• Journal of Information Processing Systems
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• v.16 no.4
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• pp.774-783
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• 2020

For the issue of subsurface target localization by reverse projection, a new approach of target localization with different distances based on symmetric sub-array multiple-input multiple-output (MIMO) radar is proposed in this paper. By utilizing the particularity of structure of the two symmetric sub-arrays, the received signals are jointly reconstructed to eliminate the distance information from the steering vectors. The distance-independent direction of arrival (DOA) estimates are acquired, and the localizations of subsurface targets with different distances are realized by reverse projection. According to the localization mechanism and application characteristics of the proposed algorithm, the grid zooming method based on spatial segmentation is used to optimize the locaiton efficiency. Simulation results demonstrate the effectiveness of the proposed localization method and optimization scheme.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.36.5-36
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• 2001

Localization is one of the key problems in the navigation of autonomous mobile robots. The probabilistic Markov localization approaches offer a good mathematical framework to deal with the uncertainty of environment and sensor readings but their use for realtime applications is limited by their computational complexity. This paper aims to reduce the high computational cost associated with the probabilistic Markov localization algorithm. We propose a hybrid landmark-based localization method combining triangulation and probabilistic approaches, which can efficiently update position probability grid, while the probabilistic framework allows to make use of any available sensor data to refine robot´s belief about its current location. The simulation results show the effectiveness and robustness of the method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.277-290
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• 2019

The demand for indoor positioning is increasing day by day. However, the widely used positioning methods today cannot satisfy the requirements of the indoor environment in terms of the positioning accuracy and deployment cost. In the existing research domain, the localization algorithm based on three-dimensional space is less accurate, and its robustness is not high. Visible light communication technology (VLC) combines lighting and positioning to reduce the cost of equipment deployment and improve the positioning accuracy. Further, it has become a popular research topic for telecommunication and positioning in the indoor environment. This paper proposes a surface centroid TOA localization algorithm based on the VLC system. The algorithm uses the multiple solutions estimated by the trilateration method to form the intersecting planes of the spheres. Then, it centers the centroid of the surface area as the position of the unknown node. Simulation results show that compared with the traditional TOA positioning algorithm, the average positioning error of the surface centroid TOA algorithm is reduced by 0.3243 cm and the positioning accuracy is improved by 45%. Therefore, the proposed algorithm has better positioning accuracy than the traditional TOA positioning algorithm, and has certain application value.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.6
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• pp.727-732
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• 2009

In this paper, the dependence of the Kalman filter-based emitter location algorithm on the initial estimate is investigated. Given all the LOB data, the initial estimate of the emitter location is obtained from the linear LSE algorithm with the former LOB data. Using the initial estimate, the Kalman filter algorithm is applied with the remaining LOB data to update the initial estimate. It is shown that as the number of data used in the calculation of the initial estimate increases, the accuracy of the final estimate is improved and the total computational complexity of obtaining the initial estimate and the final estimate increases. In addition, the dependence of the performance of the Kalman filter algorithm on the predefined constant is illustrated.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.8
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• pp.745-751
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• 2008

This paper presents a framework for GPS/INS/Vision based navigation system of helicopters. GPS/INS coupled algorithm has weak points such as GPS blockage and jamming, while the helicopter is a speedy and high dynamical vehicle amenable to lose the GPS signal. In case of the vision sensor, it is not affected by signal jamming and also navigation error is not accumulated. So, we have implemented an GPS/INS/Vision aided navigation system providing the robust localization suitable for helicopters operating in various environments. The core algorithm is the vision based simultaneous localization and mapping (SLAM) technique. For the verification of the SLAM algorithm, we performed flight tests. From the tests, we confirm the developed system is robust enough under the GPS blockage. The system design, software algorithm, and flight test results are described.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.12
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• pp.1246-1253
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• 2009

To be high efficient for a navigation of unmanned ground vehicle, it must be able to distinguish between safe and hazardous regions in its immediate environment. We present an advanced method using laser range finder for building global 2D digital maps that include environment information. Laser range finder is used for mapping of obstacles and driving environment in the 2D laser plane. Rotary encoders are used for localization of UGV. The main contributions of this research are the development of an algorithm for global 2D map building and it will turn a UGV navigation based on map matching into a possibility. In this paper, a map building algorithm will be introduced and an assessment of algorithm reliability is judged at an each environment.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.3
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• pp.151-156
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• 2019

According to recent advances in technology, major robot technologies that have been developed and commercialized for industrial use are being applied to various fields in our everyday life such as guide robots and cleaning robots. Among them, the navigation based on the self localization has become an essential element technology of the robot. In the case of indoor environment, many high-priced sensors are used, which makes it difficult to activate the robot industry. In this paper, we propose a robotic platform and a moving algorithm that can travel by using Dijkstra algorithm. The proposed system can find a short route to the destination with its own position. Also, its performance is discussed through the experimentation of an actual robot.

• ETRI Journal
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• v.43 no.4
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• pp.617-629
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• 2021

Although the actual visual simultaneous localization and mapping (SLAM) algorithms provide highly accurate tracking and mapping, most algorithms are too heavy to run live on embedded devices. In addition, the maps they produce are often unsuitable for path planning. To mitigate these issues, we propose a completely closed-loop online dense RGB-D SLAM algorithm targeting autonomous indoor mobile robot navigation tasks. The proposed algorithm runs live on an NVIDIA Jetson board embedded on a two-wheel differential-drive robot. It exhibits lightweight three-dimensional mapping, room-scale consistency, accurate pose tracking, and robustness to moving objects. Further, we introduce a navigation strategy based on the proposed algorithm. Experimental results demonstrate the robustness of the proposed SLAM algorithm, its computational efficiency, and its benefits for on-the-fly navigation while mapping.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.3
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• pp.315-321
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• 2023

In this paper, we propose an algorithm that jointly estimates the location and velocity of a near-field moving target in a pulse radar system. The proposed algorithm estimates the location and velocity corresponding to the outcome of orthogonal matching pursuit (OMP) in a 4-dimensional (4D) location-velocity space. To address the high computational complexity of 4D parameter joint estimation, we propose an algorithm that iteratively estimates the target's 2D location and velocity sequentially. Through simulations, we analyze the estimation performance and verify the computational efficiency of the proposed algorithm.