• Journal of Positioning, Navigation, and Timing
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• v.4 no.3
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• pp.141-150
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• 2015

In this study, a Global Navigation Satellite System (GNSS) / Inertial Navigation System (INS) / odometer / barometer integrated navigation system that uses a commercial navigation device including Micro Electro Mechanical Systems (MEMS) accelerometer and gyroscope in addition to GNSS, odometer information obtained from a vehicle, and a separate MEMS barometer sensor was implemented, and the performance was verified. In the case of GNSS and GNSS/INS integrated navigation system that are generally used in a navigation device, the performance would deteriorate in areas where GNSS signals are not available. Therefore, an integrated navigation system that calculates a better navigation solution in areas where GNSS signals are not available compared to general GNSS/INS by correcting the velocity error of GNSS/INS using an odometer and by correcting the cumulative altitude error of GNSS/INS using a barometer was suggested. To verify the performance of the navigation system, a commercial navigation device (Softman, Hyundai Mnsoft, http://www.hyundai-mnsoft.com) and a barometer sensor (ST Company) were installed at a vehicle, and an actual driving test was performed. To examine the performance of the algorithm, the navigation solutions of general GNSS/INS and the GNSS/INS/odometer/barometer integrated navigation system were compared in an area where GNSS signals are not available. As a result, a navigation solution that has a smaller position error than that of GNSS/INS could be obtained in the area where GNSS signals are not available.

• Journal of Ocean Engineering and Technology
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• v.27 no.2
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• pp.93-99
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• 2013

This paper describes a robust algorithm for an integrated underwater navigation system based on VKF (velocity Kalman filter). The proposed approach relies on a VKF, augmented by the altitude from an echo-sounder-based switching architecture to yield robust performance, even when DVL (Doppler velocity log) exceeds the measurement range and the measured value cannot be valid. The proposed approach relies on three parts: 1) PINS (pure inertial navigation system), 2) VKF design, and 3) VKF-aided integrated navigation filter design. To evaluate the proposed method, we compare the results of the VKF-aided navigation system with the simulation result from a PINS and conventional INS-DVL method.

• Journal of Positioning, Navigation, and Timing
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• v.9 no.2
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• pp.99-115
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• 2020

Since GNSS is easily affected by jamming and/or spoofing, alternative navigation systems can be operated as backup system to prepare for outage of GNSS. Alternative navigation systems are being researched over the world, and a multi-radio integrated navigation system using alternative navigation systems such as KNSS, eLoran, Loran-C, DME, VOR has been researched in Korea. Least Square or Kalman filter can be used to estimate navigation parameters in the navigation system. A large number of measurements of the Kalman filter may lead to heavy computational load. The decentralized Kalman filter and the federated Kalman filter were proposed to handle this problem. In this paper, the decentralized Kalman filter and the federated Kalman filter are designed for the multi-radio integrated navigation system and the performance evaluation result are presented. The decentralized Kalman filter and the federated Kalman filter consists of local filters and a master filter. The navigation parameter is estimated by local filters and master filter compensates navigation parameter from the local filters. Characteristics of three Kalman filters for a linear system and nonlinear system are investigated, and the performance evaluation results of the three Kalman filters for multi-radio integrated navigation system are compared.

• The Journal of Korea Robotics Society
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• v.15 no.1
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• pp.24-31
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• 2020

Alternative navigation in underwater environments is essential to prevent accumulating drift error of dead reckoning. In case of using an external positioning system, the installation and management process of the transmission station is cumbersome, and the operation range of underwater vehicle is limited. In order to solve this problem, navigation using geophysical information such as terrain, geomagnetic field and gravity can be used. Unlike the terrain, geomagnetic field and gravity are composed of 3-D information, so continuation process is required. In this paper, we present a integrated navigation algorithm using multiple geophysical information for long-term operation of UUV. The proposed algorithm is verified through numerical simulation in an artificially generated environments. As a result, integrated navigation showed higher navigation accuracy than single alternative navigation.

