• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.1
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• pp.22-30
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• 2003

A Coordinate-Transformation Extended Robust Kalman Filter (CERKF) designed in the Krein space is proposed, and then applied to a nonlinear incoming ballistic missile tracking system with parameter uncertainties. First, the Extended Robust Kalman filter (ERKF) is proposed to handle the nonlinearity of measurement equation which occurs whenever the polar coordinate system is transformed into the Cartesian coordinate system. Moreover, linearization error inevitably occurs and deteriorates the tracking performance, which is considerably reduced by the proposed CERKF. Through the simulation results, we show that the proposed CERKF, which uses the measurement coordinate system, has less RMS error than the previous ERKF which is designed in the Krein space using the Cartesian system. We also verify that the robustness and the stability of the proposed filter are guaranteed in two radars: the phased way radar and the scanning radar

• International Journal of Aeronautical and Space Sciences
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• v.1 no.1
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• pp.70-80
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• 2000

The attitude knowledge error model is formulated for specifically KOMPSAT attitude determination system using the Lefferts/Markley/Shuster method, and the attitude determination(AD) error analysis is performed so as to investgate the on-board attitude determination capability of KOrea Multi-Purpose SATellite(KOMPSAT) using the covariance analysis method. Analysis results show there is almost no initial value effect on Attitude Determination (AD) error and the sensor noise effects on AD error are drastically decreased as is predicted because of the inherent characteristic of Kalman filter structure. However, it shows that the earth radiance effect of IR-sensor(earth sensor) and the bias effects of both IR-sensor and fine sun sensor are the dominant factors degrading AD error and gyro rate bias estimate error in AD system. Analysis results show that the attitude determination errors of roll, pitch and yaw axes are 0.056, 0.092 and 0.093 degrees, respectively. These numbers are smaller than the required values for the normal mission of KOMPSAT. Also, the selected on-orbit data of KOMPSAT is presented to demonstrate the designed AD system.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.299-303
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• 1993

In an underwater vehicle, the navigation error is mostly caused by the initial misalignment, the bias of a gyro and an accelerometer, and the sea current. Therefore, it is important that these error sources are estimated and compensated in order to reduce the navigation error. In this paper, the E.M.Log aided SDINS is designed by using the E.M.Log which measures the forward velocity of a vehicle. And the system error state equation and the measurement equation are derived and the suboptimal Kalman Filter is established for this aided SDINS. The simulation result showed that this had an important role in estimating and compensating these error sources, thus reducing the navigation error of an underwater vehicle.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.1
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• pp.1-7
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• 2021

In this paper, we presented the experimental basis for the theoretical background and robustness of the Convolutional Neural Network for object recognition based on artificial intelligence. An experimental result was performed to visualize the weighting filters and feature maps for each layer to determine what characteristics CNN is automatically generating. experimental results were presented on the trend of learning error and identification error rate by checking the relevance of the weight filter and feature map for learning error and identification error. The weighting filter and characteristic map are presented as experimental results. The automatically generated characteristic quantities presented the results of error rates for moving and rotating robustness to geometric changes.

• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.6
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• pp.126-131
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• 2012

Tire force is one of important parameters which determine vehicle dynamics. However, it is hard to measure tire force directly through sensors. Not only the sensor is expensive but also installation of sensors on harsh environments is difficult. Therefore, estimation algorithms based on vehicle dynamic models are introduced to estimate the tire forces indirectly. In this paper, an estimation system for estimating lateral force and states is suggested. The state-space equation is constructed based on the 3-DOF bicycle model. Extended Kalman Filter, Unscented Kalman Filter and Ensemble Kalman Filter are used for estimating states on the nonlinear system. Performance of each algorithm is evaluated in terms of RMSE (Root Mean Square Error) and maximum error.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.65 no.3
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• pp.171-177
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• 2016

