A new adaptive algorithm based on conditioned normalized LMS method (original) (raw)
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A study of the normalized LMS method with threshold
Electrical Engineering in Japan, 2001
In this paper, we study a modified normalized least mean square (NLMS) algorithm for updating coefficients of an adaptive FIR digital filter (ADF). In the modified adaptive algorithm, filter coefficients are updated with the NLMS algorithm for each sample, but the coefficients are frozen when the input signal is smaller than a given threshold (constant). This modified NLMS has been known, but not been analyzed. In this paper, we call the modified NLMS the conditioned NLMS (C-NLMS) and analyze the convergence characteristics. As a result, the optimum threshold value is obtained. The simulation results and theoretical analyses show the effectiveness of the C-NLMS with the proposed threshold, and a good agreement between both results. The stability of the NLMS algorithm in the presence of a small input signal is improved. The convergence speed of the C-NLMS ADF under noisy circumstances is also faster than that of the unconditioned ordinary NLMS ADF in the small tap number case.
Adaptive Digital FIR Filters: Case Study: Noise Cancellation using LMS Algorithm
Albahit journal of applied sciences, 2021
In this paper, a review has been taken to the previous and the most recent studies and investigations on adaptive digital filter algorithms which based on adaptive noise cancellation systems. In numerous applications of noise cancellation such modern and adaptive systems characteristics that could be very quick in changing the situations which needs the utilization of adaptive filters that quickly changes accordingly. Algorithms such as LMS and RLS proves to be crucial within the noise cancellation are looked into counting guideline and later alterations to extend the merging rate and reduce the computational complexity for future execution. This paper, isn't only as a review of the basic principles on which of the adaptive filters are based uses least mean square LMS algorithm derivation of the least-mean-square (LMS) algorithm; but moreover; It's to implement a case-study of the adaptive filters to solve real-world application problems such as adaptive noise cancellation by implementing the LMS finite impulse response (FIR) adaptive filter using MATLAB, then, investigate of how to choose an appropriate value of convergence factor in order to achieve an efficient LMS adaptive filter.
Combined Adaptive Filter with LMS-Based Algorithms
AEU - International Journal of Electronics and Communications, 2003
A combined adaptive filter is proposed. It consists of parallel LMS-based adaptive FIR filters and an algorithm for choosing the better among them. As a criterion for comparison of the considered algorithms in the proposed filter, we take the ratio between bias and variance of the weighting coefficients. Simulations results confirm the advantages of the proposed adaptive filter.
A Novel Approach to Introducing Adaptive Filters Based on the LMS Algorithm and Its Variants
IEEE Transactions on Education, 2004
This paper presents a new approach to introducing adaptive filters based on the least-mean-square (LMS) algorithm and its variants in an undergraduate course on digital signal processing. Unlike other filters currently taught to undergraduate students, these filters are nonlinear and time variant. This proposal introduces adaptive filtering in the context of a linear timeinvariant system using a real problem. In this way, introducing adaptive filters using concepts already familiar to the students motivates their interest through practical application. The key point for this simplification is that the input to the filter is constant so that the adaptive filter becomes linear. Therefore, a complete arsenal of mathematical tools, already known by the students, is available to analyze the performance of the filters and obtain the key parameters to adaptive filters, e.g., speed of convergence and stability. Several variants of the basic LMS algorithm are described the same way.
Adaptive Filters Design and Analysis Using Least Square and Least Pth Norm
2013
Adaptive filters are considered nonlinear systems; therefore their behavior analysis is more complicated than for fixed filters. As adaptive filters are self-designing filters, their design can be considered less involved than in the case of digital filters with fixed coefficients. This paper presents simulation of Low Pass FIR Adaptive filter using least mean square (LMS) algorithm and least Pth norm algorithm. LMS algorithm is a type of adaptive filter known as stochastic gradient-based algorithms as it utilizes the gradient vector of the filter tap weights to converge on the optimal wiener solution whereas Least Pth does not need to adapt the weighting function involved and no constraints are imposed during the course of optimization. The performance of both approaches is compared.
