• Journal of Advanced Marine Engineering and Technology
• /
• v.40 no.9
• /
• pp.836-841
• /
• 2016

An indoor positioning system using Wi-Fi is essential to produce a radio map that combines the indoor space of two or more dimensions, the information of node positions, and etc. in processing for constructing the radio map, the measurement of the received signal strength indicator(RSSI) and the confirmation of node placement information counsume substantial time. Especially, when the installed wireless environment is changed or a new space is created, easy installation of the node and fast indoor radio mapping are needed to provide indoor location-based services. In this paper, to reduce the time consumption, we propose an algorithm to distinguish the straight and curve lines of a corridor section by RSSI visualization and Sobel filter-based edge detection that enable accurate node deployment and space analysis using cable-type Wi-Fi node installed at a 3 m interval. Because the cable type Wi-Fi is connected by a same power line, it has an advantage that the installation order of nodes at regular intervals could be confirmed accurately. To be able to analyze specific sections in space based on this advantage, the distribution of the signal was confirmed and analyzed by Sobel filter based edge detection and total RSSI distribution(TRD) computation through a visualization process based on the measured RSSI. As a result to compare the raw data with the performance of the proposed algorithm, the signal intensity of proposed algorithm is improved by 13.73 % in the curve section. Besides, the characteristics of the straight and the curve line were enhanced as the signal intensity of the straight line decreased by an average of 34.16 %.

• Journal of Internet of Things and Convergence
• /
• v.9 no.2
• /
• pp.71-76
• /
• 2023

In this paper, we propose a smoothed received signal strength indicator (RSSI)-based distance estimation using deep neural network (DNN) for accurate distance estimation in an environment where a single receiver is used. The proposed scheme performs a data preprocessing consisting of data splitting, missing value imputation, and smoothing steps to improve distance estimation accuracy, thereby deriving the smoothed RSSI values. The derived smoothed RSSI values are used as input data of the Multi-Input Single-Output (MISO) DNN model, and are finally returned as an estimated distance in the output layer through input layer and hidden layer. To verify the superiority of the proposed scheme, we compared the performance of the proposed scheme with that of the linear regression-based distance estimation scheme. As a result, the proposed scheme showed 29.09% higher distance estimation accuracy than the linear regression-based distance estimation scheme.

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.48 no.12
• /
• pp.28-34
• /
• 2011

In this paper, a PLL frequency synthesizer for RSSI applications is designed by phase noise analysis. Required synthesizer performance is achieved by optimizing the noise performance of PLL components and a loop transfer function, since its phase noise, lock time, and spur suppression capability are determined by the performance of loop components and loop filter characteristics. As an application example, a PLL frequency synthesizer for RSSI applications, which operates at the frequency of 2.288GHz, is designed using the phase noise analysis. The validity of the design technique is proved by experiments.

• ETRI Journal
• /
• v.40 no.1
• /
• pp.122-132
• /
• 2018

This paper proposes a hierarchical dual filtering (HDF) algorithm to estimate the spatial region between a Cloud of Things (CoT) gateway and an Internet of Things (IoT) device. The accuracy of the spatial region estimation is important for autonomous CoT clustering. We conduct spatial region estimation using a hidden Markov model (HMM) with a raw Bluetooth received signal strength indicator (RSSI). However, the accuracy of the region estimation using the validation data is only 53.8%. To increase the accuracy of the spatial region estimation, the HDF algorithm removes the high-frequency signals hierarchically, and alters the parameters according to whether the IoT device moves. The accuracy of spatial region estimation using a raw RSSI, Kalman filter, and HDF are compared to evaluate the effectiveness of the HDF algorithm. The success rate and root mean square error (RMSE) of all regions are 0.538, 0.622, and 0.75, and 0.997, 0.812, and 0.5 when raw RSSI, a Kalman filter, and HDF are used, respectively. The HDF algorithm attains the best results in terms of the success rate and RMSE of spatial region estimation using HMM.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.58 no.10
• /
• pp.2047-2051
• /
• 2009

In this paper, we propose the localization method on WSN(Wireless Sensor Network) using fuzzy model and Kalman filter. The proposed method is as follows: First, we estimate the distance of RSSI(Receive Signal Strength Index) by using fuzzy model in order to minimize the distance error. Second, we use a triangulation measurement for estimating the localization. And then, we minimize the localization error using a Kalman filter. Finally, we show the effectiveness and feasibility of the proposed method through some experiments.

