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Tayal, Akash
- The Principles and Applications of Adaptive Filters:Adaptive Noise Cancelling, System Identification and Kalman Tracking
Authors
1 Guru Gobind Singh University, Department of Electronics & Communication Engineering, Delhi 110403, IN
2 Guru Gobind Singh University, department of Electronics & Communication Engineering, Delhi 110403, IN
Source
Digital Signal Processing, Vol 4, No 6 (2012), Pagination: 223-228Abstract
The digital signal processing field provides better solution of problems such as noise or interference cancellation, echo cancellation etc in various applications of communications, signal processing and biomedical. This is essential to remove noise or distortion from the signals. In digital signal processing, adaptive filtering is most significant region to remove noise or distortion. There are number of adaptive algorithms were developed for noise cancellation but LMS and RLS algorithms are more popular than others. This paper presents principles & application of adaptive filtering using different adaptive algorithms and simulation has done at MATLAB platform. This paper shows the concept of adaptive noise cancellation and implements the least mean square (LMS) and recursive least square (RLS) adaptive algorithms for noise cancellation. LMS and RLS algorithms are filter the noise from the input signal and gives noise free output signal. To identify the unknown plant, system modeling is also done in this paper. System identification is done by using LMS, NLMS & RLS Algorithms and also shows comparison graph between them. This paper also presents kalman tracking behavior using RLS. Simulation results shows that the performance of RLS has better adaptive noise cancellation as compared to that of LMS and also shows that RLS has minimum error than LMS & NLMS. The Graph of tracking behavior shows that actual & estimated signal are almost same.
Keywords
Adaptive Filters, LMS, NLMS and RLS.
- Step Size Optimization of LMS Algorithm Using Genetic Algorithm in System Identification
Authors
1 Indira Gandhi Institute of Technology, Guru Gobind Singh Idraprastha University, Delhi, IN
2 ECE Department Indira Gandhi Institute of Technology, Guru Gobind Singh Idraprastha University, Delhi, IN
Source
Digital Signal Processing, Vol 4, No 6 (2012), Pagination: 229-233Abstract
System identification is the art and science of building mathematical models of dynamic systems from observed input-output data. This paper combines Genetic algorithm and LMS algorithm to describe the application of a Genetic Algorithm (GA) to the problem of parameter optimization for an adaptive Finite Impulse Response (FIR) filter. LMS algorithm computes the filter coefficients and GA search the optimal step-size adaptively. Because step-size influences on the stability and performance, so it is necessary to apply method that can control it.. However, the statistical Least Mean Squares method is faster than the genetic algorithm. For this reason we suggest using the genetic algorithm for off-line applications, and the statistical method for on-line adaptation. A hybrid method combining the advantages of both methods is proposed for real world applications. In Genetic algorithm, we have used Roulette wheel Selection, Arithmetic Crossover, Uniform Mutation& .the simulation results of the GA were compared to the traditional fixed step size LMS algorithm.