scholarly journals Detection of MDPSK in Fading Channels using Gaussian Particle Filter

2014 ◽  
pp. 76-79
Author(s):  
P. Sudhakar ◽  
P. Gopi Krishna ◽  
Dr. D. Elizabeth Rani
Keyword(s):  
Particle Filter ◽  
Fading Channels ◽  
Posterior Distribution ◽  
Particle Filtering ◽  
Sampling Process ◽  
Non Linear ◽  
Linear Problems ◽  
Filtering Approach

We propose Gaussian particle filtering approach for solving the problem of corrupting the MDPSK signals by fading as well as noise. Particle filtering is a powerful tool for non linear problems but it faces sampling degeneration problem which leads to re‐sampling process. Gaussian Particle Filtering doesn’t need re‐sampling process because it approximates the posterior distribution as Gaussian.GPF is preferable than PF for fading channels.

An Improved Particle Filtering Algorithm Based on Mean Shift Algorithm for Infrared Target Tracking

2014 ◽  
Vol 1079-1080 ◽  
pp. 650-653
Author(s):  
Xi Peng Yin ◽  
Lin Lin Xia ◽  
Min Can He ◽  
Wei Cheng
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Posterior Distribution ◽  
Particle Filtering ◽  
Mean Shift ◽  
Mean Shift Algorithm ◽  
Filtering Algorithm ◽  
Particle Filter Algorithm

Animproved particle filter algorithm which based on mean shift algorithm isintroduced. The algorithm makes the particles move towards the high likelihoodregion in posterior distribution with the effect of mean shift algorithm,increases the efficiency of the particles moving, and reduces the phenomenon ofdegradation and dilution of particles

scholarly journals Applying sequential Monte Carlo methods into a distributed hydrologic model: lagged particle filtering approach with regularization

2011 ◽  
Vol 15 (10) ◽  
pp. 3237-3251 ◽  
Author(s):  
S. J. Noh ◽  
Y. Tachikawa ◽  
M. Shiiba ◽  
S. Kim
Keyword(s):  
Monte Carlo ◽  
Data Assimilation ◽  
Particle Filter ◽  
Particle Filtering ◽  
Sequential Monte Carlo ◽  
Hydrologic Model ◽  
State Variables ◽  
Process Noise ◽  
Distributed Hydrologic Model ◽  
Filtering Approach

Abstract. Data assimilation techniques have received growing attention due to their capability to improve prediction. Among various data assimilation techniques, sequential Monte Carlo (SMC) methods, known as "particle filters", are a Bayesian learning process that has the capability to handle non-linear and non-Gaussian state-space models. In this paper, we propose an improved particle filtering approach to consider different response times of internal state variables in a hydrologic model. The proposed method adopts a lagged filtering approach to aggregate model response until the uncertainty of each hydrologic process is propagated. The regularization with an additional move step based on the Markov chain Monte Carlo (MCMC) methods is also implemented to preserve sample diversity under the lagged filtering approach. A distributed hydrologic model, water and energy transfer processes (WEP), is implemented for the sequential data assimilation through the updating of state variables. The lagged regularized particle filter (LRPF) and the sequential importance resampling (SIR) particle filter are implemented for hindcasting of streamflow at the Katsura catchment, Japan. Control state variables for filtering are soil moisture content and overland flow. Streamflow measurements are used for data assimilation. LRPF shows consistent forecasts regardless of the process noise assumption, while SIR has different values of optimal process noise and shows sensitive variation of confidential intervals, depending on the process noise. Improvement of LRPF forecasts compared to SIR is particularly found for rapidly varied high flows due to preservation of sample diversity from the kernel, even if particle impoverishment takes place.

Compressed Iterative Particle Filter for Target Tracking

2011 ◽  
Vol 55-57 ◽  
pp. 91-94
Author(s):  
Hong Bo Zhu ◽  
Hai Zhao ◽  
Dan Liu ◽  
Chun He Song
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Kalman Filtering ◽  
Particle Filtering ◽  
Filtering Algorithms ◽  
Filtering Algorithm ◽  
Ga Algorithm ◽  
Non Linear

Particle filtering has been widely used in the non-linear n-Gaussian target tracking problems. The main problem of particle filtering is the lacking and exhausting of particles, and choosing effective proposed distribution is the key point to overcome it. In this paper, a new mixed particle filtering algorithm was proposed. Firstly, the unscented kalman filtering is used to generate the proposed distribution, and in the resample step, a new certain resample method is used to choose the particles with ordered larger weights. GA algorithm is introduced into the certain resample method to keep the variety of the particles. Simuational results have shown that the proposed algorithm has better performances than other three typical filtering algorithms.

scholarly journals A Novel Stastical Particle Filtering Approach for Non-Linear and Non-Gaussian System Identification

2012 ◽  
Vol 60 (6) ◽  
pp. 53-58
Author(s):  
Dhiraj K.Jha ◽  
Abhijit Verma ◽  
Avinash Kumar ◽  
Prabhat Panda
Keyword(s):  
System Identification ◽  
Particle Filtering ◽  
Non Linear ◽  
Gaussian System ◽  
Non Gaussian ◽  
Filtering Approach

scholarly journals Collocation Methods for High-Order Well-Balanced Methods for Systems of Balance Laws

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1799
Author(s):  
Irene Gómez-Bueno ◽  
Manuel Jesús Castro Díaz ◽  
Carlos Parés ◽  
Giovanni Russo
Keyword(s):  
High Order ◽  
Collocation Methods ◽  
Quadrature Formulas ◽  
Balance Laws ◽  
Source Terms ◽  
Time Step ◽  
Systems Of Balance Laws ◽  
Reconstruction Operator ◽  
Non Linear ◽  
Linear Problems

In some previous works, two of the authors introduced a technique to design high-order numerical methods for one-dimensional balance laws that preserve all their stationary solutions. The basis of these methods is a well-balanced reconstruction operator. Moreover, they introduced a procedure to modify any standard reconstruction operator, like MUSCL, ENO, CWENO, etc., in order to be well-balanced. This strategy involves a non-linear problem at every cell at every time step that consists in finding the stationary solution whose average is the given cell value. In a recent paper, a fully well-balanced method is presented where the non-linear problems to be solved in the reconstruction procedure are interpreted as control problems. The goal of this paper is to introduce a new technique to solve these local non-linear problems based on the application of the collocation RK methods. Special care is put to analyze the effects of computing the averages and the source terms using quadrature formulas. A general technique which allows us to deal with resonant problems is also introduced. To check the efficiency of the methods and their well-balance property, they have been applied to a number of tests, ranging from easy academic systems of balance laws consisting of Burgers equation with some non-linear source terms to the shallow water equations—without and with Manning friction—or Euler equations of gas dynamics with gravity effects.

Efficient metacomputing of elliptic linear and non-linear problems

2003 ◽  
Vol 63 (5) ◽  
pp. 564-577 ◽  
Author(s):  
Nicolas Barberou ◽  
Marc Garbey ◽  
Matthias Hess ◽  
Michael M. Resch ◽  
Tuomo Rossi ◽  
...  
Keyword(s):  
Non Linear ◽  
Linear Problems

Non-Linear Problems

Nature ◽  
1968 ◽  
Vol 220 (5163) ◽  
pp. 204-204
Author(s):  
IAN N. SNEDDON
Keyword(s):  
Non Linear ◽  
Linear Problems
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