Progressive RSS Data Augmenter With Conditional Adversarial Networks

Abstract

Accuracies of most fingerprinting approaches for WiFi-based indoor localization applications are affected by the qualities of fingerprint databases, which are time-consuming and labor-intensive. Recently, many methods have been proposed to reduce the localization accuracy reliance on the qualities of the established fingerprint databases. However, studies on establishing fingerprint databases are relatively rare under the condition of sparse reference points. In this paper, we propose a novel data augmenter based on the adversarial networks to build fingerprint databases with sparse reference points. Additionally, two conditions of these networks are designed to generate data effectively and stably, which are 0-1 sketch and Gaussian sketch. Based on the networks, we design two augmenters with different cyclic training strategies to evaluate the augmenting effects comparatively. Meanwhile, five quantitative evaluation metrics of the augmenters are proposed from two perspectives of the artificial experiences and the data features, and some of them are also used as the gradient penalties for generators. Finally, experiments corresponding to these metrics and localization accuracies demonstrate that the data augmenter with the 0-1 sketch adversarial network is more efficient, effective and stable totally. INDEX TERMS CGAN, sketch, quantitative evaluation metrics, RSS.

Multi-Dimensional Wi-Fi Received Signal Strength Indicator Data Augmentation Based on Multi-Output Gaussian Process for Large-Scale Indoor Localization

“…DataLoc+ [27], on the other hand, uses the fingerprint data measured on a single floor of a hospital, which reflects many devices and the movement of people carrying them in the hospital. In the cases of the CAN [43], DL Approach [44], and Between-Location [45] methods, small-scale, proprietary, single-floor databases are used, where it would be easier to obtain the details of the internal building structure and choose the optimal locations of APs and RPs based on them for the improvement of the stability of radio maps; in these cases, the results presented in the papers cannot be reproduced by other researchers.…”

“…Type Notes Proposed High Multi-Building Multi-Floor MOGP s-GAN [26] Low Single-Floor GAN DataLoc+ [27] Low Single-Floor Dropout DL Augmentation [25] Low Single-Floor Deep Learning CAN [43] Low Single-Floor Conditional Adversarial Networks DL Approach [44] Low Single-Floor AlexNet Between-Location [45] Low Single-Floor Between-Class Learning As for the s-GAN [26], because it only provides the results of single-floor data augmentation and localization for Building 1 Floor 2 of the UJIIndoorLoc database, we also applied the proposed MOGP-based data augmentation for the same building and floor and obtained the 2D localization error using the hierarchical RNN for comparison, as summarized in Table 12. Unlike the proposed scheme, the s-GAN uses a GAN to generate augmented RSSI data, associates pseudo-labels with the generated data using semi-supervised learning, and filters out inappropriate augmented RSSI data before location estimation.…”

Machine Learning Based Indoor Localization Using Wi-Fi RSSI Fingerprints: An Overview

“…The authors of [139] demonstrate the feasibility of data augmentation using deep neural networks (DNNs), and the authors of [140] use data augmentation to reduce the number of required site surveys and improve location accuracy. In [141], the authors propose a novel data augmentation technique based on a conditional adversarial network to handle the sparsity of RPs.…”

Access-Point Centered Window-Based Radio-Map Generation Network

“…In turn, such approaches require increased time and heavy costs. To address this issue, several studies have been conducted on walking surveys [ 12 , 13 ], crowd sourcing [ 14 , 15 ], and data augmentation [ 16 , 17 ]. A walking survey [ 18 , 19 ] is the most common RSSI measurement approach, and it directly measures the receiver strength while moving along a predetermined path.…”