• 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.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2335-2337
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• 2004

Kalman filter를 필터링에 적용할 때에 센서의 아날로그 신호에 들어오는 측정값의 잡음은 Gaussian 확률분포를 갖는다고 가정한다. 그러나 Kalman filter를 digital 컴퓨터에 적용할 경우에는 analog-to-digital converter에서 측정값의 잡음이외에도 quantization 잡음이 존재하며 본 논문에서는 이러한 경우에 quantization 영향이 Kalman filter 알고리듬에 미치는 영향을 수치적으로 분석하여 quantization을 Kalman filter 구현에 고려해야 될 사항으로 분류하고자 한다.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.45.2-45
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• 2002

$\textbullet$ Uncertainties are described by sum quadratic constraint(SQC) $\textbullet$ SQC is converted into an indefinite quadratic cost function $\textbullet$ A Kalman filter developed in indefinite inner product space is Krein space Kalman filter $\textbullet$ To minimize the SQC, the Krein space Kalman filter is used $\textbullet$ The proposed robust filter outperforms the standard Kalman filter and existing robust Kalman filter $\textbullet$ The proposed filter has the same recursive, simple structure as the standard Kalman filter $\textbullet$ Easy to design, adequate for on-line implementation

• 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.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.277-282
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• 2006

It is well known that Kalman filtering is an optimal real-time data fusion method for GPS/INS integration. However, it has some limitations in terms of stability, adaptability and observability. A Kalman filter can perform optimally only when its dynamic model is correctly defined and the noise statistics for the measurement and process are completely known. It is found that estimated Kalman filter states could be influenced by several factors, including vehicle dynamic variations, filter tuning results, and environment changes, etc., which are difficult to model. Neural networks can map input-output relationships without apriori knowledge about them; hence a proper designed neural network is capable of learning and extracting these complex relationships with enough training. This paper presents a GPS/INS integrated system that combines Kalman filtering and neural network algorithms to improve navigation solutions during GPS outages. An Extended Kalman filter estimates INS measurement errors, plus position, velocity and attitude errors etc. Kalman filter states, and gives precise navigation solutions while GPS signals are available. At the same time, a multi-layer neural network is trained to map the vehicle dynamics with corresponding Kalman filter states, at the same rate of measurement update. After the output of the neural network meets a similarity threshold, it can be used to correct INS measurements when no GPS measurements are available. Selecting suitable inputs and outputs of the neural network is critical for this hybrid method. Detailed analysis unveils that some Kalman filter states are highly correlated with vehicle dynamic variations. The filter states that heavily impact system navigation solutions are selected as the neural network outputs. The principle of this hybrid method and the neural network design are presented. Field test data are processed to evaluate the performance of the proposed method.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.4
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• pp.223-229
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• 2017

An angle error is a factor obstructing to track accurate position in tracking radars. And the noise incurring the angle error can be divided as follows; thermal noise and glint. In general, Extended Kalman filter used in tracking radars is designed with considering thermal noise only. The Extended Klaman filter uses a fixed measurement error covariance when updating an estimate state by using ahead state and measurement. But, a noise power varies according to the range. Therefore we purposes the adaptive Kalman filter which changes the measurement noise covariance according to the range. In this paper, we compare the performance of the Extended Kalman filter and the proposed adaptive Kalman filter by considering KSLV-I (Korean Satellite Launch Vehicles).

• 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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.16 no.28
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• pp.31-37
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• 1993

Box-Jenkins models have some important limitations to the procedure : (a) They require a great deal of time, efforts and expertise for the model identification. (b) They require an extensive amount of past observations to identify an acceptable model. (c) The model selected is a constant model in time. Therefore, the Kalman Filter is recommended as a technique to overcome the three problems mentioned above. The research reported here uses the Kalman Filter algorithm to propose Kalman-AR(p) model. The data analysis shows that the Kalman-AR(p) model proposed can be used to resolve the problems of Box-Jenkins AR(p)model. It is seen that the Kalman Filter has great potentials for real-time industrial applications.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.3
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• pp.220-227
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• 2014

This paper presents about multi-rate sensors' synchronization and filter fusion via a sigmoid function of the Kalman filter. To synchronize multi-rate sensors, the estimation states of the Kalman filter is modified. A specific matrix that makes the filter choose sensor values only updated is multiplied to measurement matrix. For the filter that has weak points on some criteria, filter fusion is suggested by using sigmoid function. Modified kalman filter is tested with practical case. A sigmoid function was designed for the test and the performance of the modified function is estimated with respect to conventional Kalman filter. Unscented Kalman filter is used to the base filter of the suggested filter because of its stability.