Efficient Indoor Fingerprinting Localization Technique Using Regional Propagation Model

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

“…Experimental results suggest that with 23 RPs the proposed solution performs equally well as traditional fingerprint matching with over 230 RPs for an office space of 2500 m 2 . Along the same line to address the cost of constructing the radiomap in the offline phase, a new empirical propagation model called Regional Propagation Model (RPM) is used in [88]. The proposed system first collects sparse fingerprints at some certain RPs followed by an affinity propagation clustering algorithm, which operates on the sparse fingerprints to automatically divide the whole scenario into several clusters or sub-regions.…”

“…While some fingerprint matching solutions reduce the cost of constructing the radiomap through signal propagation models and sparse data collection by professionals or trained surveyors (e.g., [87], [88]), there is an increasing trend to exploit localization data collected and shared by common people acting as volunteers. Such solutions leverage on the emerging new paradigm of crowdsourcing and the high availability of smartphone devices featuring multiple sensors [229].…”

“…Hence, these approaches may fail in sparse anchor scenarios. Moreover, the distance estimates to the reliable anchors may include large errors, which means their Wi-Dist uses a convex-optimization formulation and fuses noisy fingerprints Applicable to various sensors and wireless fingerprints, with uncertain mutual distances given by their bounds up to 40% better accuracy than state-of-the-art [80] Integration of human-centric collaboration to improve accuracy by positive Robust with respect to malicious feedback, quickly and negative user feedback on their estimated locations self-correcting based on subsequent helpful feedback [81] Bayesian-rule based objective function and particle swarm optimization Kalman filter updates the initial location and tracks technique combined with Kalman filter for user tracking the user, thus mitigating the estimation error [82] Non-parametric information filter for Wi-Fi RSS fingerprints and sensor Sensitive to sensor drifting readings combined with RP selection, AP selection, and outlier detection [83] Bayesian filter predicts location from motion sensors and updates it with Sensitive to sensor drifting Wi-Fi fingerprint matching formulated as a compressive sensing problem [85] Conditional entropy metric as a dynamic measure of the uncertainty Low entropy values are correlated with small errors, associated to each position estimate high values may indicate small or large errors [86] Extensive analysis for the causes of large errors in Wi-Fi fingerprint Some large errors may be due to the geometry matching with the aim to dynamically estimate the positioning error of the space and access points placement [87] Parametric pathloss model for the GP mean and a non-parametric Using 23 RPs similar accuracy was achieved with covariance function to create the RSS radiomap with a few training data over 230 RPs for an office space of 2500 m 2 [88] Empirical Regional Propagation Model to construct the RSS radiomap Better prediction of RSS values than existing models, from sparse fingerprints through affinity propagation clustering 50% workload reduction for fingerprint data collection…”

“…• WLAN-based localization: Web and mobile geolocation services that are based either on private [57]- [59] or publicly available Wi-Fi databases [60]- [62], state-ofthe-art academic solutions that rely on Wi-Fi technology either standalone or in combination with inertial sensors and/or statistical filtering while addressing the challenges of the fingerprint-based approach [63]- [88], solutions that follow the lateration approach [89]- [93], and systems leveraging on crowdsourcing to construct radio signal databases for localization [94]- [113].…”

A Radio-Map Automatic Construction Algorithm Based on Crowdsourcing

“…Thus, WiFi-based indoor localization methods with location fingerprints [9,10,11,12,13] are commonly used for indoor localization. The WiFi information received by sensors at certain a indoor location is called a “fingerprint”, which is used to identify its corresponding location.…”