A Bayesian Strategy to Enhance the Performance of Indoor Localization Systems

Abstract

Abstract. This work describes the probabilistic modelling of a Bayesian-based mechanism to improve location estimates of an already deployed location system by fusing its outputs with low-cost binary sensors. This mechanism takes advantage of the localization capabilities of different technologies usually present in smart environments deployments. The performance of the proposed algorithm over a real sensor deployment is evaluated using simulated and real experimental data.

An improved geometric algorithm for indoor localization

“…RSSI value collection at each direction last 5 min and the acquisition frequency is once per second. After taking the mean, the measurement distance between each AP and T1 is calculated via equation (16). The RSSI and distance are shown in Table 2.…”

“…12 The main idea is using information of survey RSSI values. It depends on sample location coordinates to generate radio frequency (RF) map which is called fingerprint map for training and labeling databases after the classifier, by matching algorithms, such as K-Nearest Neighbor (K-NN), 13,14 machine-learning-based schemes, 15 Bayesian strategy, 16 neural networks, 17 and data mining based on clustering 18 to estimate MT positions by calculating Euclidean distance between the fingerprint database system and the real-time RSSI values. 19 It can provide a certain accuracy localization; however, building and updating the fingerprint database are expensive and laborious.…”