Indoor Tracking and Navigation Using Received Signal Strength and Compressive Sensing on a Mobile Device

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

“…If not, the results are discarded. Similar idea is also presented in [83] where the Wi-Fi fingerprint matching is formulated as a compressive sensing problem. Although these approaches improve robustness to the RSS variation, strong belief in the sensors make them vulnerable to sensor biases.…”

“…Subsequently, the Kalman filter is used to update the initial location and track the mobile user, thus mitigating the estimation error. Other systems employ motion sensors together with Bayesian filters [73], [82], [83]. In these solutions, the device location is predicted from motion sensor readings, and the predicted location is updated with WiFi fingerprint matching results.…”

“…However, in the presence of RSS variation, the fingerprint matching results contain large errors, and the filter output will diverge (has an error monotonically increasing) after a few iterations. This problem is interpreted as the model mismatch in the sense of Bayesian inference [83], [84]. Although the aforementioned calibration methods can be a good solution for the model mismatch mitigation, it is still vulnerable to the sudden change of the RSS variation characteristics.…”

“…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…”

Graph-Based Semi-Supervised Learning for Indoor Localization Using Crowdsourced Data

“…However, collecting more RSS measurements at any location makes the offline phase even more time-consuming and labour-intensive. Several works have been proposed to reduce the workload of the offline phase [11][12][13]. The crowdsourcing method has been shown to be a promising approach to solving this problem [14][15][16].…”