• 제어로봇시스템학회논문지
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• 제12궈1호
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• pp.47-53
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• 2006

Localization is a essential technology for mobile robot to work well. Until now expensive sensors such as laser sensors have been used for mobile robot localization. We suggest RFID tag based localization system. RFID tag devices, antennas and tags are cheap and will be cheaper in the future. The RFID tag system is one of the most important elements in the ubiquitous system and RFID tag will be attached to all sorts of goods. Then, we can use this tags for mobile robot localization without additional costs. So, in this paper, the smart floor using passive RFID tags is proposed and, passive RFID tags are mainly used for identifying mobile robot's location and pose in the smart floor. We discuss a number of challenges related to this approach, such as tag distribution (density and structure), typing and clustering. When a mobile robot localizes in this smart floor, the localization error mainly results from the sensing range of the RFID reader, because the reader just ran know whether a tag is in the sensing range of the sensor. So, in this paper, we suggest two algorithms to reduce this error. We apply the particle filter based Monte Carlo localization algorithm to reduce the localization error. And with simulations and experiments, we show the possibility of our particle filter based Monte Carlo localization in the RFID tag based localization system.

• 로봇학회논문지
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• 제16권4호
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• pp.306-312
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• 2021

This paper describes localization of autonomous underwater vehicles(AUV), which can be used when some navigation sensor data are an outlier. In that situation, localization through existing navigation algorithms causes problems in long-range localization. Even if an outlier sensor data occurs once, problems of localization will continue. Also, if outlier sensor data is related to azimuth (direction of AUV), it causes bigger problems. Therefore, a parallel localization module, in which different algorithms are performed in a normal and abnormal situation should be designed. Before designing a parallel localization module, it is necessary to study an effective method in the abnormal situation. So, we propose a localization method through machine learning. For this method, a learning model consists of only Fully-Connected and trains through randomly contaminated real sea data. The ground truth of training is displacement between subsequent GPS data. As a result, average error in localization through the learning model is 0.4 times smaller than the average error in localization through the existing navigation algorithm. Through this result, we conclude that it is suitable for a component of the parallel localization module.

• Journal of Communications and Networks
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• 제7권4호
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• pp.429-437
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• 2005

Sensor localization has become an essential requirement for realistic applications over wireless sensor networks (WSN). In this paper we propose an ad hoc localization algorithm that is infrastructure-free, anchor-free, and computationally efficient with reduced communication. A novel combination of distance and direction estimation technique is introduced to detect and estimate ranges between neighbors. Using this information we construct unidirectional coordinate systems to avoid the reflection ambiguity. We then compute node positions using a transformation matrix [T], which reduces the computational complexity of the localization algorithm while computing positions relative to the fixed coordinate system. Simulation results have shown that at a node degree of 9 we get $90\%$ localization with $20\%$ average localization error without using any error refining schemes.

• 전자공학회논문지
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• 제53권2호
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• pp.133-140
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• 2016

해안 수중 감시를 위하여 분산센서망를 해안에 설치하고, 이를 이용하여 표적을 탐지하고 표적의 위치를 추정하는 연구가 많이 이루어지고 있다. Zhou 등은 표적 탐지만 가능한 간단한 구조의 센서들로 구성된 분산센서망에서 표적을 탐지한 센서들의 위치 정보를 활용하여 표적의 위치를 추정하는 기법을 제안하였다. Zhou 등이 제안한 기법은 다른 기존의 기법에 비해 표적탐지 신호의 전파모델에 대한 파라미터들을 별도로 추정할 필요가 없고, 연산량이 적으며, 분산센서망에서 적은 량의 데이터만 송수신하여도 된다. 그러나 Zhou 기법은 표적의 위치 추정오차가 크다. Ryu는 추정오차를 줄이기 위하여 Zhou 기법을 수정하였다. 수정된 Zhou 기법은 Zhou 기법보다 추정성능이 향상되었지만, 여전히 비교적 큰 추정오차를 가지고 있다. 본 논문에서는 수정된 Zhou 기법으로 구한 표적의 방위각을 나타내는 직선과 표적을 탐지한 센서들과의 기하학적 구조를 고려한 표적위치 추정기법을 제안하였으며, 수정된 Zhou 기법에 기반을 두고 있다. 제안한 기법의 표적위치 추정성능이 Zhou 기법과 수정된 Zhou 기법 보다 향상되었음을 컴퓨터 시뮬레이션을 통하여 확인하였다.

