• Journal of the Korea Institute of Military Science and Technology
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• v.9 no.3
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• pp.132-140
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• 2006

In this paper, a new fault detection method for the extended Kalman filter, which uses a N-step predictor, is proposed. The N-step predictor performs the only time propagations for N-step intervals without measurement updates and its output is used as a monitoring signal for the fault detection. A consistency between the extended Kalman filter and the N-step predictor is tested to detect a fault. A test statistic is defined by the difference between the extended Kalman filter and the N-step predictor. The proposed method is applied to strapdown inertial navigation system (SDINS). By computer simulation, it is shown that the proposed method detects a fault effectively.

• Journal of Advanced Navigation Technology
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• v.11 no.2
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• pp.146-153
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• 2007

Autonomous unmanned vehicle is able to find the path and the way point by itself. 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.

• 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 Sensor Science and Technology
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• v.16 no.4
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• pp.294-300
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• 2007

Autonomous unmanned vehicle is able to find the path and the way point by itself. 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 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 Korea Society of Digital Industry and Information Management
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• v.18 no.3
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• pp.1-7
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• 2022

In this study, inaccurate RSSI values of beacons are corrected using extended Kalman filter. For the experiment, the beacon was manufactured using Arduino Uno board and HM-10 Bluetooth module. RSSI values according to the distance between beacon and the viewer were measured at intervals of 1m, 1.5m, 2m, 2.5m, 3m, 3.5m, 4m, 4.5m, and 5m. To remove the irregular signal pattern of the beacon, the extended Kalman filter was applied to obtain the average and standard deviation of the actual distance and the measured distance, and it was confirmed that more than 76.6% of the irregular signal pattern was removed after using the extended Kalman filter.In addition, through the smartphone app, it was confirmed that the distance accuracy between the beacon and the measurer was less than the actual distance and the measured distance within 2m, and the standard deviation was small.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.860-863
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• 1996

In this paper, we formulate the extended Kalman filter for calibration of gimballed inertial navigation system (GINS) at a pure navigation mode with 1500 ft/sec initial velocity and compare its performance to the linear Kalman filter's by using Monte-Carlo analysis method. It has been shown that estimation performance of the extended Kalman filter is better than that of the linear Kalman filter.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.3-5
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• 2007

This paper presents decision feedback equalizers using a recurrent neural network trained algorithm using extended Kalman filter(EKF) and sigma-point Kalman filter(SPKF). EKF is propagated, analytically through the first-order linearization of the nonlinear system. This can introduce large errors in the true posterior mean and covariance of the Gaussian random variable. The SPKF addresses this problem by using a deterministic sampling approach. The features of the proposed recurrent neural equalizer And we investigate the bit error rate(BER) between EKF and SPKF.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.905-908
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• 1988

In recent, Kalman filter technique has been much used as one of technique for tracking of the moving target. But some problem are still remained to be resolved. For example, when Kalman filter technique is applied to nonlinear system, the technique is nonoptimal estimator. Therefore, extended Kalman filter is proposed to reduce modeling error for nonlinear system. In this study, an extended Kalman filter in Cartesian coordinates is described for moving target, when the radar sensor measures range, azimuth and elevation angle in polar coordinates. And an approximate gain computation algorithm is proposed. In this approach, Kalman gains are computed for three uncoupled filter and multiplied by a Jacobian transformation determined from the measured target position and orientation.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.349-351
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• 2004

The classical dynamic backpropagation learning algorithm has the problems of learning speed and the determine of learning parameter. The Extend Kalman Filter(EKF) is used effectively for a state estimation method for a non linear dynamic system. This paper presents a learning algorithm using Dual Extended Kalman Filter(DEKF) for Fully Recurrent Neural Network(FRNN). This DEKF learning algorithm gives the minimum variance estimate of the weights and the hidden outputs. The proposed DEKF learning algorithm is applied to the system identification of a nonlinear SISO system and compared with dynamic backpropagation learning algorithm.