• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.3
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• pp.612-621
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• 1996

In this paper, we propose the low sidelobe digital FIR doppler filter bank synthesis algorithm through the Gradient Descent method and it can be practially appliable to coherent pulse doppler radar signal processing. This algorithm shows the appropriate calculation of tap coefficients or zeros for FIR transversal fiter which has been employed in radar signal processor. The span of the filters in the filter bank be selected at the desired position the designer want to locate, and the lower sidelobe level that has equal ripple property is achieved than one for which the conventional weithtedwindow is used. Especially, when we implemented filter zeros as design parameters it is possible to make null filter gain at zero frequency intensionally that would be very efficient for the eliminatio of ground clutter. For the example of 10 tap filter synthesis, when filter coefficients or zeros are selected as design parameters the corresponding sidelobelevel is reducedto -70db or -100db respectively and it has good convergent characteristics to the desired sidelobe reference value. The accuracy ofapproach to the reference value and the speed of convergence that show the performance measure of this algorithm are tuned out with some superiority and the fact that the bandwidth of filter appears small with respect to one which is made by conventional weighted window method is convinced. Since the filter which is synthesized by this algorithm can remove the clutter without loss of target signal it strongly contributes performance improvement with which detection capability would be concerned.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.9A
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• pp.830-837
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• 2005

ISI is caused by delay spread in the multipath channel environment. There are two kinds of channel equalizer: Linear and Non-Linear type according to the structures. In this paper, we propose an improved adaptive linear equalizer to mitigate ISI. The proposed adaptive equalizer is constructed by using asymmetrical Dsmvenu filter based on USE sub-optimal receiver. Asymmetrical structure of the transversal filter is realized by moving the main tap position from center to side. If this structure is used, we can divide ISI to precusor and postcusor. As a result the proposed equalizer has a larger extended compensation range than conventional adaptive linear equalizer. In computer simulation, we compare the bit error rate performance of the proposed linear equalizer with the conventional one on the S-V channel which is modeled for WB systems.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1577-1580
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• 2004

This paper proposes a design and implementation of transversal adaptive digital filter using LMS (Least Mean Squares) adaptive algorithm. The filter structure is based on Distributed Arithmetic (DA) which is able to calculate the inner product by shifting and accumulating of partial products and storing in look-up table, also the desired adaptive digital filter will be multiplierless filter. In addition, the hardware implementation uses VHDL (Very high speed integrated circuit Hardware Description Language) and synthesis using FLEX10K Altera FPGA (Field Programmable Gate Array) as target technology and uses Leonardo Spectrum and MAX+plusII program for overall development. The results of this design are shown that the speed performance and used area of FPGA. The experimental results are presented to demonstrate the feasibility of the desired adaptive digital filter.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.1
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• pp.133-139
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• 2013

Convex combination of two adaptive filters is an efficient method to improve adaptive filter performances. In this paper, a subband convex combination method of two adaptive filters for fast convergence rate in the transient state and low steady state error is presented. The cost function of mixing parameter for a subband convex combination is defined, and from this, the coefficient update equation is derived. Steady state analysis is used to prove the stability of the subband convex combination. Some simulation examples in system identification scenario show the validity of the subband convex combination schemes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.4
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• pp.691-698
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• 2003

We extend the sue of the method of least square to develop a recursive algorithm for the design of adaptive transversal filters such that, given the least-square estimate of this vector of the filter at iteration n-l, we may compute the updated estimate of this vector at iteration n upon the arrival of new data. We begin the development of the RLS algorithm by reviewing some basic relations that pertain to the method of least squares. Then, by exploiting a relation in matrix algebra known as the matrix inversion lemma, we develop the RLS algorithm. An important feature of the RLS algorithm is that it utilizes information contained in the input data, extending back to the instant of time when the algorithm is initiated. In this paper, we propose new tap weight updated RLS algorithm in adaptive transversal filter with data-recycling buffer structure. We prove that convergence speed of learning curve of RLS algorithm with data-recycling buffer is faster than it of exiting RLS algorithm to mean square error versus iteration number. Also the resulting rate of convergence is typically an order of magnitude faster than the simple LMS algorithm. We show that the number of desired sample is portion to increase to converge the specified value from the three dimension simulation result of mean square error according to the degree of channel amplitude distortion and data-recycle buffer number. This improvement of convergence character in performance, is achieved at the B times of convergence speed of mean square error increase in data recycle buffer number with new proposed RLS algorithm.

• 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.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.107-110
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• 2003

본 논문은 LTJ 적응필터의 실용적 구현에 관한 연구이다. 음성코덱(codec)을 사용하는 응용분야에서는 코덱 복호화단의 LPC 계수정보를 얻을 수 있으므로 이를 반사계수로 변환하여 사용하므로서 반사계수 적응에 소용되는 계산량을 감소시킬 수 있으며, 코덱에서는 프레임 또는 서브프레임 단위로 LPC 계수를 적응시키므로 시변 변환 영역 적응필터에 해당하는 LTJ 적응필터의 필터 계수 보상에 필요한 계산량을 감소시킬 수 있다. 실제 음성신호를 사용하여 제안된 실용적 구현 방법의 타당성을 검증하였다.

• Proceedings of the Acoustical Society of Korea Conference
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• 1991.06a
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• pp.28-31
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• 1991

In this paper, it is proposed to improve the robustness of the Fast Recursive Least Squarea(FRLS) algolithms with the exponential weighting, which is an important class of algolithms for adaptive filtering. It is well known that the FRLSalgolithm is numerically unstable with exponential weighting factor λ<1. However, introducing some gains into this algolithms, numerical errors can be reduced. An accurately choice of thegains then leads to a numerically stable FRLS algolithm with a complexity of 8m mulitiplications and we shown it by computer simulations.

• Proceedings of the IEEK Conference
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• 2002.06a
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• pp.65-68
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• 2002

본 논문은 HomePNA 2.0 시스템에서 채널에 의한 왜곡을 보상하기 위한 방안을 제시하는 것으로서 심벌률과 전송 방식에 따른 등화기를 Fast RLS 알고리즘인 FTF (Fast Transversal Filter) 알고리즘을 사용하여 구현하여 그 성능을 분석하며, 또한 헤더 부분의 미결정 심벌들을 이용하는 DFE (Decision Feedback Equalizer)형태로 등화기를 구성하고 이에 대한 성능을 분석하고자한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.11a
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• pp.248-251
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• 1999

We extend the sue of the method of least square to develop a recursive algorithm for the design of adaptive transversal filters such that, given the least-square estimate of this vector of the filter at iteration n-1, we may compute the updated estimate of this vector at i(oration n upon the arrival of new data. The RLS algorithm may be viewed as a special case of the Kalman filter. Indeed this special relationship between the RLS algorithm and the Kalman filter is considered. We begin the development of the RLS algorithm by reviewing some basic relations that pertain to the method of least squares. Then, by exploiting a relation in matrix algebra known as the matrix inversion lemma, we develop the RLS algorithm. An important feature of the RLS algorithm is that it utilizes information contained in the input data, extending back to the instant of time when the algorithm is initiated. The resulting rate of convergence is therefore typically an order of magnitude faster than the simple LMS algorithm. This improvement in performance, however, Is achieved at the expensive of a large increase in computational complexity.