Discrete pdf estimation in the presence of noise

The problem of estimating a pdf from measurements has been widely studied by many researchers. However, most of the work was focused on estimating a probability density function of continuous random variables, especially in the absence of noise. In this paper, we consider a model for representing discrete probability density functions based on multirate dsp models. Using this model, we propose an efficient and stable scheme for pdf estimation when the measurements are corrupted by independent additive noise. This approach makes use of well-known results from multirate dsp theory, especially that of biorthogonal partners. Simulation results are given, which clearly show the advantage of the proposed method

Channel Parameters Estimation Algorithm Based on The Characteristic Function under Impulse Noise Environment

Under communication environments, such as wireless sensor networks, the noise observed usually exhibits impulsive as well as Gaussian characteristics. In the initialization of channel iterative decoder, such as low density parity check codes, it is required in advance to estimate the channel parameters to obtain the prior information from the received signals. In this paper, a blind channel parameters estimator under impulsive noise environment is proposed, which is based on the empirical characteristic function in MPSK/MQAM higher-order modulation system. Simulation results show that for various MPSK/MQAM modulations, the estimator can obtain a more accurate unbiased estimation even though we do not know which kind of higher-order modulation is used

Studies in Signal Processing Techniques for Speech Enhancement: A comparative study

Speech enhancement is very essential to suppress the background noise and to increase speech intelligibility and reduce fatigue in hearing. There exist many simple speech enhancement algorithms like spectral subtraction to complex algorithms like Bayesian Magnitude estimators based on Minimum Mean Square Error (MMSE) and its variants. A continuous research is going and new algorithms are emerging to enhance speech signal recorded in the background of environment such as industries, vehicles and aircraft cockpit. In aviation industries speech enhancement plays a vital role to bring crucial information from pilot’s conversation in case of an incident or accident by suppressing engine and other cockpit instrument noises. In this work proposed is a new approach to speech enhancement making use harmonic wavelet transform and Bayesian estimators. The performance indicators, SNR and listening confirms to the fact that newly modified algorithms using harmonic wavelet transform indeed show better results than currently existing methods. Further, the Harmonic Wavelet Transform is computationally efficient and simple to implement due to its inbuilt decimation-interpolation operations compared to those of filter-bank approach to realize sub-bands

Statistical properties of a filtered Poisson process with additive random noise: Distributions, correlations and moment estimation

Filtered Poisson processes are often used as reference models for intermittent fluc- tuations in physical systems. Such a process is here extended by adding a noise term, either as a purely additive term to the process or as a dynamical term in a stochastic differential equation. The lowest order moments, probability density function, auto-correlation function and power spectral density are derived and used to identify and compare the effects of the two different noise terms. Monte-Carlo studies of synthetic time series are used to investigate the accuracy of model pa- rameter estimation and to identify methods for distinguishing the noise types. It is shown that the probability density function and the three lowest order moments provide accurate estimations of the parameters, but are unable to separate the noise types. The auto-correlation function and the power spectral density also provide methods for estimating the model parameters, as well as being capable of identifying the noise type. The number of times the signal crosses a prescribed threshold level in the positive direction also promises to be able to differentiate the noise type.Comment: 34 pages, 25 figure

Constellation Optimization in the Presence of Strong Phase Noise

In this paper, we address the problem of optimizing signal constellations for strong phase noise. The problem is investigated by considering three optimization formulations, which provide an analytical framework for constellation design. In the first formulation, we seek to design constellations that minimize the symbol error probability (SEP) for an approximate ML detector in the presence of phase noise. In the second formulation, we optimize constellations in terms of mutual information (MI) for the effective discrete channel consisting of phase noise, additive white Gaussian noise, and the approximate ML detector. To this end, we derive the MI of this discrete channel. Finally, we optimize constellations in terms of the MI for the phase noise channel. We give two analytical characterizations of the MI of this channel, which are shown to be accurate for a wide range of signal-to-noise ratios and phase noise variances. For each formulation, we present a detailed analysis of the optimal constellations and their performance in the presence of strong phase noise. We show that the optimal constellations significantly outperform conventional constellations and those proposed in the literature in terms of SEP, error floors, and MI.Comment: 10 page, 10 figures, Accepted to IEEE Trans. Commu