• Journal of Aerospace System Engineering
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• v.4 no.3
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• pp.17-23
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• 2010

GPS sensors provide accurate position and velocity of moving vehicles. However, GPS is weak at intermittent signal loss and large position error. Combination with INS improves the GPS position accuracy during the GPS signal loss. In this paper, a fusion filter using GPS and INS is developed and its perfomance is analyzed with RC car and RC airplane experiments.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.55-55
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• 2000

This paper presents a fault detection and isolation(FDI) method based on Ceneralized Likelihood Ratio(GLR) test for the tightly coupled INS/GPS. State and measurement GLR tests detect INS or GPS fault. Once the fault is detected, Multi-hypothesized GLR scheme performs the fault isolation between INS and GPS and find which satellite malfunctions. Simulation results show that the GLR method is effective enough to detect and isolate a fault of the integrated navigation system.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.8
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• pp.796-803
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• 2011

It is called self-alignment or initial alignment that INS(Inertial Navigation System) is aligned using the measurements from the inertial sensors as an accelerometer and a gyroscope and the inserted reference navigation information in the stop state. The main purpose of self-alignment is to obtain the initial attitude of INS. The accuracy of self-alignment is determined by the performance grade of the used inertial sensors, especially horizontal attitude accuracy by the horizontal accelerometer and vertical attitude accuracy by the E-axis gyroscope. Therefore the uncertain errors in the inertial sensors cause the performance of self-alignment to degrade. In this paper, we analyze theoretically and through a simulation how the errors of inertial sensors in the temperature stabilizing state, one of the uncertain errors, affect the accuracy of self-alignment.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.127-132
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• 2006

The Global Positioning System (GPS), Inertial Navigation System (INS) and Pseudolite (PL) technologies all play very important roles in navigation systems. As an independent navigation system, GPS can provide high precision positioning results which are independent of time. However, the performance will become unreliable when the system experiences high dynamics, or when the receiver is exposed to jamming or RF interference. In comparison to GPS, though INS is autonomous and provides good short-term accuracy, its use as a standalone navigation system is limited due to the time-dependent growth of the inertial sensor errors. PLs are ground-based transmitters that can transmit GPS-like signals. They have some advantages in that their positions can be determined precisely, and the Signal-to-Noise Ratios (SNR) are relatively high. Because their combined performance, in principle, overcomes the shortcomings of the individual systems, the integration of GPS, INS and PL is increasingly receiving attention from researchers. Depending on the desired performance vs complexity, system integration can be carried out at different levels, namely loose, tight and ultra-tight coupling. Compared with loose and tight integration, although it is more complex in terms of system design, ultra-tight integration will be the basis of the next generation of reliable and robust navigation systems. Its main advantages include improved performance under exposure to high dynamics, and jamming and RF interference mitigation. This paper presents an overview of the ultra-tight integration developments and discusses some of the challenging issues.

• 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.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.2_2
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• pp.359-370
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• 2023

For a vehicle to follow a reference path accurately, its position must be estimated accurately and reliably. In this paper, we propose a lateral control algorithm for autonomous navigation of a vehicle via USAT(Ultrasonic Satellite System), which is an absolute position measurement system using an ultrasonic wave and INS(Inertial Navigation System) integration. In order to estimate the vehicle's parameters, a J-turn test is used. And the autonomous navigation performances of proposed lateral control algorithm and validity of proposed lateral control algorithm are verified and evaluated by simulation and experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.2
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• pp.183-189
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• 2011

An alignment algorithm for strapdown inertial navigation systems is proposed, in which the psi angle error model is utilized. The proposed alignment algorithm is derived from the Psi angle error model which has been widely used in real-time navigation systems. The equation for expecting steady state alignment error is also derived. The proposed algorithm was verified through real-time experiments. Experimental results show that the proposed algorithm can be used in the inertial navigation system and GNSS/INS integrated navigation system to get an initial attitude of the vehicle.

• Journal of Positioning, Navigation, and Timing
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• v.8 no.3
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• pp.107-117
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• 2019

When a vehicle moves with a high rotation rate, it is not easy to measure the angular velocity using an off-the-shelf gyroscope. If the angular velocity is estimated using the extended Kalman filter in the gyro-free inertial navigation system, the effect of the accelerometer error and initial angular velocity error can be reduced. In this paper, in order to improve the navigation performance of the gyro-free inertial navigation system, an angular velocity estimation method is proposed based on an extended Kalman filter with an accelerometer random bias error model. In order to show the validity of the proposed estimation method, angular velocities and navigation outputs of a vehicle with 3 rev/s rotation rate are estimated. The results are compared with estimates by other methods such as the integration and an extended Kalman filter without an accelerometer random bias error model. The proposed method gives better estimation results than other methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.518-524
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• 2011

This paper presents a study on the calibration of accelerometer data in the gyroscope free inertial navigation system(GFINS) using fuzzy inference system(FIS). The conventional INS(inertial navigation system) which can measure yaw rate and linear velocity using inertial sensors as the gyroscope and accelerometer. However, the INS is difficult to design as small size and low power because it uses the gyroscope. To solve the problem, the GFINS which does not have the gyroscope have been studied actively. However, the GFINS has cumulative error problem still. Hence, this paper proposes Fuzzy-GFINS which can calibrate the data of an accelerometer using FIS consists of two inputs that are ratio between linear velocity of the autonomous ground vehicle(AGV) and the accelerometer and ratio between linear velocity of the encoders and the accelerometer. To evaluate the proposed Fuzzy-GFINS, we made the AGV with Mecanum wheels and applied the proposed Fuzzy-GFINS. In experimental result, we verified that the proposed method can calibrate effectively data of the accelerometer in the GFINS.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.5
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• pp.1010-1018
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• 2009

An inertial navigation system for pedestrian position tracking is proposed, where the position is computed using inertial sensors mounted on shoes. Inertial navigation system(INS) errors increase with time due to inertial sensor errors, and therefore it needs to reset errors frequently. During normal walking, there is an almost periodic zero velocity instance when a foot touches the floor. Using this fact, estimation errors are reduced and this method is called the zero velocity updating algorithm. When implementing this zero velocity updating algorithm, it is important to know when is the zero velocity interval. The gait states are modeled as a Markov process and each state is estimated using the hidden Markov model smoother. With this gait estimation, the zero or nearly zero velocity interval is more accurately estimated, which helps to reduce the position estimation error.