• Journal of Advanced Navigation Technology
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• v.14 no.6
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• pp.791-799
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• 2010

In this paper, integrated navigation algorithm is designed for land transport sector which is needed high accuracy and monitoring program is developed for lane departure warning. High accuracy position information which is possible lane separation is needed for lane departure warning, so position detection algorithm based GPS/DR which combine GPS with dead reckoning is proposed. For the verification of the designed integrated navigation algorithm, we drived to acquire data and showed post-processing experiment results with monitoring program. Vehicle driving movie and aerial photograph in monitoring program is designed to show lane keeping and lane separation.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.241-245
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• 2005

Strapdown inertial navigation system (SINS) integrated with astronavigation system (ANS) yields reliable mission capability and enhanced navigational accuracy for spacecrafts. The theory and characteristics of integrated system based on unscented Kalman filtering is investigated in this paper. This Kalman filter structure uses unscented transform to approximate the result of applying a specified nonlinear transformation to a given mean and covariance estimate. The filter implementation subsumed here is in a direct feedback mode. Axes misalignment angles of the SINS are observation to the filter. A simple approach for simulation of axes misalignment using stars observation is presented. The SINS error model required for the filtering algorithm is derived in space-stabilized mechanization. Simulation results of the integrated navigation system using a medium accuracy SINS demonstrates the validity of this method on improving the navigation system accuracy with the estimation and compensation for gyros drift, and the position and velocity errors that occur due to the axes misalignments.

• 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

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2005.10a
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• pp.47-52
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• 2005

This paper describes how a relatively compensate the error of position by using low cost Inertial Measurement Unit (IMU) has been evaluated and compared with the well established method based on a Kalman Filter(KF). The compensation algorithm by using IMU have been applied to the problem of integrating information from an Inertial Navigation System (INS). The KF is to estimate and compensate the errors of an INS by using the integrated INS velocity and position. We verify the proposed algorithm by simulation results.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.491-494
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• 2006

Kalman filtering (KF) is hard to be applied to the SINS (Strap-down Inertial Navigation System)/RDSS (Radio Determination Satellite Service) integrated navigation system directly because the time delay of RDSS positioning in active mode is random. BP (Back-Propagation) Neuron computing as a powerful technology of Artificial Neural Network (ANN), is appropriate to solve nonlinear problems such as the random time delay of RDSS without prior knowledge about the mathematical process involved. The new algorithm betakes a BP neural network (BPNN) and velocity feedback to aid KF in order to overcome the time delay of RDSS positioning. Once the BP neural network was trained and converged, the new approach will work well for SINS/RDSS integrated navigation. Dynamic vehicle experiments were performed to evaluate the performance of the system. The experiment results demonstrate that the horizontal positioning accuracy of the new approach is 40.62 m (1 ${\sigma}$), which is better than velocity-feedback-based KF. The experimental results also show that the horizontal positioning error of the navigation system is almost linear to the positioning interval of RDSS within 5 minutes. The approach and its anti-jamming analysis will be helpful to the applications of SINS/RDSS integrated systems.

• ETRI Journal
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• v.35 no.5
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• pp.943-946
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• 2013

Instead of the extended Kalman filter, the unscented Kalman filter (UKF) has been used in nonlinear systems without initial accurate state estimates over the last decade because the UKF is robust against large initial estimation errors. However, in a multirate integrated system, such as an inertial navigation system (INS)/Global Positioning System (GPS) integrated navigation system, it is difficult to implement a UKF-based navigation algorithm in a low-grade or mid-grade microcontroller, owing to a large computational burden. To overcome this problem, this letter proposes a modified UKF that has a reduced computational burden based on the basic idea that the change of probability distribution for the state variables between measurement updates is small in a multirate INS/GPS integrated navigation filter. The performance of the modified UKF is verified through numerical simulations.