Design & Implementation of Adaptive Filtering Algorithm using NLMS having Diffferent Targets
this paper presents a review of adaptive algorithms that is LMS (Least mean square) Algorithm and NLMS (Normalised least mean square) algorithm. The adaptive filters NLMS (Normalized Least Mean Square) filter, is the most widely used and simplest to implement. NLMS algorithm has low computational complexity, with good convergence speed which makes this algorithm good for echo cancellation. It has minimum steady state error. Recently, adaptive filtering Algorithms have a trade-off between complexity and the convergence speed. In this, it presents an NLMS filter with different target filters such as FIR and IIR. Also effects of parameters like step size, frequency will also find out. Three performance criteria are used in the study of these algorithms; the minimum mean square error, convergence rate and complexity. Comparison of LMS and NLMS filter will also be proposed. We will use MATLAB for simulation of Adaptive filters.
VLSI Design and Implementation for Adaptive Filter using LMS Algorithm
interscience.in
Adaptive filters, as part of digital signal systems, have been widely used, as well as in applications such as adaptive noise cancellation, adaptive beam forming, channel equalization, and system identification. However, its implementation takes a great deal and becomes a very important field in digital system world. When FPGA (Field Programmable Logic Array) grows in area and provides a lot of facilities to the designers, it becomes an important competitor in the signal processing market. In general FIR structure has been used more successfully than IIR structure in adaptive filters. However, when the adaptive FIR filter was made this required appropriate algorithm to update the filter's coefficients. The algorithm used to update the filter coefficient is the Least Mean Square (LMS) algorithm which is known for its simplification, low computational complexity, and better performance in different running environments. When compared to other algorithms used for implementing adaptive filters the LMS algorithm is seen to perform very well in terms of the number of iterations required for convergence. This phenomenon can be achieved by a sufficient choice of bit length to represent the filter's coefficients. This paper presents a lowcost and high performance programmable digital finite impulse response (FIR) filter. It follows the adaptive algorithm used for the development of the system. The architecture employs the computation sharing algorithm to reduce the computation complexity.
Review on Implementation of Fir Adaptive Filter Using Distributed Arithmatic and Block Lms Algorithm
IJMER
Adaptive filters play very important role in signal processing application. There are several algorithms for implementation of filters such as Least mean square (LMS), Recursive least square (RLS), etc. The LMS algorithm is the most efficient algorithm for implementation of FIR adaptive filters. RLS algorithm gives faster convergence as compared to LMS but the computational complexity is high in case of RLS. An effective distributed arithmetic can be used to implement the block least mean square algorithm (BLMS). The DA based structure uses a LUT sharing scheme to calculate the filter output and weight increment terms of BLMS algorithm. The structure can save a number of adders. This paper presents a literature review on the different algorithms used for implementation of FIR adaptive filters and implementation of filters using distributed arithmetic and block LMS algorithm
Study of Adaptive filters using LMS and Newton-LMS Algorithm
—The paper explore the use of Least Means Square (LMS) and Newton-LMS algorithms for adaptive equalization of a linear dispersive channel that produces unknown distortion. Comparison is made between the rate of convergence of regular LMS and Newton-LMS. Various factors that affect stability of an adaptive filter operation such as the number of iteration, step-size, leakage factor and distortion parameter(Q) were subject of discussion.
A time-dependent LMS algorithm for adaptive filtering
2003
A novel approach for the least-mean-square (LMS) estimation algorithm is proposed. The approach utilizes the conventional LMS algorithm with a time-varying convergence parameter μ n rather than a fixed convergence parameter μ. It is shown that the proposed time-varying LMS algorithm (TV-LMS) provides reduced mean-squared error and also leads to a faster convergence as compared to the conventional fixed parameter LMS algorithm. This paper presents a performance study for the proposed TV-LMS algorithm and other two main adaptive approaches: the least-mean square (LMS) algorithm and the recursive least-squares (RLS) algorithm. These algorithms have been tested for noise reduction and estimation in single-tone sinusoids and nonlinear narrow-band FM signals corrupted by additive white Gaussian noise. The study shows that the TV-LMS algorithm has a computation time close to conventional LMS algorithm with the advantages of faster convergence time and reduced mean-squared error.