• Journal of Positioning, Navigation, and Timing
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• 제9권3호
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• pp.221-227
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• 2020

In this paper, the Iterated Consider Extended Kalman Filter (ICEKF) is proposed for bearing-only localization of GNSS interference to improve the estimation performance and filter consistency. The ICEKF is an extended version of Consider KF (CKF) for Iterated EKF (IEKF) to consider an effect of bearing measurement bias error to filter covariance. The ICEKF can mitigate the EKF divergence problem which can occur when linearizing the nonlinear bearing measurement by a large initial state error. Also, it can mitigate filter inconsistency problem of EKF and IEKF which can occur when a weakly observable bearing measurement bias error state is not included in filter state vector. The simulation result shows that the localization error of the ICEKF is smaller than the EKF and IEKF, and the Root Mean Square (RMS) estimation error of ICEKF matches the covariance of filter.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권4호
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• pp.670-691
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• 2013

In RSSI-based RFID(Radio Frequency IDentification) indoor localization system, the signal path loss model of each sub-region is different from others in the whole localization area due to the influence of the multi-path phenomenon and other environmental factors. Therefore, this paper divides the localization area into many sub-regions and constructs separately the signal path loss model of each sub-region. Then an improved LANDMARC method is proposed. Firstly, the deployment principle of RFID readers and tags is presented for constructing localization sub-region. Secondly, the virtual reference tags are introduced to create a virtual signal strength space with RFID readers and real reference tags in every sub-region. Lastly, k nearest neighbor (KNN) algorithm is used to locate the target object and an error compensating algorithm is proposed for correcting localization result. The results in real application show that the new method enhances the positioning accuracy to 18.2% and reduces the time cost to 30% of the original LANDMARC method without additional tags and readers.

• 전기학회논문지
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• 제59권10호
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• pp.1874-1881
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• 2010

A recursive linear stochastic error compensation algorithm is newly proposed for target localization in sensor network which provides range difference of arrival(RDOA) measurements. Target localization with RDOA is a well-known nonlinear estimation problem. Since it can not solve with a closed-form solution, the numerical methods sensitive to initial guess are often used before. As an alternative solution, a pseudo-linear estimation scheme has been used but the auto-correlation of measurement noise still causes unacceptable estimation errors under low SNR conditions. To overcome these problems, a stochastic error compensation method is applied for the target localization problem under the assumption that a priori stochastic information of RDOA measurement noise is available. Apart from the existing methods, the proposed linear target localization scheme can recursively compute the target position estimate which converges to true position in probability. In addition, it is remarked that the suggested algorithm has a structural reconciliation with the existing one such as linear correction least squares(LCLS) estimator. Through the computer simulations, it is demonstrated that the proposed method shows better performance than the LCLS method and guarantees fast and reliable convergence characteristic compared to the nonlinear method.

• 전자공학회논문지
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• 제51권7호
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• pp.9-17
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• 2014

본 논문은 무선 센서 네트워크의 분산 분포되어 있는 센서 노드들의 측위를 위해 Binary Particle Swarm Optimization (BPSO) 알고리즘을 제안한다. 자신의 위치를 모르는 센서 노드들은 셋 이상의 인접한 앵커, 즉, 위치를 알고 있는 노드들로부터의 거리를 측정하여 측위를 수행한다. 이러한 과정이 반복하는 동안 측위를 수행한 센서 노드들은 나머지 노드들에 대하여 또 다른 앵커 역할을 수행한다. 성능 평가를 위해 기존의 PSO 알고리즘에 대비하여, BPSO를 이용한 측위 오류 및 계산 시간 성능을 매트랩 시뮬레이션을 통해 비교 분석하였다. 시뮬레이션 결과 PSO 기반의 측위가 상대적으로 더 정확한 결과를 보여준다. 대조적으로, BPSO 알고리즘은 분산되어 있는 센서 노드들의 위치 측위를 더 빠르게 수행한다. 추가적으로, 전송 범위와 초기 앵커 노드들의 수가 측위 성능에 미치는 영향에 대한 분석을 수행한다.

• 한국정보과학회논문지:정보통신
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• 제36권1호
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• pp.24-33
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• 2009

패치-스티치(patch-and-stitch) 기법을 사용하는 위치추정 알고리즘에서 발생하는 플립오류는 두 패치를 하나의 좌표계로 통합하는 과정에서 패치가 잘못 뒤집혀 병합되는 경우에 발생한다. 본 논문은 플립오류의 발생을 억제하는 앵커프리(anchor free) 패치-스티치 위치추정 알고리즘을 제안한다. 제안하는 알고리즘은 두 단계를 거쳐서 플립 오류의 가능성을 제거한다. 첫째, 각각의 인접한 패치 쌍에 대해서 플립모호성(flip ambiguity) 검사를 통해 플립오류의 발생가능성이 높은 패치 쌍을 찾아낸다. 둘째, 전역적인 수준에서 플립충돌(flip conflict) 검사를 통해 플립 오류의 가능성이 높은 패치 쌍을 찾아낸다. 시뮬레이션을 통한 성능 평가는 제안하는 알고리즘이 기존 것에 비해 더 우수한 위치추정이 가능함을 보여준다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.597-599
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• 2005

To make the autonomous mobile robot move in the unknown space, we have to know the information of current location of the robot. So far, the location information that was obtained using Encoder always includes Dead Reckoning Error, which is accumulated continuously and gets bigger as the distance of movement increases. In this paper, we analyse the effect of the size of the two wheels of the mobile robot and the wheel track of them among the factors of Dead Reckoning Error. And after this, we compensate this Dead Reckoning Error by Kalman filter using Gyro Sensors. To accomplish this, we develop the controller to analyse the error components of Gyro Sensor and to minimize the error values. We employ the numerical approach to analyse the error components by linearizing them because each error component is nonlinear. And we compare the improved result through simulation.