indoor localization.…”