The Adaptive Fingerprint Localization in Dynamic Environment

Abstract

Indoor localization service is an indispensable part of modern intelligent life, among which Wi-Fi based fingerprint localization system is popular in indoor positioning researches due to its advantages of low cost and widely deployment. However, Wi-Fi based localization system is susceptible to dynamic environment, and fingerprint collection and updating are time-consuming and labor-intensive. To address this problem, we propose a novel positioning framework based on multiple transfer learning fusion using Generalized Policy Iteration (GPI). Firstly, a 1-Dimension Convolutional Autoencoder (1-D CAE) is designed to extract features from one-dimensional fingerprint data; similar to Convolutional Neural Network (CNN), it can not only pay more attention to the information of different dimensions of fingerprints, but also compress redundant information and reduce noise. After that, Domain Adversarial Neural Network (DANN) and Passive Aggressive (PA) algorithm are fused to train localization model based on unlabeled fingerprint of target domain using the theory of GPI in offline stage. Finally, the model is fine-tuned with unlabeled fingerprints and few labeled fingerprints in daily online predictions to improve the performance of the localization system. Various evaluations in five typical scenarios validate the effectiveness of proposed algorithm in dynamic environment, with low tendency, easy recalibration, long-term stabilization high accuracy and so on.

Seamless Fusion: Multi-Modal Localization for First Responders in Challenging Environments

“…These techniques generally require pre-deployed infrastructure or nodes, which can be a major constraint in disaster scenarios where the existing infrastructure may be damaged or entirely non-functional [7]. Furthermore, fingerprinting methods necessitate the prior knowledge of the environment through the collection of signal strength maps, a process that is not feasible in the dynamic or rapidly changing conditions typical of emergency situations [8]. The reliability of these techniques can also be severely compromised by environmental factors, such as interference, obstructions, and signal attenuation, which are often exacerbated in chaotic environments [9].…”