• Journal of Institute of Control, Robotics and Systems
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• v.21 no.4
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• pp.378-384
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• 2015

Indoor localization is becoming one of the most important technologies for smart mobile applications with different requirements from conventional outdoor location estimation algorithms. Fingerprinting location estimation techniques based on neural networks have gained increasing attention from academia due to their good generalization properties. In this paper, we propose a novel location estimation algorithm based on an ensemble of multiple neural networks. The neural network ensemble has drawn much attention in various areas where one neural network fails to resolve and classify the given data due to its' inaccuracy, incompleteness, and ambiguity. To the best of our knowledge, this work is the first to enhance the location estimation accuracy in indoor wireless environments based on a neural network ensemble using fingerprinting training data. To evaluate the effectiveness of our proposed location estimation method, we conduct the numerical experiments using the TGn channel model that was developed by the 802.11n task group for evaluating high capacity WLAN technologies in indoor environments with multiple transmit and multiple receive antennas. The numerical results show that the proposed method based on the NNE technique outperforms the conventional methods and achieves very accurate estimation results even in environments with a low number of APs.

• International Journal of Contents
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• v.10 no.4
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• pp.69-74
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• 2014

General location aware systems are only applied to indoor and outdoor environments using more than three transmitters to estimate a fixed object location. Those kinds of systems have environmental restrictions that require an already established infrastructure. To solve this problem, an Object Location Estimation (OLE) algorithm based on PTP (Point To Point) communication has been proposed. However, the problem with this method is that deduction of performance parameters is not enough and location estimation is very difficult because of unknown restriction conditions. From experimental tests in this research, we determined that the performance parameters for restriction conditions are a maximum transmission distance of CSS communication and an optimum moving distance interval between personal locations. In this paper, a system applied OLE algorithm based on PTP communication is implemented using a CSS (Chirp Spread Spectrum) communication module. A maximum transmission distance for CSS communication and an optimum moving distance interval between personal locations are then deducted and studied to estimate a fixed object location for generalization.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.8
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• pp.1934-1942
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• 2015

An estimation problem in the environment which GPS signals do not reach, should be solved by employing an indoor location estimation scheme. Location estimation schemes for indoor environments generally include the AOA, TOA, RSS, Fingerprint, and TDOA. For a ship environment where there exist many spaces enclosed by iron plates, the TDOA scheme is appropriate because location estimation is usually performed at a closed range. In this paper, we address the problem of estimating the location of a terminal under the ship environment. The problem of location estimation by using the TDOA is presented in detail, and then an algorithm for applying the estimation to the ship environment is proposed. Finally, the proposed algorithm of location estimation in a ship by the TDOA scheme is verified through simulations from three viewpoints.

• Journal of information and communication convergence engineering
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• v.10 no.3
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• pp.269-275
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• 2012

In this paper, we proposed a novel location estimation algorithm based on the concept of space-time signature matching in a moving target environment. In contrast to previous fingerprint-based approaches that rely on received signal strength (RSS) information only, the proposed algorithm uses angle, delay, and RSS information from the received signal to form a signature, which in turn is utilized for location estimation. We evaluated the performance of the proposed algorithm in terms of the average probability of error and the average error distance as a function of target movement. Simulation results confirmed the effectiveness of the proposed algorithm for location estimation even in moving target environment.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.1
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• pp.83-89
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• 2010

If an appliance perceives the location or health condition of a resident in the smart home, it can provide more intelligent service actively. That is, while the conventional appliance is operated by manual input of a resident, the location-based human adaptive appliance detects the resident's information such as location, activity pattern, or health condition by itself and provides the most suitable living condition for the resident autonomously. This paper presents the real-time location-based metabolic rate estimation method that measures the amount of physical activity (metabolic rate) for location-based human adaptive air-conditioner. And, the feasibility of the algorithm is evaluated experimentally on a test bed using the pyroelectric infrared sensor-based indoor location aware system (PILAS) that is a non-terminal-based location-aware system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.5A
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• pp.284-293
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• 2003

