• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.3
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• pp.237-246
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• 1989

A sliding memory covariance circular lattice (SMC-CL) filter and an efficient ARMA modeling method using the SMC-CL filter are presented. At first, SMC-CL filter is derived based on the geometric approach. Then ARMA process is converted into 2 channel AR process, and SMC-CL filter is applied to it. The structure of SMC-CL filter becomes simpler in case of ARMA modeling due to the whiteness of a driving input process. The parameters of ARMR process can be obtained by the Levinson recursions from the PARCOR coefficients of the second channel of the filter. Computer simulations are performed to show the effctiveness of the proposed algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.262-264
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• 2005

We propose a filter design method to suppress the effect of gyroscope mixed random errors at INS system level. It is based on the result that mixed random errors can be represented by a single equivalent ARMA model. At first step, the time difference of equivalent ARMA process is performed, which consider the characteristic of indirect feedback Kalman filter used in INS filter. Next, a state space conversion of time differenced ARMA model is achieved. If the order of AR is greater than that of MA, the controllable or observable canonical form is used. Otherwise, we introduce the state equation of which the state variable is composed of the ARMA model output and several step ahead predicts of that. At final step, a complete form state equation is presented. The simulation results shows that the proposed method gives less transient error and better convergence compared to the conventional filter which assume the mixed random errors as white noise.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.11 no.1
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• pp.67-79
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• 1992

For effective on-line ARMA parameter estimation, a covariance type ARMA fast transversal filter (FTF) algorithm is presented. The proposed algorithm is a covariance type implementation of ELS(Extended Least Squares) estimator and it is a fast time update recursion which is based on the fact that the correlation matrix of ARMA model satisfies the shift invariance property in each sub-block. The geometric approach is used in the derivation of the proposed algorithm. It takes small computational burden of 13N+37 MADPR(Multiplication And Division Per Recursion). Also, AR and MA orders can be independetly and arbitrarily specified.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.923-926
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• 1992

ARMA fast Transversal filter(FTF) algorithm solves the extended least squres estimation problems in a very efficient way. But unfortunately, it exhibits a very unstable behavior, due to the accumulation of round-off errors. So, in this paper, two effective method to stabilize ARMA FTF algorithm is proposed. They are based on the analysis of the propagation of the numerical errors according to a first order linear model. The proposed methods modify the numerical properties of the variables responsible for the numerical instability, while proeserving the theoretical form of the algorithm. The proposed algorithms still have the nice complexity properties of the original algorithm, but have a much more stable brhavior.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.442-445
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• 1987

In this paper, a new ARMA spectrum estimation algorithm based on Circular Lattice filter is presented. Since APMA model is used in signal modeling, high-resolution spectrum can be obtained. And the computational burden is reduced by using Circular Lattice filter. By modifying the input estimation part of other proposed methods, we can get high-resolution spectrum with less computation and less memory compared with other Lattice methods. Some computer simulations are performed.

• Phonetics and Speech Sciences
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• v.2 no.4
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• pp.145-151
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• 2010

In this paper, a robust feature compensation method is proposed for improving the performance of speech recognition. The proposed method is incorporated into the auto-regressive moving average (ARMA) based feature compensation. We employ variable weights for the ARMA filter according to the degree of speech activity, and pass the normalized cepstral sequence through the weighted ARMA filter. Additionally when normalizing the cepstral sequences in training, the cepstral means and variances are estimated from total training utterances. Experimental results show the proposed method significantly improves the speech recognition performance in the noisy and reverberant environments.

• 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.

• Journal of Biomedical Engineering Research
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• v.15 no.4
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• pp.395-400
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• 1994

High resolution spectral analysis is essential for ECG anaysis. The fast Fourier transform has been widely used for frequency analysis of ECG signals but this procedure provides poor resolution when the data record is short and shows Gibb's phenomena. The ARMA FTF (Fast Transversal Filter) algorithm is used for high resolution spectral analysis. The reason of unsalability of this algorithm is investigated and the method for improving the numerical stability is proposed. The proposed algorithm is applied to spectral analysis of the ECG. Since this result has less variations than the FFT based results, it can be used for the computerized diagonosis of the ECG.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.427-430
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• 1989

For on-line ARMA spectral estimation, the fast transversal filter algorithm of extended least squares method(ETS FTF) is presented. The projection operator, a key tool for geometric approach, is used in the derivation of the algorithm. ELS FTF is a fast time update recursion which is based on the fact that the correlation matrix of ARMA model satisfies the shift invariance property in each block, and thus it takes 10N+31 MADPR.