• Journal of Positioning, Navigation, and Timing
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• v.4 no.4
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• pp.181-186
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• 2015

A High-degree Cubature Kalman Filter (CKF) is proposed to deal with the Strapdown Inertial Navigation System (SDINS) alignment problem. In-flight Alignment (IFA) is an effective method to compensate for attitude errors of the navigation system. While providing precise attitude error compensation, however, the external source aided alignment often creates a nonlinear filtering problem caused by a large misalignment angle. Introduced recently, Cubature Kalman Filter is a suitable technique for various nonlinear problems. In this paper, a higher degree CKF is applied to this accuracy-is-everything SDINS IFA problem. The simulation results show that the proposed technique outperformed a traditional nonlinear filter in terms of precision and alignment time.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1001-1006
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• 2003

This paper presents the pedestrian navigation algorithm and the error compensation filter. The pedestrian navigation system (PNS) consists of the MEMS inertial sensors, the fluxgate, and the small-size GPS receiver. PNS calculates the navigational information using the signal patterns of the accelerometers. And the navigational information is completed by integration of the patterns, the fluxgate, and the GPS information. In general, PNS can provide the better solution than the low-cost inertial navigation system.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.643-648
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• 1988

A strapdown inertial navigation system fabrication utilizing dynamically tuned gyroscope was finished as a first stage development. So it's design, fabrication and tests are reported. Although this system lacks in accuracy compared with the cimballed system, factors such as low cost, small size and lightness make it useable in wide range of applications. The initial cost for investment is relatively cheap, and so it is best suitable for local development in various kind of inertial navigation system. Since all of the locally used systems are imported and even with it's close relation to the military, foreign technical transfer is practically non-existent. The independent local development of such system at a time of domestic initation in aerospace and defense industry, can be seen as a significant milestone in the advancement of the inertial navigaion system field.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.3
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• pp.199-208
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• 2022

Inertial Navigation System (INS)/Global Navigation Satellite System (GNSS) integrated navigation system can be used for land vehicle navigation. When the GNSS signal is blocked in a dense urban area or tunnel, however, the problem of increasing the error over time is unavoidable because navigation must be performed only with the INS. In this paper, Non-Holonomic Constraints (NHC) information is utilized to solve this problem. The NHC may correct some of the errors of the INS. However, it should be noted that NHC information is not applicable to all areas within the vehicle. In other words, the lever arm effect occurs according to the distance between the Inertial Measurement Unit (IMU) mounting position and the NHC effective point, which causes the NHC condition not to be satisfied at the IMU mounting position. In this paper, an INS/GNSS/NHC integrated navigation filter is designed, and this filter has a function to compensate for the lever arm effect. Therefore, NHC information can be safely used regardless of the vehicle's driving environment. The performance of the proposed technology is verified through Monte-Carlo simulation, and the performance is confirmed through experimental test.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.2
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• pp.71-81
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• 2022

In order for electro-optical tracking system (EOTS) to have accurate target coordinate, accurate navigation results are required. If an integrated navigation system is configured using an inertial measurement unit (IMU) of EOTS and the vehicle's navigation results, navigation results with high rate can be obtained. Due to the time-delay of the navigation results of the vehicle in the EOTS and scale factor errors of the EOTS IMU in high-speed and high dynamic operation of the vehicle, it is much more difficult to have accurate navigation results. In this paper, an integrated navigation system of EOTS which compensates time-delay and scale factor error is proposed. The proposed integrated navigation system consists of vehicle's navigation system which provides time-delayed navigation results, an EOTS IMU, an inertial navigation system (INS), an augmented Kalman filter and integration Kalman filter. The augmented Kalman filter outputs navigation results, in which the time-delay of the vehicle's navigation results is compensated. The integration Kalman filter estimates position, velocity, attitude error of the EOTS INS and accelerometer bias, accelerometer scale factor error, gyro bias and gyro scale factor error from the difference between the output of the augmented Kalman filter and the navigation result of the EOTS INS. In order to check performance of the proposed integrated navigation system, simulations for output data of a measurement generator and land vehicle experiments were performed. The performance evaluation results show that the proposed integrated navigation system provides more accurate navigation results.

