• Journal of Advanced Navigation Technology
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• v.20 no.1
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• pp.53-58
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• 2016

In this paper, we propose a novel quadratic Kalman filter based object tracking algorithm using moving pictures. Quadratic Kalman filter, which is introduced recently, has not yet been applied to the problem of 3-dimensional (3-D) object tracking. Since the mapping of a position in 2-D moving pictures into a 3-D world involves non-linear transformation, appropriate algorithm must be chosen for object tracking. In this situation, the quadratic Kalman filter can achieve better accuracy than extended Kalman filter. Under the same conditions, we compare extended Kalman filter, unscented Kalman filter and sequential importance resampling particle filter together with the proposed scheme. In conculsion, the proposed scheme decreases the divergence rate by half compared with the scheme based on extended Kalman filter and improves the accuracy by about 1% in comparison with the one based on unscented Kalman filter.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.973-978
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• 2005

In this paper, we investigate an improved mobile robot localization method using Kalman filter. The highlight of the paper lies in the formulation of combined Kalman filter and its application to mobile robot experiment. The combined Kalman filter is a kind of extended Kalman filter which has an extra degree of freedom in Kalman filtering recursion. It consists of the standard Kalman filter, i.e., the predictor-corrector and the perturbation estimator which reconstructs unknown dynamics in the state transition equation of mobile robot. The combined Kalman filter (CKF) enables to achieve robust localization performance of mobile robot in spite of heavy perturbation such as wheel slip and doorsill crossover which results in large odometric errors. Intrinsically, it has the property of integrating the innovation in Kalman filtering, i.e., the difference between measurement and predicted measurement and thus it is so much advantageous in compensating uncertainties which has not been reflected in the state transition model of mobile robot. After formulation of the CKF recursion equation, we show how the design parameters can be determined and how much beneficial it is through simulation and experiment for a two-wheeled mobile robot under indoor GPS measurement system composed of four ultrasonic satellites. In addition, we discuss what should be considered and what prerequisites are needed to successfully apply the proposed CKF in mobile robot localization.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1349-1359
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• 2021

Acquisition of precise position and velocity information of GNSS-INS (Global Navigation Satellite System; Inertial Navigation System) sensors in obtaining SAR SLC (Single Look Complex) images from raw data using BPA (Backprojection Algorithm) was regarded decisive. Several studies on BPA were accompanied by Kalman Filter for sensor noise oppression, but often implemented once where insufficient information was given to determine whether the filtering was effectively applied. Multiple operation of Kalman Filter on GNSS-INS sensor was presented in order to assess the effective order of sensor noise calibration. FMCW (Frequency Modulated Continuous Wave)-SAR raw data was collected from twice airborne experiments whose GNSS-INS information was practically and repeatedly filtered via Kalman Filter. It was driven that the FMCW-SAR raw data with diverse path information could derive different order of Kalman Filter with optimum operation of BPA image restoration.

• Journal of the Korea Convergence Society
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• v.4 no.4
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• pp.41-48
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• 2013

Observer design for system with unknown input was carried out. First, Kalman filter was considered to estimate system state with White noise. With the results of Kalman filter design, state observer, controller properties, including controllability and observability, and the Kalman filter structure and algorithm were also studied. Kalman filter algorithm was applied to Position and velocity measurement based on Kalman filter with white noise, and it was constructed and achieved by programming based on Matlab programming. Finally, observer for system with unknown input was constructed with the help of Gershgorin's disc theorem. With the designed observer, system states was constructed and applied to system with unknown input. By simulation results, estimation performance was verified. In this project, state feedback control theory, observer theory and relevant design procedure, as well as Kalman filter design were understood and used in practical application.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1897-1902
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• 2005

The objective of this paper is to estimate the concentration distribution in flow field inside the pipeline based on electrical impedance tomography. Special emphasis is given to the development of dynamic imaging technique for two-phase field undergoing a rapid transient change. Nonlinearity-compensation extended Kalman filter is employed to cope with unexpected measurement uncertainty. The nonlinearity-compensation extended Kalman filter compensates for the influence of measurement uncertainty and solves the instability of extended Kalman filter. Extensive computer simulations are carried out to show that nonlinearity-compensation extended Kalman filter has enhanced estimation performance especially in the unexpected measurement environment.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1655-1658
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• 1997

To obtain aircraft dynamic parameters, various estimation methods such as Maximum Likelihood, Linear Regression are applied. In this paper we adopt the extended Kalman filter(EKF) to estimate the stability and control derivatives in aircraft dynamic models from flight test data. The extended Kalman filter is applied to nonlinear augmented system assuming that unknown parameters are additional states. In this work, the results of the extended Kalman filter are compared with the results of the wind tunnel test using Chang Gong-91 aircraft flight test data.

• 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 Korea Information Processing Society Conference
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• 2011.11a
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• pp.1365-1368
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• 2011

The Kalman Filter is one of the most common techniques of tracking mobile user. Web service is a promising method of reusing programs. Therefore, this paper presents our implementation of Kalman Filter web service. There are three ways of implementing a web service system. Ours is Kalman filter as RPC web service.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.683-688
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• 2017

The unscented Kalman filter features a direct transforming process involving unscented transformation for removing the linearization process error that may occur in the extended Kalman filter. This paper proposes a reduced-order unscented Kalman filter for the sensorless control of a permanent magnet synchronous motor. The proposed method can reduce the computational load without degrading the accuracy compared to the conventional Kalman filters. Moreover, the proposed method can directly estimate the electrical rotor position and speed without a back-electromotive force. The proposed Kalman filter for the sensorless control of a permanent magnet synchronous motor is verified through the simulation and experimentation. The performance of the proposed method is evaluated over a wide range of operations, such as forward and reverse rotations in low and high speeds including the detuning parameters.