• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.9
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• pp.1141-1145
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• 2016

In general, a variable forgetting factor is applied to the RLS algorithm for the time-varying parameter estimation in the non-stationary environments. The introduction of a variable forgetting factor to RLS needs heavy additional calculation complexity. We propose a new Gauss Newton variable forgetting factor RLS algorithm which needs small amount of calculation as well as estimates the better parameters in time-varying nonstationary environment. The algorithm performs as good as the conventional Gauss Newton variable forgetting factor RLS and the required additional calculation complexity reduces from $O(N^2)$ to O(N).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.9
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• pp.1793-1801
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• 2015

In a recent work, kernel recursive least-squares tracker (KRLS-T) algorithm has been proposed. It is capable of tracking in non-stationary environments using a forgetting mechanism built on a Bayesian framework. The forgetting mechanism in KRLS-T is implemented by a fixed forgetting factor. In practice, however, we frequently meet that the fixed forgetting factor cannot handle time-varying system effectively. In this paper we propose a new KRLS-T with a variable forgetting factor. Experimental results show that proposed algorithm can handle time-varying system more effectively than the KRLS-T.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.305-308
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• 1988

Pole assignment controller with variable forgetting factor is generalizaed to allow the output and/or input variance to be reduced. The algorithm can give significant reductions in variance for little extra computational effort and is presented for servo-tracking using leat-squares estimation. Moreover, the use of a variable forgetting factor with correct choice of information bound can avoid 'blowing-up' of the covariance matrix of the estimates and subsequent unstable control.

• The Journal of the Acoustical Society of Korea
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• v.38 no.3
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• pp.283-289
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• 2019

RLS (Recursive-least-squares) algorithm is known to have good convergence and excellent error level after convergence. However, there is a disadvantage that numerical instability is included in the algorithm due to inverse matrix calculation. In this paper, we propose an algorithm with no matrix inversion to avoid the instability aforementioned. The proposed algorithm still keeps the same convergence performance. In the proposed algorithm, we adopt an averaged gradient-based step size as a self-adjusted step size. In addition, a variable forgetting factor is introduced to provide superior performance for time-varying channel estimation. Through simulations, we compare performance with conventional RLS and show its equivalency. It also shows the merit of the variable forgetting factor in time-varying channels.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.177-187
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• 2014

This paper proposes a fast subspace tracking methods, which is called GVFF FAPI, based on FAPI (Fast Approximated Power Iteration) method and GVFF RLS (Gradient-based Variable Forgetting Factor Recursive Lease Squares). Since the conventional FAPI uses a constant forgetting factor for estimating covariance matrix of source signals, it has difficulty in applying to non-stationary environments such as continuously changing DOAs of source signals. To overcome the drawback of conventioanl FAPI method, the GVFF FAPI uses the gradient-based variable forgetting factor derived from an improved means square error (MSE) analysis of RLS. In order to achieve the decreased subspace error in non-stationary environments, the GVFF-FAPI algorithm used an improved forgetting factor updating equation that can produce a fast decreasing forgetting factor when the gradient is positive and a slowly increasing forgetting factor when the gradient is negative. Our numerical simulations show that GVFF-FAPI algorithm offers lower subspace error and RMSE (Root Mean Square Error) of tracked DOAs of source signals than conventional FAPI based MUSIC (MUltiple SIgnal Classification).

• The Journal of the Acoustical Society of Korea
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• v.20 no.1E
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• pp.7-13
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• 2001

In this paper we propose a new subspace tracking algorithm based on the PASTd (Projection Approximation Subspace Tracking with deflation). The algorithm is obtained via introducing the variable forgetting factor which adapts itself to the time-varying subspace environments. The tracking capability of the proposed algorithm is demonstrated by computer simulations in an abruptly changing DOA scenario. The estimation results of the variable forgetting factor PASTd(VFF-PASTd) outperform those of the PASTd in the nonstationary case as well as in the stationary case.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.173-176
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• 2000

시변 신호 추적 특성을 향상시키기 위하여, Gauss-Newton Variable Forgetting Factor RLS (GN-VFF-RLS) Algorithm을 제안한다. 최적화된 망각인자를 가정한 기존의 RLS 알고리듬과 비교하여, 제안된 방법은 특히 신호의 변화가 급격히 일어날 경우 주목할만한 추정 성능의 향상을 보여준다. 제안된 알고리듬의 시변 추정 특성을 신호 대 잡음비와 시변 정도에 대하여 모의 실험하고 기존의 추정 알고리듬들과 비교한다.

• The Journal of the Acoustical Society of Korea
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• v.22 no.1E
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• pp.19-25
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• 2003

Ryu et al. recently proposed a multiple target angle-tracking algorithm without a data association problem. This algorithm, however, shows the degraded performance on evasive maneuvering targets, because the estimated signal subspace is d,:graded in the algorithm. In this Paper, we proposed a new algorithm, in which VFF-PASTd (Variable Forgetting Factor PASTd) algorithm is applied to Ryu's algorithm to effectively handle the evasive target tracking with better time-varying signal subspace.

• The Journal of the Acoustical Society of Korea
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• v.33 no.5
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• pp.326-334
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• 2014

This paper proposes a novel covariance matrix estimation scheme for minimum variance distortionless response (MVDR) beamforming. By accurately tracking direction-of-sound source arrival (DoA) information using audio-visual sensors, the covariance matrix is efficiently estimated by adopting a variable forgetting factor. The variable forgetting factor is determined by considering signal-to-interference ratio (SIR). Experimental results verify that the performance of the proposed method is superior to that of the conventional one in terms of interference/noise reduction and speech distortion.

• The Journal of the Acoustical Society of Korea
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• v.23 no.2E
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• pp.40-43
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• 2004

In moving jammer environments, the performance of conventional adaptive beamformer is severely degraded and the robust adaptive beamformer requires additional sensors to obtain desired performances. Therefore, it is necessary to develop efficient algorithm without any additional requirement of the number of sensors, etc. In this paper, we introduce a fast adaptive algorithm with variable forgetting factor, which does not have any additional requirements. From the computer simulations, we obtain the better performances than those of other techniques for the arrays with various aperture lengths.