• International Journal of Aeronautical and Space Sciences
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• v.3 no.2
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• pp.88-94
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• 2002

This paper describes attitude determination algorithm for the low earth orbit(LEO) spacecraft using stellar inertial sensors. The cascaded gyro/star tracker extended Kalman filter is constructed to fuse two sensor data. And then the smoothing of the measurement are proposed for an unreasonable jump of star tracker. The smoothing algorithm for the rejection of star tracker error jumps is designed by scalar adaptive filter. The proposed algorithms operate to process the measurement of gyro/star tracker Kalman filter, therefore, it is comparatively simple to apply these methods to other integration systems. Simulations to gyro/star tracker integrated system show that the proposed method is effective.

• Journal of Advanced Navigation Technology
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• v.16 no.5
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• pp.738-743
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• 2012

Kalman filter is generally used in INS/GPS integrated navigation filter. However, the INS with low performance inertia sensor can not find accurate azimuth in initial alignment stage because sensor noise level is too large compare to Earth rotation rate, therefore the performance and stability of Kalman filter can not be guaranteed. In this paper, the extended Kalman filter and particle filter combined filter structure which can be overcome large initial azimuth error is proposed.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.984-989
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• 2003

The integrated global position system (GPS) and inertial navigation system (INS) has been considered as a cost-effective way of providing an accurate and reliable navigation system for civil and military system. Even the integration of a navigation sensor as a supporting device requires the development of non-traditional approaches and algorithms. The objective of this paper is to assess the feasibility of integrated with GPS and INS information, to provide the navigation capability for long term accuracy of the integrated system. Advanced algorithms are used to integrate the GPS and INS sensor data. That is fuzzy inference system based Weighted Extended Kalman Filter(FWEKF) algorithm INS signal corrections to provided an accurate navigation system of the integrated GPS and INS. Repeatedly, these include INS error, calculated platform corrections using GPS outputs, velocity corrections, position correction and error model estimation for prediction. Therefore, the paper introduces the newly developed technology which is aimed at achieving high accuracy results with integrated system. Finally, in this paper are given the results of simulation tests of the integrated system and the results show very good performance

• Journal of Positioning, Navigation, and Timing
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• v.12 no.2
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• pp.129-140
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• 2023

Autonomous driving systems are likely to be operated in various complex environments. However, the well-known integrated Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS), which is currently the major source for absolute position information, still has difficulties in accurate positioning in harsh signal environments such as urban canyons. To overcome these difficulties, integrated Visual/Inertial/GNSS (VIG) navigation systems have been extensively studied in various areas. Recently, a Compressed-State Constraint Kalman Filter (CSCKF)-based VIG navigation system (CSCKF-VIG) using a monocular camera, an Inertial Measurement Unit (IMU), and GNSS receivers has been studied with the aim of providing robust and accurate position information in urban areas. For this new filter-based navigation system, on the basis of time-propagation measurement fusion theory, unnecessary camera states are not required in the system state. This paper presents a performance evaluation of the CSCKF-VIG system compared to other conventional navigation systems. First, the CSCKF-VIG is introduced in detail compared to the well-known Multi-State Constraint Kalman Filter (MSCKF). The CSCKF-VIG system is then evaluated by a field experiment in different GNSS availability situations. The results show that accuracy is improved in the GNSS-degraded environment compared to that of the conventional systems.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.8
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• pp.148-155
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• 2014

In this research a signal processing system is designed for detecting the ultrasonic signal envelope using Xilinx's Zynq SoC(system on chip). As a design tool, Vivado IDE(integrated design environment) is used to hierarchically design the whole signal processing system. The proposed system consists of a Zynq-internal ADC, an FIR(finite impulse response) BPF(band pass filter), an absolute value calculator, an FIR LPF(lpw pass filter), and the Kalman filter. Under this configuration, two design schemes, HW design scheme with LPF as a final stage and HW/SW co-design scheme with a Kalman filter as a final stage, are compared in terms of the performance and efficiency. As a result, envelope detecting performances of the two schemes are proved to be almost same, but the HW/SW co-design is verified to be much more efficient than the HW design considering the much smaller time consumption during system design.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.3
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• pp.22-30
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• 2009

