In this study, Gaussian white noise and color noise of speech signal are reduced by using adaptive filter and soft computing algorithms. Since the main target is noise reduction of speech signal in a car, ambient noise recorded in a BMW750i is used as color noise in the applications. Signal Noise Ratios (SNR) are selected as +5, 0 and -5 dB for white and color noise. Normalized Least Mean Squar...

Availability of channel state information (CSI) at any communication receiver is important for accurate detection and decoding of transmitted signals. Different types of algorithms have been proposed for the estimation of channel impulse response (CIR) of a time varying channel among which are the least mean square (LMS) and the recursive least square (RLS) algorithms. In this paper we propose ...

A new approach for joint data estimation and channel tracking for multiple-input multiple-output (MIMO) channels is proposed based on the decision-directed recursive least squares (DD-RLS) algorithm. RLS algorithm is commonly used for equalization and its application in channel estimation is a novel idea. In this paper, after defining the weighted least squares cost function it is minimized and...

The parallel implementation of the Least Mean Square (LMS) and Recursive Least Square (RLS) adaptive algorithms was investigated to study the scalability and the isoefficiency of these parallel implementations. The analysis includes deriving theoretical expressions for the computation and communication time for the parallel implementation of the adaptive algorithms. These expressions capture th...

This paper presents a new pel recursive motion compensated prediction algorithm for video coding applications. The derivation of the algorithm is based on Recursive Least Squares (RLS) estimation that minimizes the mean square prediction error for each pel (picture element). A comparison with the modified Steepest-descent gradient estimation algorithm shows significant improvement in terms of m...

Adaptive signal processing algorithms derived from LS (least squares) cost functions are known to converge extremely fast and have excellent capabilities to " track " an unknown parameter vector. This paper treats analytically and experimentally the steady-state operation of RLS (recursive least squares) adaptive filters with exponential windows for stationary and nonstationary inputs. A new fo...

This paper proposes an autonomous vehicle trajectory tracking system that fully considers road friction. When intelligent drives at high speed on roads with different friction coefficients, the difficulty of its control lies in fast and accurate identification coefficients. Therefore, improved strategy is designed based traditional recursive least squares (RLS), which utilized for coefficient. ...

Two recursive least-squares (RLS) adaptive filtering algorithms are most often used in practice, the exponential and sliding (rectangular) window RLS algorithms. This popularity is mainly due to existence of low-complexity versions these However, two windows not always best choice for identification fast time-varying systems, when performance important. In this paper, we show how with arbitrary...

In this paper, an asymptotic bias of the recursive least squares (RLS) estimate in the closed loop environment is analyzed and its compensation method is proposed under the assumption that the noise is white. Namely, a bias compensated RLS method in the closed loop environment based on output error (OE) model is proposed. A posteriori error is also analyzed for the estimation of the noise varia...