• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.344-347
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• 1995

The standard estimation and filtering theory are well known and has recently been incorporated with the H$_{\infty}$ optimization techniques where the parametrizations of all estimators and filters are utilized. The issue of reducing its order is always of interest. This paper presents a method for synthesizing low-order stable state estimators. The method presented in this paper is based on the utilization of a free parameter function contained in the parametrization of all state estimators. The results obtained in the paper are compared with standard results on low-order estimators. Both results are shown to be the same in a sense of its orders, but the approaches taken are largely different. It is also shown in the paper that the method can easily and directly be extended to the Kalman filters and the H$_{\infty}$ (sub)optimal filters. Consequently, the orders of all state estimators, Kalman filters, and H$_{\infty}$ filters are shown to be reduced down to the number of states minus the number of outputs, respectively.ly.

• 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.

• Journal of Korea Multimedia Society
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• v.24 no.12
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• pp.1589-1597
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• 2021

Due to the low output rate and time delay of vehicle's navigation results, the electro-optical tracking system(EOTS) cannot estimate accurate target positions. If an inertial measurement unit(IMU) is additionally mounted into the EOTS and inertial navigation system(INS) is constructed, the high navigation output rate can be obtained. And the time-delay can be compensated by using the augmented Kalman filter. An accurate initial attitude is required in order to have accurate navigation outputs. In this paper, an attitude determination algorithm is proposed using the augmented Kalman filter in order to compensate measurement delay of the EOTS and have accurate initial attitude. The proposed initial attitude determination algorithm consists of an augmented Kalman filter, an INS, and an integrated Kalman filter. The augmented Kalman filter compensates the time-delay of the vehicle's navigation results and the integrated Kalman filter estimates the navigation error of the INS. In order to evaluate performance of the proposed algorithm, vehicle's navigation outputs and IMU measurements were generated using sensors' model-based measurement generator and initial attitude estimation errors of the proposed algorithm and the conventional algorithm without the augmented Kalman filter were compared for the generated measurements. The evaluation results show that the proposed algorithm has better accuracy.

• Journal of Ocean Engineering and Technology
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• v.2 no.2
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• pp.37-45
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• 1988

A dynamically positioned vessel must be capable of maintaining a specified position and direction by controlling the thruster devices. The motions of a vessel are often assuned to tne sum of low frequency(LF)motions and high frequency(HF)motions. The former is mainly due to wind, current and second order wave forces, while the latter is mainly due to first order wave forces. In order to avoid the high frequency thruser modulation, the control system must include filters to estimate the low frequency motions from the measured motion signals, This paper presents a control system based on Kalman filtering technique and optimal control tyeory. Using the combined kalmam filter, LF motion estimates and HF ones are achieved from the motion measurement of the vessel. The estimated low frequency motions are used as inputs to the dynamic positioning system. The thruster modulation is minimized using the optimal control theory; Linear Quadratic Gaussian(LQG)controller. The performances of the Kalman filter and the dynamic positioned vessel are investigated by computer simulation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2348-2360
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• 2023

As science and technology evolve, object detection of moving objects has been widely used in the context of machine learning and artificial intelligence. Traditional moving object detection algorithms, however, are characterized by relatively poor real-time performance and low accuracy in detecting moving objects. To tackle this issue, this manuscript proposes a modified Kalman filter algorithm, which aims to expand the equations of the system with the Taylor series first, ignoring the higher order terms of the second order and above, when the nonlinear system is close to the linear form, then it uses standard Kalman filter algorithms to measure the situation of the system. which can not only detect moving objects accurately but also has better real-time performance and can be employed to predict the trajectory of moving objects. Meanwhile, the accuracy and real-time performance of the algorithm were experimentally verified.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.4
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• pp.47-52
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• 2006

Using the equivalent ARMA model representation of the mixed random errors, we propose Klaman filter design methods for aided INS(Inertial Navigation System) which contains the gyroscope mixed random errors. At first step, considering the characteristic of indirect feedback Kalman filter used in the aided INS, we perform the time difference of equivalent ARMA model. Next, according to the order of the time differenced ARMA model, we achieve the state space conversion of that by two methods. If the order of AR part is greater than MA part, we use controllable or observable canonical form. Otherwise, we establish the state apace equation via the method that several step ahead predicts are included in the state variable, where we can derive high and low order models depending on the variable which is compensated from gyroscope output. At final step, we include the state space equation of gyroscope mixed random errors into aided INS Kalman filter model. Through the simulation, we show that both the high and low order filter models proposed give less navigation errors compared to the conventional filter which assume the mixed random errors as white noise.

• Journal of the Society of Naval Architects of Korea
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• v.40 no.1
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• pp.8-19
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• 2003

Special purpose vessels such as drillship and ocean research vessels install the DPS(Dynamic Positioning System) to maintain the position and heading for long-time operation. This paper deals with the design parameters for the control theory and filter algorithms of DP system. for the environmental loadings wind forces, current forces and wave forces were considered. In order to estimate the low frequency motions without first-order wave motion, the Kalman filter was used and it was assumed that the first-order wave forces correspond to system noises and first-order wave motions are measurement noises. In this simulation, the length of research vessel is 65 meters and it has four thrusters to maintain the position. The ability of keeping position and heading was confirmed. For the calculation of thruster input the LQR and LOI control theory were adopted and the effects of gain were investigated.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.990-994
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• 2003

When the low cost IMU is used, the result of the stationary self alignment is not suitable for navigation. In this paper, an in-motion alignment method is proposed to obtain an accurate initial attitude of a low cost IMU based GPS/INS integration system. To design Kalman filter for alignment, large heading error model is introduced. And then Kalman filter is designed to estimate initial attitude error as the indirect feedback filter. In order to assess performance of the alignment method, computer simulations are carried out. The simulation results show that initial attitude error rapidly reduces.

• Journal of the Korea institute for structural maintenance and inspection
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• v.19 no.4
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• pp.43-48
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• 2015

In this study, a method to enhance the dynamic range of seismic sensor is proposed. The low frequency noise included in the measurement of seismic sensor is modelled as an ARMA(Auto Regressive Moving Average) model and the order and parameters of the model are identified through system identification method. The identified noise model is augmented into Kalmman filter which estimate seismic signal from sensor measurement. The proposed method is applied to a newly developed seismic sensor which is MEMS based 3-axis accelerometer type. The experiment show that the proposed method can enhance the dynamic range compared to the simple low pass filtering.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.184-187
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• 1997

In terms of a system identification, we present a method for autoregressive(AR) modelling of variious biomedical signal. Model order is estimated fly low rank approximation and coefficients are determined by innovation processes of Kalman filter derivation. An application of the method is given for visual evoked potentials.