• Aerospace Engineering and Technology
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• v.6 no.1
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• pp.237-244
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• 2007

Estimation of target position is one of the main functions of surveillance UAVs, and is being used to various purposes but generally noisy target position is estimated due to the existence of random measurement errors. In this report, a method of diminishing target position estimation error by calculating target position using Kalman Filtered optimum values such as position, attitude of UAV and sight vector of optical instrument, is proposed.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.3
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• pp.28-39
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• 2010

In this paper, a new method to estimate MU (motor unit) location in the short head of BIC (biceps brachii) muscle using surface EMG (electromyogram) is proposed. The SMUAP (single motor unit action potential) is generated from a MU located at certain depth from the skin surface. The depth is referred as MU location. For estimating muscle force precisely, the information of the MU location is required. The reference SMUAPs are simulated based on anatomical structure of human muscle, and compared with acquired real EMG signals using 3-channel surface EMG electrode. The proposed method was compared with the results of previous researchers and verified its accuracy by computer simulation. From the simulation result in case of the MU located in 8[mm], the average estimation error of proposed method was 0.01[mm]. But the average estimation error of Roeleveld's method was 2.33[mm] and Akazawa's method was 1.70[mm]. Therefore the proposed method was more accurate than the methods of previous researchers.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10a
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• pp.310-312
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• 2001

샘플링되지 않은 불확실한 이동객체의 위치값을 추정하기 위한 기존의 연구방범 중 가장 보편적으로 사용하고 있는 방법은 선형 보간법이다. 선형 보간법을 사용할 경우 샘플링 구간은 좁게하여 오차를 줄일 수 있고 계산 시간을 단축할 수 있지만, 연속적인 이동객체의 경로는 직선이라기 보다는 곡선으로 나타내어지므로 샘플링되지 않은 이동객체의 위치값에 대해 불확실한 위치정보를 사용자에게 반환하게 된다. 따라서 이 논문에서는 샘플링된 이동객체의 위치값에 오차가 없다는 가정하에서 모든 위치점을 지나는 보간 다항식을 구해서 처리하는 선형 보간법 대신 이동객체의 위치값 자체의 오차범위까지 고려하는 다항회귀모형(polynomial regression model)을 이용한 이동객체의 불확실한 이동위치 추정방법을 제시한다. 다항회지모형은 이용할 경우 선형 보간법 보다 추정된 위치값에 대한 오차를 줄일 수 있으며, 이동객체의 과거 및 미래 위치값을 사용자에게 반환해 줄 수 있는 장점을 가진다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.8
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• pp.1789-1798
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• 2012

IEEE 802.15.4a standard can provide a variety of location-based services for ZigBee or wireless network applications by adapting the time-of-arrival (TOA) ranging technique. The non-line-of-sight (NLOS) condition is the critical problem in the IEEE 802.15.4a networks, and it can significantly degrade the performance of the TOA-based localization. To enhance the location accuracy due to the NLOS problem, this paper proposes an energy-efficient low complexity localization algorithm. The proposed approach performs the ranging with the multicast method, which can reduce the message overhead due to packet exchanges. By limiting the search region for the location of the node, the proposed approach can enhance the location accuracy. Experimental results show that the proposed algorithm outperforms previous algorithms in terms of the energy consumption and the localization accuracy.

• Journal of Korea Multimedia Society
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• v.9 no.2
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• pp.172-188
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• 2006

Sensor localization is one of the fundamental problems in wireless sensor networks. Previous localization algorithms can be classified into two categories, the GGB (Global Geometry-Based) approaches and the LGB (Local Geometry-Based). In the GGB approaches, there are a fixed set of reference nodes of which the coordinates are pre-determined. Other nodes determine their positions based on the distances from the fixed reference nodes. In the LGB approaches, meanwhile, the reference node set is not fixed, but grows up dynamically. Most GGB algorithms assume that the nodes are deployed in a convex shape area. They fail if either nodes are in a concave shape area or there are obstacles that block the communications between nodes. Meanwhile, the LGB approach is vulnerable to the errors in the distance estimations. In this paper, we propose new localization algorithms to cope with those two limits. The key technique employed in our algorithms is to determine, in a fully distributed fashion, if a node is in the line-of-sight from another. Based on the technique, we present two localization algorithms, one for anchor-based, another for anchor-free localization, and compare them with the previous algorithms.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.863-865
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• 2015

A method for estimating position is AOA, TOA, TDOA, Wi-Fi, Beacon etc. A method for estimating the location in indoor environment is used mainly Wi-Fi, Beacon. The reason is that AOA, TOA and TDOA are unfit to estimate position in indoor environment. To address this problem, this paper presents a AOA algorithm based on AP having a four directional antenna. The algorithm uses only the angle received from the four antennas. This can draw linear equations for signal. And calculate the intersections of the lines. Intersections means the position of user.

• Journal of KIISE:Information Networking
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• v.37 no.6
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• pp.431-443
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• 2010

In this paper, we propose a range-free localization method that can improve the estimation accuracy of Approximate Point in Triangle(APIT), which is the representative localization method for low cost wireless sensor networks. Specifically, we propose extended-APIT(e-APIT) method, which minimizes the error in deciding whether an object is in an area formed by three beacons. We also propose a way to improve the localization by narrowing down the potential localization area using the signals from neighboring beacons. According to the simulation performed, the proposed e-APIT method demonstrated noticeable accuracy improvement over the conventional APIT method.

• The Journal of the Acoustical Society of Korea
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• v.42 no.6
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• pp.529-535
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• 2023

Noise originating from incipient propeller cavitation is assumed to come from a limited number of sources emitting a broadband signal. Conventional methods for cavitation localization have limitations because they cannot distinguish adjacent sound sources effectively due to low accuracy and resolution. On the other hand, sparse Bayesian learning technique demonstrates high-resolution restoration performance for sparse signals and offers greater resolution compared to conventional cavitation localization methods. In this paper, an incipient propeller cavitation localization method using sparse Bayesian learning is proposed and shown to be superior to the conventional method in terms of accuracy and resolution through experimental data from a model ship.

• 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 the Institute of Convergence Signal Processing
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• v.9 no.2
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• pp.164-172
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• 2008

This paper presents the efficient localization of a mobile robot traveling on the floor with tags installed, using the spatial and temporal information acquired from passive RFID environment. Compared to previous research, the proposed localization method can reduce the position estimation error and also cut down the initial cost tag installation cost. Basically, it is assumed that a mobile robot is traveling over a series of straight line segments, each at a certain constant velocity, and that the number of tags sensed by a mobile robot at each sampling instant is at most one. First, the velocity and position estimation of a mobile robot starting from a known position, which is valid for all segments except the first one. Second, for the first segment in which the starting position is unknown, the velocity and position estimation is made possible by enforcing a mobile robot to traverse at least two tags at a constant velocity with the steering angle unchanged. Third, through experiments using our passive RFID localization system, the validity and performance of the mobile robot localization proposed in this paper is demonstrated.