Location service is usually provided by the GPS method using satellites. In the next generation mobile communication systems which use smart antennas, location service can be accomplished using direction of arrival (DOA) estimation techniques. In this paper, we propose a DOA estimation technique for the location service of the next generation mobile communication systems and investigate the validity of the proposed technique through computer simulation. First, DOA estimation problems of distributed sources are considered using vortical and horizontal array processors which are orthogonal to each other. The DOA of the elevation angle is estimated by the vertical array processor and then that of the azimuth angle is estimated by the horizontal array processor. Finally, the procedures of the location service for specific signal sources using three smart antennas are exhibited by computer simulation to show that the proposed DOA estimation technique can be used for the location service in the next generation mobile communication systems.

• Journal of the Korea Institute of Military Science and Technology
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• v.7 no.4 s.19
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• pp.61-69
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• 2004

This paper presents a new analytical method for estimating the location of a target using directional data. Based on a nonlinear programming (NLP) problem formulated for the line method, which is a well known algorithm for two-dimensional location estimation, we present a method to find an optimal solution for the problem. Then we present a two-stage method for better location estimation based on the NLP problem. In addition, another two-stage method is presented for location estimation problems in which different types of observers are used to obtain directional data based on the analysis of the maximum likelihood estimate of the target location. The performance of the suggested method is evaluated through simulation experiments, and results show that the two-stage method is computationally efficient and highly accurate.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.1
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• pp.19-25
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• 2017

Recently, indoor LBS has been attracting much attention because of its promising prospect. One of key technologies for its success is indoor location estimation. A popular one for indoor positioning is to find the location based on the strength of received Wi-Fi signals. Since the Wi-Fi services are currently prevalent, it can perform indoor positioning without any further infrastructure. However, it is found that its accuracy depends heavily on the surrounding radio environment. To alleviate this difficulty, we present a novel indoor position technique employing the geomagnetic characteristics as well as Wi-Fi signals. The geomagnetic characteristic is known to vary according to the location. Therefore, employing the geomagnetic signal in addition to Wi-Fi signals is expected to improve the location estimation accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.1
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• pp.119-133
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• 2009

The purpose of this research is to accurately estimate the location of a device using the received signal strength indicator (RSSI) of IEEE 802.11 WLAN for location tracking in indoor environments. For the location estimation method, we adopted the calibration model. By applying the Adaptive Zone Based K-NNSS (AZ-NNSS) algorithm, which considers the velocity of devices, this paper presents a 9% improvement of accuracy compared to the existing K-NNSS-based research, with 37% of the K-NNSS computation load. The accuracy is further enhanced by using a Kalman filter; the improvement was about 24%. This research also shows the level of accuracy that can be achieved by replacing a subset of the calibration data with values computed by a numerical equation, and suggests a reasonable number of calibration points. In addition, we use both the mean error distance (MED) and hit ratio to evaluate the accuracy of location estimation, while avoiding a biased comparison.

• Journal of information and communication convergence engineering
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• v.14 no.4
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• pp.207-214
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• 2016

In this paper, we propose a radial reference map-based location fingerprinting technique with constant spacing from an access point (AP) to all reference points by considering the minimum dynamic range of the received signal strength indicator (RSSI) obtained through an experiment conducted in an indoor environment. Because the minimum dynamic range, 12 dBm, of the RSSI appeared every 20 cm during the training stage, a cell spacing of 80 cm was applied. Furthermore, by considering the minimum dynamic range of an RSSI in the location estimation stage, when an RSSI exceeding the cumulative average by ${\pm}6dBm$ was received, a previously estimated location was provided. We also compared the location estimation accuracy of the proposed method with that of a conventional fingerprinting technique that uses a grid reference map, and found that the average location estimation accuracy of the conventional method was 21.8%, whereas that of the proposed technique was 90.9%.