• ETRI Journal
• /
• v.34 no.4
• /
• pp.625-628
• /
• 2012

In this letter, a novel method for localizing a user in a smart home environment is presented. We propose a two-level structure, in which the first level determines an occupant's location in the block level using RSSI in a ZigBee network, while the second level accurately estimates the occupant's location using a particle filter to handle the variations in the signal strength measurement. We devise an experimental setup with people performing significant tasks in the smart home. The results obtained from the testbed indicate that the proposed model leads to an improvement in the mean distance error.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.27 no.10
• /
• pp.909-916
• /
• 2016

The beacon receiver is an equipment which detects and measures the signal strength of transmitting satellite beacon signal. Beacon signals transmitted by satellites are low power continuous wave(CW) signals without any modulation intended for antenna steering to satellite direction and power control purposes on the earth. The beacon signal detection method using a very narrow band analog filter and RSSI(Received Signal Strength Intensity) has been typically used. However, it requires the implementation to track the frequency at the beacon receiver, thus a beacon frequency variation of the satellite due to temperature changes and long-term operation. Therefore, in this paper, the beacon signal detection receiver is designed by using a very narrow band digital filter array for a faster acquisition and SNR(Signal to Noise Ratio) method detection. For this purpose, by calculating the satellite link budget with the rain attenuation between satellite and ground station, and then extracting the received $C/N_o$ of the beacon signal, this work derives the bandwidth and the array number of the configured digital filter that gives the required C/N.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.12
• /
• pp.107-116
• /
• 2013

We proposed the WSN monitoring system applied the augmented reality to visualize effectively an indoor WSN. To implement system, we used wireless sensor network, indoor location determination, location-based augmented reality technology. First, we composed the wireless sensor networks indoors and implement web server and then get data from server DB using Android phones. Then, we obtained the (x, y) coordinates using the triangulation method from RSSI of three point of the strongest signal strength of the AP's. Also, we adjusted coordinates using the Kalman filter. Finally, we inserted the adjusted coordinates to the latitude and the longitude of the Mixare that use the GPS signal, and we got location of user and wireless sensor in the server DB. After that, we implemented augmented reality system using the android phone and wireless sensor location and data and real life image.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.9 no.4
• /
• pp.1012-1017
• /
• 2008

Recently, the use of wireless fan is increased by the development of wireless communication and convenience. Moreover, it makes an issue of security threat and vulnerability of wireless tan. Therefore, the IEEE established new standard such as 802.11i in 802.11 to supplement security vulnerability of wireless tan. But the security threat that does not solve, still remains. In this paper, we proposed that the location detection algorithm, that is used Kalman-Filter, Lateration and RSSI, and the mechanism that detects security status of AP and unauthorized AP by using beacon-frame of AP in building. Finally, we confirmed performance of proposed algorithm is good in comparison of established algorithm.

• Journal of Advanced Navigation Technology
• /
• v.19 no.6
• /
• pp.564-573
• /
• 2015

GPS is the representative positioning technology for providing the location information. This technique has the disadvantage that does not operate in the shadow areas, such as urban or dense forest and the interior. This paper proposes a hybrid indoor positioning algorithm, which estimates a more accurate location of the terminal using strength of the Wi-Fi signal from the indoor AP. To determine the location of the user, we establish the most appropriate path loss model for the measurement environment. by using the RSSI value measured in a variety of environment such as building structure, person, distance, etc. The path loss exponent obtained by the path loss model is changed according to the environment. REKF, PF estimate the position of the terminal by using measured value from the AP with path loss exponent. For more accurate position estimation, we select positioning system by the value of threshold measured by experiments rather than a single positioning system. Experimental results using the proposed hybrid algorithm show that the performance is improved by about 17% than the conventional single positioning method.