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Author: search.filters.author.Suroso, Dwi JokoSubject: search.filters.subject.Fingerprint technique

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    Fingerprint-based Indoor Localization via Deep Learning
    (2023-03-24)
    Suroso, Dwi Joko
    ;
    Cherntanomwong, Panarat
    ;
    Sooraksa, Pitikhate
    Deep learning (DL) application is proven helpful in a vast research field. One recent trend is to employ DL in radio frequency (RF)-based indoor localization. The fingerprint technique is the most used indoor localization technique known for its accuracy and performance. However, the fingerprint technique pays a high cost and effort in offline database construction, while its performance solely depends on the database density. Moreover, to apply deep learning, we also need a large dataset for it to learn efficiently. We propose to implement a DL-based fingerprint technique to tackle both problems of dataset scarcity and localization performance. We propose the DL's discriminative model, i.e., multilayer perceptron (MLP), for classification tasks. For the fingerprint database augmentation, we employed the generative model, i.e., Generative adversarial networks (GANs). We considered using a received signal strength indicator (RSSI) from a measurement campaign based on Wi-Fi devices for the database. The total area of interest is 25 m<sup>2</sup> inside the typical classroom environment, and we consider the 25 fingerprint locations as labels. We have a dataset of 1,250 rows x 8 columns (from 8 reference points). From the results, by using only 50% of actual data combined with the 125 synthetic data, we can improve the accuracy by more than 200% compared to only using 50% of actual data and show a 60% improvement in the loss. The combination of 100% actual data and 125 synthetic data gives the best accuracy and loss performance of 0.76 and 0.85, respectively. It gives an improvement of 144% in accuracy and 200% loss performance. By implementing deep learning for fingerprint techniques for data augmentation and classification, we can achieve good performance and reduce the workload of fingerprint database construction.
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    Synthesis of a Small Fingerprint Database through a Deep Generative Model for Indoor Localisation
    (2023-01-01)
    Suroso, Dwi Joko
    ;
    Cherntanomwong, Panarat
    ;
    Sooraksa, Pitikhate
    In deep learning (DL), the deep generative model is helpful for data augmentation objectives to tackle the lack of datasets that have a significant impact on learning performance. Data augmentation or synthesis is expected to solve the issue in a small/sparse database. The problem of databasing also exists in the fingerprint-based indoor localisation system. The dense offline fingerprint database must be constructed with the accuracy requirement. However, this will affect the high cost, massive laborious work, and increase the complexity of the system. Therefore, this paper proposes to address these issues by generating synthetic data via a deep generative model. The generative adversarial network (GAN) is selected to generate the synthetic fingerprint database for indoor localisation. Our database consideration consists of power-based parameters, i.e., the received signal strength indicator (RSSI) from Wi-Fi devices obtained from the actual measurement campaign. Some of the literature mainly discusses how GAN works in a vast and complex dataset. Here, we consider applying GAN in a relatively small dataset and for a simple setup. Our results show that by only using the 20 % fraction of actual RSSI data combined with the synthetic RSSI, the accuracy validation performance is slightly higher than when using all actual data usage. Moreover, in only 60 % of actual data usage and in combination with 625 samples of synthetic data, the accuracy performance is improved to 0.73 (1.37 times higher than the use of all actual data, 0.53). Thus, this result proves that the challenges of offline fingerprint databases can be alleviated by data synthesis through GAN by using only a small dataset.