Ju-man Song

An optimal variable step-size affine projection algorithm for the modified filtered-x active noise control

Abstract This paper introduces an optimal variable step-size affine projection algorithm for the modified filtered-x active noise control systems. First, the recursion form of the error covariance from the tap weight update equation is constructed, not ignoring the dependency between the estimation error and the secondary noise signal. Such consideration has not been concerned previously for the analysis of the modified filtered-x affine projection algorithm. Second, a recursion form of the mean square deviation is derived from that of the error covariance. From the recursion form, an optimal step size is decided to get the fastest convergence rate. Both the recursion forms of the mean square deviation and the optimal step size require scalar additions and multiplications that do not contribute to the overall complexity seriously. The simulation results on the active noise control environments show both fast convergence rate and low steady-state error.

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

A Diffusion Strategy for the Multichannel Active Noise Control System in Distributed Network

This paper introduces a diffusion strategy for the multichannel active noise control. The diffusion strategy is designed to reduce the computational complexity by distributes computations to all nodes of multichannel active noise control system. Thus, the multichannel filtered-x normalized least mean square algorithm, which is the simplest way for real active noise control environments is used as a base algorithm for the diffusion strategy. From the structure of the multichannel active noise control system, the optimal weight vector at a node of the proposed strategy is calculated by considering the weight vector of neighbor nodes. With the proposed strategy, the computational complexity is distributed from one main processor to all nodes. With some simulation results, the performance of the proposed strategy is shown with the reduced computational complexity for one processor.

Estimation of Acid Concentration Model of Cooling and Pickling Process Using Volterra Series Inputs

This paper deals with estimating the acid concentration of pickling process using the Volterra inputs. To estimate the acid concentration, the whole pickling process is represented by the grey box model consists of the white box dealing with known system and the black box dealing with unknown system. Because there is a possibility of nonlinear term in the unknown system, the Volterra series are used to estimate the acid concentration. For the white box modeling, the acid tank solution level and concentration equations are used, and for the black box modeling, the acid concentration is estimated using the Volterra Least Mean Squares (LMS) algorithm and Least Squares (LS) algorithm. The LMS algorithm has the advantage of the simple structure and the low computation, and the LS algorithm has the advantage of lowest error. The simulation results compared to the measured data are included.

Non-periodic-partial-update affine projection algorithm with data-selective updating

This paper proposes a non-periodic-partial-update affine projection algorithm with data-selective updating. The proposed algorithm employs two update concepts: non-periodic partial update and data-selective update. The former plays a role in adjusting the length of the update period, and the latter in reducing computational complexity. Thus, the algorithm requires two key procedures of length assignment and state decision. The length assignment procedure determines the length of the update period by checking whether the current input vectors have enough information with an update period assignment criterion. The state decision procedure stochastically determines whether the adaptive filter has reached a steady state. When the current state of the adaptive filter is confirmed as a transient state by the decision procedure, the algorithm updates all filter coefficients with an update period assigned by the length assignment procedure. Through these two procedures, the proposed algorithm not only achieves good performance, especially for colored input signals, in terms of the convergence rate and steady-state estimation errors but also provides a substantial reduction in the number of updates.

An evolving update interval algorithm for the optimal step-size affine projection algorithm

This paper introduces an evolving update interval algorithm for the optimal step-size affine projection algorithm. The optimal step-size affine projection algorithm is one of numerous approaches to get better performance for the affine projection algorithm. It is suggested by analyzing the mean square deviation of fixed step-size affine projection algorithm. With the optimal step-size affine projection algorithm, in this paper, by evolving the update interval, it is able to show much better performance. From the MSD analysis, the learning curve is dived into two stage: the transient stage and the steady-state. By finding the cross point of affine projection algorithms learning curve, the update interval is modified. By updating the weight vector for updated interval, the proposed algorithm reduces the computational complexity. With the proposed algorithm from simulations, it shows higher convergence rate and lower steady-state error.