scholarly journals Speech Source Tracking based on Particle Filter under non-Gaussian Noise and Reverberant Environments

2019 ◽  
Author(s):  
Ruifang Wang ◽  
Xiaoyu Lan
Keyword(s):  
Particle Filter ◽  
Gaussian Noise ◽  
Source Tracking ◽  
Reverberant Environments ◽  
Non Gaussian

A Novel Filtering Method for the Random Drift of MEMS Gyroscope

2014 ◽  
Vol 901 ◽  
pp. 73-79 ◽  
Author(s):  
Yu Wang ◽  
Yun Xu ◽  
Xin Hua Zhu
Keyword(s):  
Particle Filter ◽  
Gaussian Noise ◽  
Engineering Application ◽  
Random Drift ◽  
Unscented Transformation ◽  
Mems Gyroscope ◽  
Starting State ◽  
Nonlinearity Effect ◽  
Non Gaussian ◽  
Environment Noise

In engineering application, the nonlinearity effect of the environment noise is inconsistent with the successive starting state of MEMS gyroscope which will induce the random drifts. It manifests as the weak nonlinearity, non stability and slow time varying which cannot be compensated by the conventional method. In order to overcome the problems of the great random drift error model established based on the time series for MEMS gyroscope and the non Gaussian noise, the method of Iteration Unscented Kalman Particle Filter (IUKPF) is proposed in this paper. This method is based on the Particle Filter combing the Unscented Transformation (UT) with Iteration Kalman Filter (IKF), and it solved the instability of the precision for the conventional filtering methods and the degradation for the weight of the particle filter. The filtering result shows that the method of IUKPF can effectively restrain the random drift error under nonlinear and non Gaussian noise. The standard deviation for the output noise of MEMS gyroscope has decreased 81.9% by IUKPF which verifies the efficiency and superiority of this method.

scholarly journals Positioning Techniques in Indoor Environments Based on Stochastic Modeling of UWB Round Trip Time Measurements

2021 ◽  
Author(s):  
Paolo Carbone
Keyword(s):  
Particle Filter ◽  
Gaussian Noise ◽  
Additive White Gaussian Noise ◽  
Least Square ◽  
Indoor Environments ◽  
Round Trip Time ◽  
Round Trip ◽  
Positioning Systems ◽  
Trip Time ◽  
Non Gaussian

<div><div><div><p>In this paper, a technique for modeling propagation of Ultra Wide Band (UWB) signals in indoor or outdoor environments is proposed, supporting the design of a positioning systems based on Round Trip Time (RTT) measurements and on a particle filter. By assuming that nonlinear pulses are transmitted in an Additive White Gaussian Noise Channel, and detected using a threshold based receiver, it is shown that RTT measurements may be affected by a non-Gaussian noise. RTT noise properties are analyzed, and the effects of non-Gaussian noise on the performance of a RTT based positioning system are investigated. To this aim, a classical Least Square, an extended Kalman Filter and a Particle Filter are compared when used to detect a slowly moving target in presence of the modeled noise. It is shown that, in a realistic indoor environment, the Particle Filter solution may be a competitive solution, at a price of increased computational complexity. Experimental verifications validate the presented approach.</p></div></div></div>

scholarly journals Pipeline Gaussian Particle Filter and Hardware Design for Attitude Estimation with UAV

2020 ◽  
Vol 2020 ◽  
pp. 1-6 ◽  
Author(s):  
Yali Xue ◽  
Hu Chen ◽  
Jie Chen ◽  
Jiahui Wang
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Gaussian Noise ◽  
Estimation Algorithm ◽  
Attitude Estimation ◽  
Estimation Accuracy ◽  
Gaussian State ◽  
Time Performance ◽  
Simulation Results ◽  
Non Gaussian

This paper based on the Gaussian particle filter (GPF) deals with the attitude estimation of UAV. GPF algorithm has better estimation accuracy than the general nonlinear non-Gaussian state estimation and is usually used to improve the system’s real-time performance whose noise is specific such as Gaussian noise during the mini UAV positioning and navigation. The attitude estimation algorithm is implemented on FPGA to verify the effectiveness of the Gaussian particle filter. Simulation results have illustrated that the GPF algorithm is effective and has better real-time performance than that of the particle filter.

scholarly journals An Adaptive Unscented Particle Filter Algorithm through Relative Entropy for Mobile Robot Self-Localization

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Wentao Yu ◽  
Jun Peng ◽  
Xiaoyong Zhang ◽  
Shuo Li ◽  
Weirong Liu
Keyword(s):  
Nonlinear System ◽  
Particle Filter ◽  
State Estimation ◽  
Real Time ◽  
Relative Entropy ◽  
Gaussian Noise ◽  
The Real ◽  
Unscented Particle Filter ◽  
Non Gaussian ◽  
Number Of Particles

Self-localization is a basic skill for mobile robots in the dynamic environments. It is usually modeled as a state estimation problem for nonlinear system with non-Gaussian noise and needs the real-time processing. Unscented particle filter (UPF) can handle the state estimation problem for nonlinear system with non-Gaussian noise; however the computation of UPF is very high. In order to reduce the computation cost of UPF and meanwhile maintain the accuracy, we propose an adaptive unscented particle filter (AUPF) algorithm through relative entropy. AUPF can adaptively adjust the number of particles during filtering to reduce the necessary computation and hence improve the real-time capability of UPF. In AUPF, the relative entropy is used to measure the distance between the empirical distribution and the true posterior distribution. The least number of particles for the next step is then decided according to the relative entropy. In order to offset the difference between the proposal distribution, and the true distribution the least number is adjusted thereafter. The ideal performance of AUPF in real robot self-localization is demonstrated.

scholarly journals Simplified partial resampling method for state estimation usingparticle filter

2018 ◽  
Vol 7 (2.7) ◽  
pp. 243
Author(s):  
M Tirumala Reddy ◽  
Y Sri Ganesh ◽  
Ch Lakshmi Gayathri ◽  
T Megha Shyam ◽  
S Koteswar Rao ◽  
...  
Keyword(s):  
Particle Filter ◽  
State Estimation ◽  
Gaussian Noise ◽  
The State ◽  
Resampling Methods ◽  
Simple Pendulum ◽  
Filter Methods ◽  
Resampling Method ◽  
Paper Work ◽  
Non Gaussian

Particle filter methods are used in the estimation and tracking of the objects for non-linear and non-gaussian noise conditions. In this paper work the object estimation using partial resampling methods are discussed. On using partial resampling method resampling becomes faster. The performance of particle filter with partial resampling scheme is analyzed using the state estima-tion of a simple pendulum.

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