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UWB Transmission Waveform Application for in an Indoor Localization System
(2024-01-01)
Mankong, Thanadon
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Promwong, Sathaporn
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Bunlaksananusorn, Chanin
Nowadays, the indoor localization systems had been widely utilized. The popular use of localization system is the global position system (GPS). However, the system fails in particular environments such as indoors because low received signal power. It is not appropriate to take a position. The indoor localization system was used to assist in finding a position in an indoor environment. There are several techniques used to evaluate. Technique trilateration model is a technique of geometric. This technique leverages circular intersections to estimate target positions. The circles are derived from a set of reference positions. This paper studies on indoor positioning in line of sight (LOS) using by IEEE 802.15.4 standard in ultra wideband option which this study employs trilateration to estimate the position of target objects. Biconical antennas with vertical polarization serve as both transmitting and receiving elements. The evaluation is conducted using a vector network analyzer (VNA) across a frequency range of 3GHz to 11GHz. The positioning accuracy will be assessed in terms of distance error.
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Deep Generative Model-based RSSI Synthesis for Indoor Localization
(2022-01-01)
Suroso, Dwi Joko
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Cherntanomwong, Panarat
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Sooraksa, Pitikhate
Indoor localization via deep learning (DL) is attracting researchers' attention. DL is mainly used for fingerprinting-based indoor localization as it generally employs a vast offline database to ensure its reliability. However, the long effort and high cost of constructing this database are the disadvantages of this technique. This paper implements variational autoencoders (VAE), one of the popular deep generative models, to alleviate the drawbacks of offline database issues. Our proposal works using the received signal strength indicator (RSSI); unfortunately, it is known for its fluctuation and instability. Thus, instead of using RSSI directly as a localization parameter, we learn its distribution via VAE to generate the synthetic RSSI values. We utilized the RSSI from an actual measurement campaign. The VAE implementation results show that we can obtain the RSSI synthesis by exploring the latent distribution learned from the input distribution. Thus, the offline database density grids can be enhanced. We validated the results by varying epochs to map the learned latent distribution. However, we still have relatively low accuracy in the synthetic RSSI values, especially when applying a small number of epochs, i.e., 10 and 100. When we applied epoch number 1000, the error was relatively low (-3dBm average error) in the sampled position. Our preliminary assumption is that the dataset is small for VAE learning, and probably the 3-by-3 RSSI-to-image size assumption could still be inadequate.
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Investigation of Wireless Body Area Network Localization Based on Measurement Data
(2022-01-01)
Promwong, Sathaporn
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Khongsomboon, Khamphong
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Chaisang, Aditep
Short-range wireless communication is important for application use in wireless medical monitoring systems and wireless body area network localizations. Therefore, the channel's characteristics and the human body effect must be studied. This paper will investigate the wireless body area network localization and ZigBee technology based on the IEEE 802.15.4. Moreover, model wireless body area network measurement using a vector network analyzer at the frequency range of 2.2 GHz to 2.6 GHz for measurement and recording. In the experiment result, the wireless body area network localization is analyzed and evaluated by considering the received signal strength and distance error shown in the cumulative distribution function based on the CLEAN algorithm. From experimental studies, this project is beneficial for development studies and essential information for future research studies of wireless body area network localization applications.
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Is Deep Diffusion Probabilistic Model Applicable for Fingerprint-based Indoor Localization?
(2022-01-01)
Suroso, Dwi Joko
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Sooraksa, Pitikhate
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Cherntanomwong, Panarat
The latest deep learning (DL) phenomenon is the Denoising Diffusion Model (DDM). DDM is in a class of latent variable models of the deep generative model (DGM) along with the big name of Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). Moreover, in a recent finding, DDM beats GANs in image synthesis. This paper presents the prospective applicability discussion of DDM for indoor localization research as previous models, e.g., GANs and VAEs, which are successfully implemented. Here, we focus more on how DDM can synthesize localization parameters with the help of fingerprinting technique's database enhancement. The fingerprint technique needs a preconstructed database which has the main drawbacks of its cost, time inefficient, and high complexity. We found valuable works of literature on this specific topic for GANs and VAEs. However, there are few DDM applications for discrete data types, and as the authors' concern, there is no attempt to apply them to indoor localization yet. DDM implementation is to generate continuous data domains, e.g., image, text, and audio data. A radio map or fingerprint database is essentially needed for fingerprint-based indoor localization. Learning this database pattern helps increase the system's performance. Obtaining a high-density and quality database is expensive and challenging to implement. Then, it raises a question, is DDM applicable for synthesizing this database and alleviating this problem?