Noise problem that occurs in living environment is a big trouble in the economic, social and environmental aspects. In this paper, the filtered-X LMS algorithms, the adjoint LMS algorithms, and the robust adjoint LMS algorithms will be introduced for applications in active noise control(ANC). The filtered-X LMS algorithms is currently the most popular method for adapting a filter when the filter exits a transfer function in the error path. The adjoint LMS algorithms, that prefilter the error signals instead of divided reference signals in frequency band, is also used for adaptive filter algorithms to reduce the computational burden of multi-channel ANC systems such as the 3D space. To improve performance of the adjoint LMS ANC system, an off-line measured transfer function is connected parallel to the LMS filter. This parallel-fixed filter acts as a noise controller only when the LMS filter is abnormal condition. The superior performance of the proposed system was compared through simulation with the adjoint LMS ANC system when the adaptive filter is in normal and abnormal condition.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.10
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• pp.175-185
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• 1998

In this paper, we studied some design methodologies of typical FIR filters based on the peak-error constrained least square criterion which was first introducedd by Adams in 1991. This method is a mixed type of the classical least squared error method(LSM) and the so-called min-max error method (MMM). And by considering both the least squared error as well as the maximum error, the solution, i.e. the impulse response of the filter, can be found only when the restrictions on maximum gain, transition bandwidth, and the squared error are satisfied simultaneously under some trade-off conditions. We used the multiple exchange algorithms for optimization procedure and applied the design methodology to the cases of the multiband filter, the differentiator, and the Hilbert transformer by taking the balance of two design criteria into account. The results show that the peak-error constrained least weighted square error design method(PLEM) is superior in performance to the existing LSM and MMM from both the squared error and the maximum error standpoints. And it is verified that PLEM can be applied to not only the case of simple low pass filter, but also to various types of FIR filters.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.18 no.8
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• pp.739-746
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• 2005

When we design a binary phase holographic optical low-pass filter (HOLF), the phase difference is generally set to be $\pi$ to optimize the diffraction efficiency. However, the phase difference of real HOLF mostly deviate from $\pi$ by the error in the fabrication process. The deviation causes the (0,0)-th order diffracted beam to increase, which results In raising the diffraction efficiency. To study the effects of the phase error on the performance of HOLF, we calculated the MTF of HOLF for various phase differences. The results show that the phase error of 10 $\%$ makes little change in the filtering characteristics of HOLF. Considering the filtering by lens and CCD, the effects of the phase error becomes much smaller. To confirm it experimentally, we fabricated HOLFs for various phase differences. After installing it in a digital camera, we take picture of test targets and observe the Moire fringes and the resolution. The results agree with our prediction.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.329-334
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• 1998

This work is concerned with the problem of tracking a maneuvering target. In this paper, an error monitoring and recovery method of perception net is utilized to improve tracking performance for a highly maneuvering tar-get. Many researches have been performed in tracking a maneuvering target. The conventional Interacting Multiple Model (IMM) filter is well known as a suboptimal hybrid filter that has been shown to be one of the most cost-effective hybrid state estimation scheme. The subfilters of IMM can be considered as fusing its initial value with new measurements. This approach is also shown in this paper. Perception net based error monitoring and recovery technique, which is a kind of geometric data fusion, makes it possible to monitor errors and to calibrate possible biases involved in sensed data and extracted features. Both detecting a maneuvering target and compensating the estimated state can be achieved by employing the properly implemented error monitoring and recovery technique. The IMM filter which employing the error monitoring and recovery technique shows good tracking performance for a highly maneuvering target as well as it reduces maximum values of estimation errors when maneuvering starts and finishes. The effectiveness of the pro-posed method is validated through simulation by comparing it with the conventional IMM algorithm.

• Journal of Broadcast Engineering
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• v.10 no.4 s.29
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• pp.540-547
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• 2005

In the DFE(Decision Feedback Equalizer) for ATSC DTV receivers, there are decision errors in the slicer or. the simplified trellis decoder, and these decided false data comes to the feedback filter to make the error propagation phenomenon. The error propagation degrades the equalizer performance by increasing residual errors as well as slowing down the convergence rate. In this paper we propose a dual-feedback equalization structure. There are two feedback filters. One is the decision feedback filter which uses the simplified trellis decoder output data, the other is non-decision feedback filter which uses the equalizer output data. The additional non-decision feedback filter doesn't introduce the error propagation, so it can compensate the error propagation. The proposed structure accelerates the convergence rate as well as reduces output men-square error(MSE). We analyzed the performance enhancement of DTV receiver using dual-feedback equalization structure.