GPS and INS integrated systems are expected to become commonly available as a result of low cost Micro-Electro-Mechanical Sensor (MEMS) technology. However, the current performance achieved by low cost sensors is still relatively poor due to the large inertial sensor errors. This is particularly prevalent in the urban environment where there are significant periods of restricted sky view. To reduce the inertial sensor error, GPS and low cost INS are integrated using a Loosely Coupled Kalman Filter architecture which is appropriate in most applications where there is good satellite availability. In this paper, we present the GPS/INS sensor Integration using Loosely Coupled Kalman Filter approach. We also compare the simulation results of Wander Azimuth Strapdown Mechanization Scheme with the reference values generated by the ZH35C trajectory simulator that is describe mathematically either by the geometry of the path, or as the position of the object over time.

• The Journal of Information Systems
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• v.17 no.1
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• pp.131-153
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• 2008

So many location based service systems, including automobile navigation system logistic management, taxi fleet management, and so on, are being used everywhere. However, these are all outdoors. This paper provides a stepping stone for commercial indoor location based services by developing an integrated system of our indoor positioning and map viewer modules. For the indoor positioning, we propose WLAN (Wireless Local Area Network) based EKF (Extended Kalman Filter) which estimates user's current location and tracts user's trace in the sequence of time. Our map viewer renders a map recorded in an Autocad DXF file and provides functions of map manipulation such as zoom-in, zoom-out, and move. We integrate our indoor positioning and map viewer modules and discuss the experimental results of the integrated system.

• Journal of Advanced Navigation Technology
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• v.22 no.3
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• pp.220-227
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• 2018

The most widely used method for constructing an inertial navigation system (INS)/global navigation satellite system (GNSS) coupling system is to construct an integrated navigation system using a Kalman filter. However, depending on the trajectory, non-observable state variables may be generated. In this case, the state variables are not estimated. To solve this problem, a integrated navigation system is constructed and then an observability analysis is performed. In this paper, a 24th order position-matched Kalman filter is defined to design an INS/GNSS integrated navigation system for vehicles moving with a large pitch angle change. To verify the appropriateness of the error state variables applied to the Kalman filter, an observability analysis was performed. The trajectory was divided into five segments, and the piece-wise constant system (PWCS) was assumed for each segment, and the results were analytically analyzed. The analytical results and the simulation results confirm that the error state parameters of the Kalman filter are well-designed to the estimation side.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.12
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• pp.1067-1075
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• 2002

The system error model for the compensation of the low-cost personal navigation system is derived and the error compensation method using GPS is also proposed. The walking navigation system (WNS) that calculates navigation information through walking detection has small error than INS, but the error also increases with time. In order to improve reliability of the system regardless of time, WNS is integrated with GPS. Since WNS is usually used in urban area, the blockage of CPS signal is frequently occurred. Therefore tightly coupled Kalman filter is used for the integration of WNS and GPS. In this paper, the system model for the design of tightly coupled Kかm filter is designed and measurement is linearized in consideration of moving distance error. It is shown by Monte Carlo simulation that the error is bounded even through the number of visible satellite is less than 4.

• International Journal of Aeronautical and Space Sciences
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• v.2 no.1
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• pp.67-77
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• 2001

In recent navigation system, the profitable solution is to integrate the GPS and Stapdwon INS (SDINS) system and its integration allows compensation for shortcomings of each system. This paper describes the hardware preparation and presents the test results obtained from the automobile test of the developed system. The automobile tests was conducted with two kinds of inertial sensors and GPS receivers : short range and middle range test, to verify and evaluate the performance of the integrated navigation system. The reference of position is given by the Differential GPS(DGPS) which has cm-level accuracy to compare the accuracy of system. Kalman filtering is used for integrating GPS and SDINS and this filter effectively allows the long-term stability of GPS to correct and decrease the time deviation error of SDINS.