JaeWook Shin

Variable Step-Size Sign Subband Adaptive Filter

This letter proposes a variable step-size sign subband adaptive filter (SSAF) based on the minimization of mean-square deviation (MSD). In the process of minimizing the MSD, because it is not feasible to know the exact value of the MSD, the step size is derived by minimizing the upper bound of the MSD in each iteration. The proposed algorithm uses this step size in the SSAF update equation so as to improve the filter performance in terms of the convergence rate and the steady-state estimation error. The proposed algorithm is tested in a system-identification scenario that includes impulsive noise. Simulation results show that the proposed algorithm performs better than the previous algorithms.

Variable Step-Size Affine Projection Sign Algorithm

This brief proposes a novel variable step-size affine projection sign algorithm (APSA), which is characterized by its robustness against impulsive noises. To obtain a step size reasonably, the proposed algorithm investigates the mean-square deviation (MSD) of APSA. Because it is impossible to accurately compute the MSD of APSA, the proposed algorithm derives the upper bound of the MSD using the upper bound of the L1-norm of the measurement noise. The optimal step size is calculated at each iteration by minimizing the upper bound of the MSD, which improves the filter performance, with respect to the convergence rate and the steady-state estimation error. The simulation results demonstrate that the proposed algorithm improves the filter performance in a system-identification scenario in the presence of impulsive noises.

Fast communication: A band-dependent variable step-size sign subband adaptive filter

This letter proposes a band-dependent variable step-size sign subband adaptive filter using the concept of mean-square deviation (MSD) minimization. Since it is difficult to obtain the value of the MSD accurately, the proposed step size is derived by minimizing the upper bound of the conditional MSD with given input. By assigning the different step size in each band, the filter performance can be improved. Moreover, we suggest the estimation method of the measurement-noise variance in an impulsive-noise environment, because the proposed algorithm needs the measurement-noise variance to calculate the step size. The reset algorithm is also applied for maintaining the filter performance when a system change occurs suddenly. Simulation results demonstrate that the proposed algorithm performs better than the existing algorithms in aspects of the convergence rate and the steady-state estimation error.

An Improved NLMS Algorithm in Sparse Systems Against Noisy Input Signals

This brief proposes a novel normalized least mean square algorithm that is characterized by robustness against noisy input signals. To compensate for the bias caused by the input noise that is added at the filter input, a derivation method based on reasonable assumptions finds a bias-compensating vector. Moreover, the proposed algorithm has a fast convergence rate when applied to sparse systems, owing to its L0-norm cost in the proposed update equation. The simulation results verify that the proposed algorithm improves the performance of the filter, in terms of system identification in sparse systems, in the presence of noisy input signals.

A two-stage affine projection algorithm with mean-square-error-matching step-sizes

This paper proposes a two-stage affine projection algorithm (APA) with different projection orders and step-sizes. The proposed algorithm has a high projection order and a fixed step-size to achieve fast convergence rate at the first stage and a low projection order and a variable step-size to achieve small steady-state estimation errors at the second stage. The stage transition moment from the first to the second stage is determined by examining, from a stochastic point of view, whether the current error reaches the steady-state value. Moreover, in order to prevent the sudden drop of convergence rate on switching from a high projection order to a low projection order, a matching step-size method has been introduced to determine the initial step-size of the second stage by matching the mean-square errors (MSEs) before and after the transition moment. In order to continuously reduce steady-state estimation errors, the proposed algorithm adjusts the step-size of the second stage by employing a simple algorithm. Because of the reduced projection orders and variable step-size in the steady-state, the algorithm achieves improved performance as well as extremely low computational complexity as compared to the existing APAs with selective input vectors and APAs with variable step-size.

Variable step-size sign algorithm against impulsive noises

This paper proposes a new variable step-size sign algorithm through the minimisation of mean-square deviation (MSD). As it is difficult to obtain the MSD accurately, the upper bound of the MSD is derived for calculating the step size at each iteration. The proposed algorithm is not only robust to impulsive noises, but also has improved filter performance in aspects of convergence rate and steady-state estimation error owing to the proposed variable step-size strategy. The simulation results verify that the proposed algorithm has better performance than the existing algorithms in a system-identification scenario in the presence of impulsive noises.

Direction and Location Estimating Algorithm for Sound Sources with Two Hydrophones in Underwater Environment

