32,888 research outputs found
Noise Cancellation in Cognitive Radio Systems: A Performance Comparison of Evolutionary Algorithms
Noise cancellation is one of the important signal processing functions of any
communication system, as noise affects data integrity. In existing systems,
traditional filters are used to cancel the noise from the received signals.
These filters use fixed hardware which is capable of filtering specific
frequency or a range of frequencies. However, next generation communication
technologies, such as cognitive radio, will require the use of adaptive filters
that can dynamically reconfigure their filtering parameters for any frequency.
To this end, a few noise cancellation techniques have been proposed, including
least mean squares (LMS) and its variants. However, these algorithms are
susceptible to non-linear noise and fail to locate the global optimum solution
for de-noising. In this paper, we investigate the efficiency of two global
search optimization based algorithms, genetic algorithm and particle swarm
optimization in performing noise cancellation in cognitive radio systems. These
algorithms are implemented and their performances are compared to that of LMS
using bit error rate and mean square error as performance evaluation metrics.
Simulations are performed with additive white Gaussian noise and random
nonlinear noise. Results indicate that GA and PSO perform better than LMS for
the case of AWGN corrupted signal but for non-linear random noise PSO
outperforms the other two algorithms
Numerical Fitting-based Likelihood Calculation to Speed up the Particle Filter
The likelihood calculation of a vast number of particles is the computational
bottleneck for the particle filter in applications where the observation
information is rich. For fast computing the likelihood of particles, a
numerical fitting approach is proposed to construct the Likelihood Probability
Density Function (Li-PDF) by using a comparably small number of so-called
fulcrums. The likelihood of particles is thereby analytically inferred,
explicitly or implicitly, based on the Li-PDF instead of directly computed by
utilizing the observation, which can significantly reduce the computation and
enables real time filtering. The proposed approach guarantees the estimation
quality when an appropriate fitting function and properly distributed fulcrums
are used. The details for construction of the fitting function and fulcrums are
addressed respectively in detail. In particular, to deal with multivariate
fitting, the nonparametric kernel density estimator is presented which is
flexible and convenient for implicit Li-PDF implementation. Simulation
comparison with a variety of existing approaches on a benchmark 1-dimensional
model and multi-dimensional robot localization and visual tracking demonstrate
the validity of our approach.Comment: 42 pages, 17 figures, 4 tables and 1 appendix. This paper is a
draft/preprint of one paper submitted to the IEEE Transaction
The Recovery of Weak Impulsive Signals Based on Stochastic Resonance and Moving Least Squares Fitting
In this paper a stochastic resonance (SR)-based method for recovering weak impulsive signals is developed for quantitative diagnosis of faults in rotating machinery. It was shown in theory that weak impulsive signals follow the mechanism of SR, but the SR produces a nonlinear distortion of the shape of the impulsive signal. To eliminate the distortion a moving least squares fitting method is introduced to reconstruct the signal from the output of the SR process. This proposed method is verified by comparing its detection results with that of a morphological filter based on both simulated and experimental signals. The experimental results show that the background noise is suppressed effectively and the key features of impulsive signals are reconstructed with a good degree of accuracy, which leads to an accurate diagnosis of faults in roller bearings in a run-to failure test
Bibliographic Review on Distributed Kalman Filtering
In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud
The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area
Adaptive System Identification using Markov Chain Monte Carlo
One of the major problems in adaptive filtering is the problem of system
identification. It has been studied extensively due to its immense practical
importance in a variety of fields. The underlying goal is to identify the
impulse response of an unknown system. This is accomplished by placing a known
system in parallel and feeding both systems with the same input. Due to initial
disparity in their impulse responses, an error is generated between their
outputs. This error is set to tune the impulse response of known system in a
way that every change in impulse response reduces the magnitude of prospective
error. This process is repeated until the error becomes negligible and the
responses of both systems match. To specifically minimize the error, numerous
adaptive algorithms are available. They are noteworthy either for their low
computational complexity or high convergence speed. Recently, a method, known
as Markov Chain Monte Carlo (MCMC), has gained much attention due to its
remarkably low computational complexity. But despite this colossal advantage,
properties of MCMC method have not been investigated for adaptive system
identification problem. This article bridges this gap by providing a complete
treatment of MCMC method in the aforementioned context
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