• 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 Ocean Engineering and Technology
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• v.19 no.6 s.67
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• pp.78-85
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• 2005

Initial alignment and localization are important topics in inertial navigation systems, since misalignment and initial position error wholly propagate into the navigation systems and deteriorate the performance of the systems. This paper presents the error convergence characteristics of the hybrid navigation system for underwater vehicles initial position, which is based on an inertial measurement unit (IMU) accompanying a range sensor. This paper demonstrates the improvement on the navigational performance oj the hybrid system with the range information, especially focused on the convergence of the estimation of underwater vehicles initial position error. Simulations are performed with experimental data obtained from a rotating ann test with a fish model. The convergence speed and condition of the initial error removal for random initial position errors are examined with Monte Carlo simulation. In addition, numerical simulation is conducted with an AUV model in lawn-mowing survey mode to illustrate the error convergence of the hybrid navigation System for initial position error.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.2
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• pp.174-185
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• 2006

This paper describes a DR construction for land navigation and the sigma point based receding horizon Kalman FIR (SPRHKF) filter for DR/GPS hybrid navigation system. A simple DR construction is adopted to improve the performance both of the pure DR navigation and the DR/GSP hybrid navigation system. In order to overcome the flaws of the EKF, the SPKF is merged with the receding horizon strategy. This filter has several advantages over the EKF, the SPKF, and the RHKF filter. The advantages include the robustness to the system model uncertainty, the initial estimation error, temporary unknown bias, and etc. The computational burden is reduced. Especially, the proposed filter works well even in the case of exiting the unmodeled random walk of the inertial sensors, which can be occurred in the MEMS inertial sensors by temperature variation. Therefore, the SPRHKF filter can provide the navigation information with good quality in the DR/GPS hybrid navigation system for land navigation seamlessly.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.3
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• pp.565-573
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• 2017

This paper presents a multistage in-flight alignment (MIFA) method for a strapdown inertial navigation system (SDINS) suitable for moving vehicles with no initial attitude references. A SDINS mounted on a moving vehicle frequently loses attitude information for many reasons, and it makes solving navigation equations impossible because the true motion is coupled with an undefined vehicle attitude. To determine the attitude in such a situation, MIFA consists of three stages: a coarse horizontal attitude, coarse heading, and fine attitude with adaptive Kalman navigation filter (AKNF) in order. In the coarse horizontal alignment, the pitch and roll are coarsely estimated from the second order damping loop with an input of acceleration differences between the SDINS and GPS. To enhance estimation accuracy, the acceleration is smoothed by a scalar filter to reflect the true dynamics of a vehicle, and the effects of the scalar filter gains are analyzed. Then the coarse heading is determined from the GPS tracking angle and yaw increment of the SDINS. The attitude from these two stages is fed back to the initial values of the AKNF. To reduce the estimated bias errors of inertial sensors, special emphasis is given to the timing synchronization effects for the measurement of AKNF. With various real flight tests using an UH60 helicopter, it is proved that MIFA provides a dramatic position error improvement compared to the conventional gyro compass alignment.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.2
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• pp.138-145
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• 2000

In this paper an initial alignment algorithm for a strapdown inertial navigation system is implemented using a RISC CPU board. The algorithm computes roll pitch and yaw angles of the direction cosine matrix utilizing measured components of the specific force and earth rate when the navigation system is stationary. The coarse alignment algorithm is performed first and then the fine alignment algorithm containing a 3rd-order gyrocompass loop follows. The experimental set consists of an IMU a CPU board and a monitoring system Experimental results show that the implemented algorithm can be utilized in navigation systems.