Abstract: For underwater vehicles, the use of sensors such as cameras and laser scanners is limited by the difference in environment compared to robots designed to work on dry land. In underwater environments, if use is made of sound signals, valuable information can be obtained. The most important application is the localization of underwater sound sources. The estimated location of a sound source can be used to control underwater robots or submarines. Thus, the purpose of this research is to estimate the source’s direction and location in a noisy underwater environment. The direction of the sound source is obtained using two hydrophones. Furthermore, if we assume that the robot or sound source is moving, the location of the sound source is estimated using more than two estimated directions. The feasibility of the developed algorithm is examined by experiments in a water tank and in the ocean. Keywords: hydrophone, time-delay estimation, generalized cross correlation, localization, particle filter I. 서론 주어진 임무수행을 위해 목표지점까지 정확하게 이동 할 수 있는 자율이동이 가능한 로봇의 개발을 위해 주변환경을 인식하고 로봇의 현재 위치를 추정하는 방법의 연구가 활발히 이루어 지고 있다. 이러한 방법들은 주로 초음파 센서, GPS, Laser Range Finder, 카메라, 마이크, RFID와 같은 센서를 사용하거나 여러 가지 센서 데이터들을 퓨전하여 사용한다[1-5]. 지상과 공중에서 동작하는 로봇들은 위에서 나열한 센서들을 큰 제약조건 없이 사용이 가능하지만 수중에서 작동하는 로봇이나 잠수정의 경우에는 사용할 수 있는 센서들이 음향관련 센서들과 관성센서 정도로 굉장히 제한적이다. 수중환경에서 음향신호를 활용하여 사용자로 하여금 주변의 환경적 변화 또는 로봇이나 잠수정의 운행상태를 추정하는데 도움을 주거나 주어진 신호에 대한 방위각 추정을 통해 신호발생기의 위치를 추정 할 수 있다. 수중 음원의 방위각을 추정하기 위해 여러 가지 방법들은 제안되었다. 그 중 가장 널리 사용되고 있는 방법은 TDE (Time-Delay Estimation)와 SBF (Steered Beamforming) 이다. 두 방법은 측정되는 음향데이터를 음원 위치일 때 최고 값을 가지는 함수로 변환하여 음원의 방향을 추정한다. 하지만 이러한 직접적인 방법은 수중에서 발생하는 잡음신호와 반사파의 영향에 따라 성능저하가 발생하게 된다. 따라서 본 논문에서는 반사파와 잡음신호가 존재하는 환경에서도 성능저하를 줄이기 위해 음원 방향을 추정하기 위한 파티클 필터(Particle filter) 방법을 제안한다. 제안하는 파티클 필터는 likelihood function을 생성하기 위해 GCC (Generalized Cross Correaltion) 함수를 사용하고 일반적인 TDE 방법과 달리 최고값 하나만을 사용하는 것이 아니라 여러 peak 값을 사용하여 반사파와 잡음신호의 영향을 줄인다. 또한 제안된 파티클 필터를 이용하여 얻어진 음원의 방위각과 음원 또는 로봇의 모션 정보를 이용하여 음원과 로봇의 상대위치를 추정하는 방법을 제안한다. II. 수중 음원의 방향 추정 1. GCC (Generalized Cross Correlation) 두 하이드로폰을 통해 얻어진 음향신호의 시간차를 추정하는 방법은 여러 가지가 있지만 그 중 가장 일반적으로 사용되는 알고리즘은 GCC이다. GCC는 두 입력신호의 Cross Correlation을30, 2013 이용하여 신호의 시간차이를 추정하는 방법으로 계산이 간단하기 때문에 시스템의 복잡도가 낮아도 사용이 가능하다[6]. 하지만 실제 수중 음원 신호의 방향을 추정함에 있어서 발생하는 여러 가지 문제점으로 GCC의 성능이 저하된다. 그 중 가장 대표적인 것이 반사파와 잡음신호이다. 수중 음원에서 발생된 신호가 물의 표면이나 바닥 면에 도달한 경우 반사되어 하이드로폰으로 입력될 수 있다. 이렇게 반사된 신호

An Affine Projection Algorithm With Update-Interval Selection

This paper presents a mean-square deviation (MSD) analysis of the periodic affine projection algorithm (P-APA) and two update-interval selection methods to achieve improved performance in terms of the convergence and the steady-state error. The MSD analysis of the P-APA considers the correlation between the weight error vector and the measurement noise vector. Using this analysis, it is verified that the update interval governs the trade-off between the convergence rate and the steady-state errors in the P-APA. To overcome this drawback, the proposed APAs increase the update interval when the adaptive filter reaches the steady state. Consequently, these algorithms can reduce the overall computational complexity. The simulation results show that the proposed APAs perform better than the previous algorithms.

A variable step-size diffusion affine projection algorithm

This paper presents a new variable step-size diffusion affine projection algorithm VSS-DAPA to advance the filter performance of the diffusion affine projection algorithm DAPA. The proposed VSS strategy is developed for the DAPA, which can solve the distributed estimation problem over diffusion networks well. To obtain the optimal step size reasonably, we seek the update recursion of mean-square deviation MSD that is suitable for the DAPA. The step size is optimally given through the minimization for the MSD of the DAPA at each iteration. The derived step size through the MSD minimization improves the filter performance with respect to the convergence and the estimation error in steady state. The results based on simulations demonstrate that the proposed VSS-DAPA performs better than the existing algorithms with a system-identification scenario in diffusion network. Copyright

Adaptive regularisation for normalised subband adaptive filter: mean-square performance analysis approach

The normalised subband adaptive filter (NSAF) is a useful adaptive filter, which improves the convergence rate compared with the normalised least mean-square algorithm. Most analytical results of the NSAF set the regularisation parameter set to zero or present only steady-state mean-square error performance of the regularised NSAF (e-NSAF). This study presents a mean-square performance analysis of e-NSAF, which analyses not only convergence behaviour but also steady-state behaviour. Furthermore, a novel adaptive regularisation for NSAF (AR-NSAF) is also developed based on the proposed analysis approach. The proposed AR-NSAF selects the optimal regularisation parameter that leads to improving the performance of the adaptive filter. Simulation results comparing the proposed analytical results with the results achieved from the simulation are presented. In addition, these results verify that the proposed AR-NSAF outperforms the previous algorithms in a system-identification and acoustic echo-cancellation scenarios.