• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.2
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• pp.95-100
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• 2013

Localization is very important for the autonomous navigation of Unmanned Ground Vehicles; however, it is difficult that they have a precise Inertial Navigation System(INS) sensor, especially Small Unmanned Ground Vehicle(SUGV). Moreover, there are some condition such as denial of global position system(GPS), GPS/INS integrated system is not robust. This paper proposes the estimation algorithm with optical flow sensor and INS. Being compared with previous researches, the proposed algorithm is suitable for skid steering vehicles. We revised the measurement model of previous research for the accuracy of side direction position. Experimental results were performed to verify the algorithm, and the result showed an excellent performance.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.3
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• pp.188-193
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• 1996

In this paper, an integrated navigation system based on GPS(Global Positioning System) and Dead-Reckoning (DR) is designed. For the calibration of DR, a self-calibration method and a GPS-based calibration method are proposed. From the field-test results, it is shown that DR can be successfully calibrated by the two proposed calibration methods. Also, a cascaded filter approach and a mixed-measurement algorithm are employed for GPS/DR integration. By using the newly proposed mixed-measurement algorithm, it is shown in simulation that the position error becomes smaller than by using only DR even if the number of visible GPS satellites is less than 4.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.4
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• pp.361-368
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• 2022

An indoor navigation system that utilizes long-term evolution (LTE) signals has the benefit of no additional infrastructure installation expenses and low base station database management costs. Among the LTE signal measurements, received signal strength (RSS) is particularly appealing because it can be easily obtained with mobile devices. Propagation channel models can be used to estimate the position of mobile devices with RSS. However, conventional channel models have a shortcoming in that they do not discriminate between line-of-sight (LOS) and non-line-of-sight (NLOS) conditions of the received signal. Accordingly, a previous study has suggested separated LOS and NLOS channel models. However, a method for determining LOS and NLOS conditions was not devised. In this study, a machine learning-based LOS/NLOS classification method using RSS measurements is developed. We suggest several machine-learning features and evaluate various machine-learning algorithms. As an indoor experimental result, up to 87.5% classification accuracy was achieved with an ensemble algorithm. Furthermore, the range estimation accuracy with an average error of 13.54 m was demonstrated, which is a 25.3% improvement over the conventional channel model.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.5
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• pp.636-643
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• 2009

NHC means that the velocity of the vehicle in the plane perpendicular to the forward direction is almost zero. The main error source of NHC is the mounting misalignment which is the difference between the body frame of a land vehicle and the sensor frame of an inertial measurement unit. This paper suggests new NHC algorithm that can reduce position errors by real-time estimation of mounting misalignment. Then NHC/ZUPT integrated land navigation system is designed and its performances are analyzed by simulations with van test data. Simulation results show that the proposed NHC/ZUPT land navigation system improves navigation accuracy regardless of misalignment angle and is very useful when SDINS operates stand-alone for land vehicle navigation with large mounting misalignment.

• Transactions of the Korean Society of Automotive Engineers
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• v.16 no.3
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• pp.7-14
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• 2008

Unmanned vehicle has a performance for finding the path and the way point by itself, so called orientation and direction. For the more precise navigation performance, Extended kalman filter, which is integrated with inertial navigation system and global positioning system is proposed in this paper. Extended kalman filter's performance is evaluated by the simulation and applied to the unmanned vehicle. The test result shows the effectiveness of Extended kalman filter for the navigation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.5
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• pp.339-348
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• 2017

Due to recent improvements in computer processing speed and image processing technology, researches are being actively carried out to combine information from a camera with existing GNSS (Global Navigation Satellite System) and dead reckoning. In this study, the mathematical model based on SPR (Single Photo Resection) is derived for image-based assistant algorithm for vehicle positioning. Simulation test is performed to analyze factors affecting SPR. In addition, GNSS/on-board vehicle sensor/image based positioning algorithm is developed by combining image-based positioning algorithm with existing positioning algorithm. The performance of the integrated algorithm is evaluated by the actual driving test and landmark's position data, which is required to perform SPR, based on simulation. The precision of the horizontal position error is 1.79m in the case of the existing positioning algorithm, and that of the integrated positioning algorithm is 0.12m at the points where SPR is performed. In future research, it is necessary to develop an optimized algorithm based on the actual landmark's position data.

• Journal of Biosystems Engineering
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• v.36 no.2
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• pp.116-121
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• 2011

This study was conducted to develop a robust navigator which could be in positioning for precision farming through developing a plural GPS receiver with 4 sets of GPS antenna. In order to improve positioning accuracy by integrating GPS signals received simultaneously, the algorithm for processing plural GPS signal effectively was designed. Performance of the algorithm was tested using a simulation program and a fixed point on WGS 84 coordinates. Results of this study are aummarized as followings. 1. 4 sets of lower grade GPS receiver and signals were integrated by kalman filter algorithm and geometric algorithm to increase positioning accuracy of the data. 2. Prototype was composed of 4 sets of GPS receiver and INS components. All Star which manufactured by CMC, gyro compass made by KVH, ground speed sensor and integration S/W based on RTOS(Real Time Operating System)were used. 3. Integration algorithm was simulated by developed program which could generate random position error less then 10 m and tested with the prototype at a fixed position. 4. When navigation data was integrated by geometrical correction and kalman filter algorithm, estimated positioning erros were less then 0.6 m and 1.0 m respectively in simulation and fixed position tests.

• Journal of Positioning, Navigation, and Timing
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• v.9 no.2
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• pp.59-63
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• 2020

The antenna arrays are known to be effective for GPS anti-jamming and the performance can be improved further if a dual-polarized antenna array is used. However, when the Minimum Variance Distortionless Response (MVDR) beamformer is used as a signal processing algorithm for the dual-polarized antenna array, the anti-jamming performance can degrade in the presence of errors in the steering vector that is a key factor of the MVDR beamformer. Therefore, in this paper, the effect of the steering vector error on the anti-jamming performance of the dual-polarized antenna array is analyzed by simulations and the result is compared to that of the single-polarized antenna array.

• Journal of Advanced Navigation Technology
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• v.23 no.5
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• pp.437-443
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• 2019

In this paper, a new algorithm for the calculation of the range between an LTE base station (BS) and a user equipment (UE) using time-of-arrival (TOA) measurements of LTE signals is proposed. First, the cell identity (cell ID) of the received signal is acquired using the primary synchronization signal (PSS) and secondary synchronization signal (SSS) to identify the BS transmitted the signal. The proposed algorithm exploits the cell-specific reference signal (CRS), the reference sequence inserted in commercial LTE signals, to estimate the time delay using 2D cross-correlation. The obtained TOA estimations can be used to calculate the range employed from the known BS location. The performance of the proposed algorithm is evaluated with the experiment performed using real LTE signals transmitted from the commercial BS.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.377-383
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• 2021

Recently, in accordance with the 4th industrial revolution, The use of autonomous mobile robots for flexible logistics transfer is increasing in factories, the warehouses and the service areas, etc. In large factories, many manual work is required to use Simultaneous Localization and Mapping(SLAM), so the need for the improved mobile robot autonomous driving is emerging. Accordingly, in this paper, an algorithm for mapless navigation that travels in an optimal path avoiding fixed or moving obstacles is proposed. For mapless navigation, the robot is trained to avoid fixed or moving obstacles through Deep Q Network (DQN) and accuracy 90% and 93% are obtained for two types of obstacle avoidance, respectively. In addition, DQN requires a lot of learning time to meet the required performance before use. To shorten this, the target size change algorithm is proposed and confirmed the reduced learning time and performance of obstacle avoidance through simulation.