Cherntanomwong, Panarat
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Cherntanomwong, Panarat
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panarat.ch@kmitl.ac.th
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Item type:Publication, Synthesis of a Small Fingerprint Database through a Deep Generative Model for Indoor Localisation(2023-01-01); 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An Efficient Radio Map Construction Method Using Region-Aware Adaptive Ensemble Regression for Indoor Localization(2025-01-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Distance-based indoor localization system utilizing general path loss model and RSSI(2020-11-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Soft-clustering technique for fingerprint-based localization(2018-01-01); In this paper, the soft-clustering algorithm for the fingerprint-based localization technique is proposed. In an indoor environment, the fingerprint-based localization technique is usually employed since it can deal with signal fluctuation. Its basic principle is to find the target location by comparing its signal parameters with a previously recorded database of knownlocation- signal parameters. Here, the received signal strength indicator (RSSI) provided by the wireless sensor network (WSN) is used as the signal parameter. The high accuracy of location estimation requires a very fine spatial resolution of the database, corresponding to the time consumed for pattern matching. To reduce the calculation time, clustering can be applied because it can reduce the database size by grouping similar data in the same cluster. The accuracy of the algorithm to cluster the target location and fingerprint locations is the main concern. The result shows that the clustering technique used can successfully cluster the target sensing node into an appropriate cluster. This implies that, by using soft clustering with the fingerprint technique, the target location can be estimated faster than by using classical fingerprint techniques since the target location can be estimated within a small set of fingerprints in the cluster, not with all fingerprints in the database. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, TLB and WC-TLB-MM: The Improved Min-Max Algorithms for Multi Targets Indoor Localization(2023-01-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Fingerprint Database Enhancement by Applying Interpolation and Regression Techniques for IoT-based Indoor Localization(2020-01-01); Most applied indoor localization is based on distance and fingerprint techniques. The distance-based technique converts specific parameters to a distance, while the fingerprint technique stores parameters as the fingerprint database. The widely used Internet of Things (IoT) technologies, e.g., Wi-Fi and ZigBee, provide the localization parameters, i.e., received signal strength indicator (RSSI). The fingerprint technique advantages over the distance-based method as it straightforwardly uses the parameter and has better accuracy. However, the burden in database reconstruction in terms of complexity and cost is the disadvantage of this technique. Some solutions, i.e., interpolation, image-based method, machine learning (ML)-based, have been proposed to enhance the fingerprint methods. The limitations are complex and evaluated only in a single environment or simulation. This paper proposes applying classical interpolation and regression to create the synthetic fingerprint database using only a relatively sparse RSSI dataset. We use bilinear and polynomial interpolation and polynomial regression techniques to create the synthetic database and apply our methods to the 2D and 3D environments. We obtain an accuracy improvement of 0.2m for 2D and 0.13m for 3D by applying the synthetic database. Adding the synthetic database can tackle the sparsity issues, and the offline fingerprint database construction will be less burden. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Dynamic Ensemble Learning With Received Signal Strength Transformation for Robust Multi-Floor Wi-Fi Indoor Localization(2026-01-01); Joko Suroso, DwiReceived Signal Strength (RSS)-based Wi-Fi localization offers a cost-effective solution for multi-floor indoor location estimation. However, its accuracy is often degraded by signal fading, multipath propagation, and device heterogeneity, posing major challenges to reliable localization. Recent studies have increasingly employed deep neural networks due to their ability to extract meaningful patterns from RSS data; however, these models require substantial computational resources and extensive parameter tuning, which limits their adaptability across diverse dynamic environments. To address these limitations, we propose DELLoc-RT, a localization framework integrating Dynamic Ensemble Learning (DELLoc) with RSS Transformation (RT) for accurate, efficient, and adaptable multi-floor indoor localization. The RT module applies Sigmoid-scaled normalization and confidence weighting to convert RSS values into compact, learnable features. DELLoc employs multiple base learners optimized via the Tree-structured Parzen Estimator with a pruning strategy (TPE-PS) that accelerates convergence by focusing on promising configurations. Additionally, Iterative Ensemble Optimization with Stepwise Selection (IEO-SS) selects complementary learners to enhance overall performance. Experimental results demonstrate that DELLoc-RT achieves floor classification accuracies of 93.32%, 94.38%, and 94.02% on the UJIIndoorLoc, UTSIndoorLoc, and Tampere datasets, respectively, with mean Euclidean errors (MEE) of 10.63 m, 7.87 m, and 8.19 m. These results highlight the model’s strong adaptability across diverse datasets. Evaluation on a self-constructed dataset further confirms that DELLoc-RT delivers high accuracy and efficiency while substantially reducing the need for manual tuning, enabling rapid deployment in practical scenarios. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Indoor Device-free Localization Using Received Signal Strength Indicator and Illuminance Sensor for Random-forest-based Fingerprint Technique(2021-01-01); Indoor device-free localization (IDFL) offers more flexibility than conventional indoor localization (device-based) systems, as the targets or objects need not be equipped with any device to be located. In the process of IDFL, the target is passive, enabling applications such as monitoring of elderly people, security systems to detect intruders, and indoor navigation. Despite having more flexibility than device-based systems, IDFL is still inferior in terms of localization performance. The most commonly used technique for IDFL is the fingerprint technique, which uses the uniqueness of spatial information to predict the target's location. The spatial information is a fingerprint database containing information on locations and their corresponding parameters. The most specific parameter for the fingerprint database is the received signal strength indicator (RSSI). RSSI can be obtained directly from many low-cost devices, i.e., Wi-Fi-based devices, without the need to install additional hardware. The fingerprint technique is a two-phase process: The database is constructed in the offline phase, and a matching process to compare the target's current parameter with those in the database is performed in the online phase. We propose fingerprint-technique-based IDFL using RSSI and illumination from an illuminance sensor as the additional parameters of the fingerprint database. Both parameters are recorded by considering two scenarios: An empty room and a person standing in the fingerprint grids. The constructed database is the person-filled room subtracted from the empty room database. We use random forest, one of the machine learning (ML) algorithms, as the pattern-matching algorithm. We evaluate its performance by comparison with two other ML algorithms: K-nearest neighbor (k-NN) and neural networks (NN). The results show that k-NN has better accuracy than the random forest for learning and testing in terms of the root mean square error (RMSE). On the other hand, the random forest has better accuracy than NN and better precision than either k-NN or NN for learning and testing in terms of the standard deviation (STD). The results show the possibility of improving the IDFL performance by adding more parameters to the fingerprint database and using an ML-based pattern-matching algorithm. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Signal subspace interpolation from discrete measurement samples In constructing a database for location fingerprint technique(2009-01-01);