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Fingerprint Database Enhancement using Spatial Interpolation for IoT-based Indoor Localization
(2022-01-01)
Martin Adiyatma, Farid Yuli
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Joko Suroso, Dwi
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Cherntanomwong, Panarat
The widespread adoption of the internet of things (IoT) drives indoor location-based service (ILBS) applications forward. The core parameter of ILBS is indoor localization. Generally, indoor localization is divided into two techniques, distance-based, i.e., triangulation, and distance-free, i.e., fingerprint technique. This paper discusses the fingerprint technique because of some advantages, i.e., higher accuracy performance compared to the distance-based technique. However, the fingerprint technique has drawbacks in offline database construction: extraordinarily time-consuming and labor-intensive, which hinders its application in the real world. Furthermore, the fingerprint database needs to be updated regularly in a dynamic environment. Therefore, we propose fingerprint database enhancement based on various spatial interpolations to tackle the issues of fingerprint database construction. We apply Inverse Distance Weighted (IDW), Quadratic Spline, Cubic Spline, and Ordinary Kriging Interpolation methods to generate the synthetic database. We have conducted a measurement campaign to obtain Received Signal Strength Indicator (RSSI) as the fingerprint-based localization parameter. From our results, the interpolation methods show that the generated synthetic RSSI can provide a lower prediction error. Our proposed methods can have similar accuracy performance compared to manual fingerprints using actual data. Moreover, the synthetic RSSI data has a 0 dBm error for the best prediction and not more than 6 dBm for the worst prediction. Thus, we conclude that our proposed methods can enhance the fingerprint database and have proven to increase localization performance.
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WLAN Localization Measurement and Analysis Using RSS and TOA Positioning Methods
(2018-07-02)
Lorvannger, Khounhack
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Lakanchanh, Donekeo
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Tiengthong, Thanadol
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Promwong, Sathaporn
Wireless local area network (WLAN) has been exclusively research due to its widely used applications. Its operate frequency spectrum is in the ISM band and regulated in IEEE 802.11 family. Localization is one of the application which able to used WLAN signal. Its can be used in various applications such as logistics, security, disaster rescue operation tracking, and military service. Since the various kind of its applications is work in indoor environment, the indoor localization requires high accuracy. The receive signal strength (RSS) and time of arrival (TOA) are a parameter that used in the comparison between min-max technique and trilateration technique in this paper. In the measurement of this research, the frequency bandwidth is ranging from 2.3 GHz to 2.5 GHz, an access point is used to represent both of transmit antenna and receive antenna. The cumulative distribution function (CDF) is used for study the most accuracy.
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Performance comparison between UWB and NB propagation models for an indoor localization
(2015-04-22)
Vinicchayakul, Wipassorn
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Promwong, Sathaporn
Nowadays, an indoor localization has been widely employed in some applications such as finding itself in a department store. Nevertheless, general indoor localization has the moderate effectiveness which some applications require high accuracy to use in emergency situations. Thus, the indoor localization has been improved in many ways. Normally, most methods are acceptable to use in finding a location within the building such as WLAN with the fingerprinting technique. Therefore, the fingerprinting technique was used in this paper. Then, narrowband (NB) and ultra wideband (UWB) signals were compared in order to show about the efficiency in the indoor localization. The channel model was used from VNA at the frequency range 3 GHz to 11 GHz. As the result, the best result of UWB localization modeling has yield as 90.84% at 1 meter accuracy value in 3 rooms whereas the best result of WLAN localization modeling has yield as 36.64% at 3 meters accuracy value in 3 rooms. Then, UWB signal is more suitable than NB signal.
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Mixed K-means and GA-based weighted distance fingerprint algorithm for indoor localization system
(2015-01-26)
Sunantasaengtong, Panya
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Chivapreecha, Sorawat
This paper proposes an application of Wireless Sensor Network (WSN) for indoor localization using IEEE 802.15.4 standard. Proposed algorithm applies K-means clustering and Genetic Algorithm (GA) as engine to prepare offline information which result in increasing accuracy and decreasing computational cost of fingerprint technique for indoor localization. K-means clustering will be applied to cluster received signal strength indicator (RSSI) vector into several classes for coarse positioning estimation. Consequently, GA will be applied to search the optimal weights for each reference sensor and used in order to obtain more accuracy for positioning estimation. Experiments are conducted in indoor environment using zigbee sensor network and the proposed algorithm can be compared with K-Nearest Neighbor (KNN) algorithm and conventional weighted distant fingerprint (WDF) algorithm. Results demonstrate that the proposed algorithm can improve an accuracy increase to 87.56 % for identifying correctly 1.5 m × 1.5 m area of target node and also decrease computational cost of 67.60 %.
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Evaluation of antennas performance with FCC and common spectral mask for UWB localization
(2015-01-26)
Kingsakda, Vongkeo
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Satahack, Phonepaseuth
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Mingmanee, Sirapop
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Promwong, Sathaporn
Antennas which are used to transmit and receive ultra wideband impulse radio (UWB-IR) signals should be able to accomodate its large bandwidth. Moreover, the signals should not be distorted too much when they pass through the antennas. Therefore, the transfer function of antennas should be known the performance. The indoor and the outdoor localization are usually evaluated by using the extension of Friis' transmission formula. However, it is not directly application to UWB transmission systems. This paper are presentation, the characterization of UWB antennas performance that takes into account the transmission signal waveform, its distortion due to the antennas, and the receiver. Since the antennas are significant pulse-shaping filters in UWB, various kinds of antennas are experimentally examined, especially focusing on the UWB template waveform.

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