• Journal of Institute of Control, Robotics and Systems
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• v.19 no.5
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• pp.481-487
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• 2013

This paper presents an FIR (Finite Impulse Response) fixed-interval smoothing filter for fast and exact estimating state variables of a discrete nonlinear system with modeling uncertainty. Conventional IIR (Infinite Impulse Response) filter and smoothing filter can estimate state variables of a system with an exact model when the system is observable. When there is an uncertainty in the system model, however, conventional IIR filter and smoothing filter may cause large errors because the filters cannot estimate the state variables corresponding to the uncertain model exactly. To solve this problem, FIR filters that have fast estimation properties and have robustness to the modeling uncertainty have been developed. However, there is time-delay estimation phenomenon in the FIR filter. The FIR smoothing filter proposed in this paper makes up for the drawbacks of the IIR filter, IIR smoothing filter, and FIR filter. Therefore, the FIR smoothing filter has good estimation performance irrespective of modeling uncertainty. The proposed FIR smoothing filter is applied to the integrated navigation system composed of a magnetic compass based DR (Dead Reckoning) and a GPS (Global Positioning System) receiver. Even when the magnetic compass error that changes largely as the surrounding magnetic field is modeled as a random constant, it is shown that the FIR smoothing filter can estimate the varying magnetic compass error fast and exactly with simulation results.

• ETRI Journal
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• v.34 no.3
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• pp.379-387
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• 2012

In this paper, an error compensation technique for a dead reckoning (DR) system using a magnetic compass module is proposed. The magnetic compass-based azimuth may include a bias that varies with location due to the surrounding magnetic sources. In this paper, the DR system is integrated with a Global Positioning System (GPS) receiver using a finite impulse response (FIR) filter to reduce errors. This filter can estimate the varying bias more effectively than the conventional Kalman filter, which has an infinite impulse response structure. Moreover, the conventional receding horizon Kalman FIR (RHKF) filter is modified for application in nonlinear systems and to compensate the drawbacks of the RHKF filter. The modified RHKF filter is a novel RHKF filter scheme for nonlinear dynamics. The inverse covariance form of the linearized Kalman filter is combined with a receding horizon FIR strategy. This filter is then combined with an extended Kalman filter to enhance the convergence characteristics of the FIR filter. Also, the receding interval is extended to reduce the computational burden. The performance of the proposed DR/GPS integrated system using the modified RHKF filter is evaluated through simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.742-745
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• 1994

In this paper we show the performance of neural Chandrasekhar filtering which is a special case for the new method of neural filtering using the artificial neural network systems developed recently for the filtering problems of linear and nonlinear, stationary and nonstationary stochastic signals. The neurofilter developed has either the finite impulse response(FIR) structure or the infinite impulse response(IIR) structure. The neurofilter differs from the conventional linear digital FIR and IIR filters because the artificial neural network system used in the neurofilter has nonlinear structure due to the sigmoid function. Numerical studies for the estimation of a second order Butterworth process are performed by changing the structures of the neurofilter in order to evaluate the performance indices under the changes of the output noises or disturbances. In the numerical studies both Chandrasekhar filtering estimates and true signals are used as the training signals for the neurofilter. The results obtained from the studies verified the capabilities which are essentially necessary for on-line filtering of various stochastic signals.

• ETRI Journal
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• v.31 no.2
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• pp.162-172
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• 2009

A linear-prediction-based blind equalization algorithm for single-input single-output (SISO) finite impulse response/infinite impulse response (FIR/IIR) channels is proposed. The new algorithm is based on second-order statistics, and it does not require channel order estimation. By oversampling the channel output, the SISO channel model is converted to a special single-input multiple-output (SIMO) model. Two forward linear predictors with consecutive prediction delays are applied to the subchannel outputs of the SIMO model. It is demonstrated that the partial parameters of the SIMO model can be estimated from the difference between the prediction errors when the length of the predictors is sufficiently large. The sufficient filter length for achieving the optimal prediction is also derived. Based on the estimated parameters, both batch and adaptive minimum-mean-square-error equalizers are developed. The performance of the proposed equalizers is evaluated by computer simulations and compared with existing algorithms.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2004.05a
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• pp.386-389
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• 2004

Beamforming for underwater acoustic communication is affected by the broadband feature of underwater acoustic communication signal, which has the low center frequency compared to the signal bandwidth. In this paper, the baseband equivalent array signal model is derived and we present computer simulation results for the broadband finite impulse response (FIR) beamformer performance according to the FIR filter order and the tap spacing. If the FIR filter order is increased above the optimum value, the beamformer performance is degraded. Also the tap spacing is related to the optimum FIR filter order.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.8
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• pp.148-155
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• 2014

In this research a signal processing system is designed for detecting the ultrasonic signal envelope using Xilinx's Zynq SoC(system on chip). As a design tool, Vivado IDE(integrated design environment) is used to hierarchically design the whole signal processing system. The proposed system consists of a Zynq-internal ADC, an FIR(finite impulse response) BPF(band pass filter), an absolute value calculator, an FIR LPF(lpw pass filter), and the Kalman filter. Under this configuration, two design schemes, HW design scheme with LPF as a final stage and HW/SW co-design scheme with a Kalman filter as a final stage, are compared in terms of the performance and efficiency. As a result, envelope detecting performances of the two schemes are proved to be almost same, but the HW/SW co-design is verified to be much more efficient than the HW design considering the much smaller time consumption during system design.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.150-158
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• 2010

Filtering is necessary for the SRC(sample rate converter), that is used to change the sampling rate of a digital signal. The larger the conversion ratio of the sampling rate becomes, the more signal processing is needed for the filter, which means more complexity on realization. Thus it is important to reduce the amount of signal processing for the case of substantial conversion ratios. In this paper it is presented an efficient design method of a multistage FIR(finite impulse response) filter, with which the rate conversion occurs in stages rather than in one step. In this method, filter searching is performed exhaustively over all possible factorization of the conversion ratio, and also the filter complexity is measured based on direct realization rather than on estimation. It has been shown a designed multistage filter to have a less number of multiplications for filtering operation in comparison with a conventionally designed one. It has also been found that by allowing some variations of the filter architecture such as a halfband filter or a filter with multiple transition bands, the number of multiplications can be reduced further.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.10 no.6
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• pp.327-334
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• 1985

A new method on the hardware implementation of multi-channel Finite Impulse Response(FIR) digital filter using vector multiplication structure is proposed. The proposed method can reduce the complexity of hardware structure and improve execution speed. The frequency response of four channel digital filter implemented by the above method is quite agreeable with the frquency response simulated by Remez method.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2196-2199
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• 2003

With the development of digital signal processor(DSP), digital pulse compressor (DPC) is commonly used in radar systems. A DPC is implemented by using finite impulse response(FIR) filter algorithm in time domain or fast Fourier transform(FFT) algorithm in frequency domain. This paper compares the computation complexity tot these two methods and calculates boundary Fm filter taps that determine which of the two methods is better based on computation amount. Also, it shows that the boundary FIR filter taps for DSP, ADSP21060, and those for computation complexity have similar characteristic.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.95-97
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• 2007

In this paper, we propose the optimal lag size which is optimize the performance of the fixed-lag minimum variance FIR smoother. Since the performance of estimation is represented with two Riccati equation and the nonlinear equation of lag size, it is difficult to obtain the optimal lag size. Therefore, we consider the optimal lag size for the scalar system and the numerical example is provided to demonstrate the proposed algorithm.