Research of Signal Processing Based on Kalman Filter and Particle Filter

2014 ◽  
Vol 548-549 ◽  
pp. 1080-1084
Author(s):  
Ou Yang Jin ◽  
Yan Song Li ◽  
Jun Liu
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Kalman Filtering ◽  
Particle Filtering ◽  
System Analysis ◽  
Matlab Simulation ◽  
Low Snr ◽  
Noise Characteristics ◽  
Current Transducer ◽  
Output Snr

The current transducer is the premise condition of electricity measurement, relay protection, monitoring and diagnosis system, and power system analysis. This paper introduces the principle and signal to noise characteristics of optical current transducer (OCT), which is based on Faraday Magneto-optic effect. Then, proposed uses the kalman filter and particle filtering method to improve the output SNR of OCT, for the OCT has a low SNR. At last, Establish the both particle filter dynamic model for AC and DC situation, After choosing appropriate parameters of the kalman filtering and particle filtering mix method on the matlab simulation of the above situation, the results show that the kalman filtering and particle filtering mix method can improve the output SNR and measuring accuracy.

scholarly journals COMPARISON OF GPS SMOOTHENING METHODS BETWEEN EXTENDED KALMAN FILTER AND PARTICLE FILTER FOR UAV

2017 ◽  
Keyword(s):  
Numerical Simulation ◽  
Kalman Filter ◽  
Particle Filter ◽  
Extended Kalman Filter ◽  
Kalman Filtering ◽  
Particle Filtering ◽  
State Vector ◽  
The State ◽  
Gps Data ◽  
Extended Kalman Filtering

This paper presents a method for smoothing GPS data from a UAV using Extended Kalman filtering and particle filtering for navigation or position control. A key requirement for navigation and control of any autonomous flying or moving robot is availability of a robust attitude estimate. Consider a dynamic system such as a moving robot. The unknown parameters, e.g., the coordinates and the velocity, form the state vector. This time dependent vector may be predicted for any instant time by means of system equations. The predicted values can be improved or updated by observations containing information on some components of the state vector. The whole procedure is known as Kalman filtering. On the other hand, the particle filtering algorithm is to perform a recursive Bayesian filter by Monte Carlo simulations. The key is to represent the required posterior density function by a set of random samples, which is called particles with associated weights, and to compute estimates based on these samples as well as weights. We compare the two GPS smoothening methods: Extended Kalman Filter and Particle Filter for mobile robots applications. Validity of the smoothing methods is verified from the numerical simulation and the experiments. The numerical simulation and experimental results show the good GPS data smoothing performance using Extended Kalman filtering and particle filtering.

Terrain-Aided Localization Using Feature-Based Particle Filtering

2011 ◽  
Author(s):  
Sneha Kadetotad ◽  
Pramod K. Vemulapalli ◽  
Sean N. Brennan ◽  
Constantino Lagoa
Keyword(s):  
Particle Filter ◽  
Kalman Filtering ◽  
Computational Efficiency ◽  
Particle Filtering ◽  
Feature Vector ◽  
Global Search ◽  
Wide Area ◽  
Global Localization ◽  
Number Of Solutions ◽  
Feature Based

The localization of vehicles on roadways without the use of a GPS has been of great interest in recent years and a number of solutions have been proposed for the same. The localization of vehicles has traditionally been divided by their solution approaches into two different categories: global localization which uses feature-vector matching, and local tracking which has been dealt by using techniques like Particle Filtering or Kalman Filtering. This paper proposes a unifying approach that combines the feature-based robustness of global search with the local tracking capabilities of a particle filter. Using feature vectors produced from pitch measurements from Interstate I-80 and US Route 220 in Pennsylvania, this work demonstrates wide area localization of a vehicle with the computational efficiency of local tracking.

A Method for Tracking Dim Target Using Particle Filter

2014 ◽  
Vol 602-605 ◽  
pp. 3127-3130
Author(s):  
Hong Wei Quan ◽  
Jun Hua Li ◽  
Da Yu Huang
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Random Sampling ◽  
Unscented Kalman Filter ◽  
Numerical Technique ◽  
System Model ◽  
Low Snr ◽  
Practical Engineering ◽  
Simulation Results ◽  
Optimal Evaluation

Traditional methods encountered two serious problems in tracking dim targets. One is the nonlinearity of the system model, and other is the low SNR of measurement signals. The two problems are hardly solved simultaneously in practical engineering applications. The particle filter is a recursive numerical technique which uses random sampling to approximate the optimal evaluation to target tracking problems. In this paper, we developed a method for tracking dim target using particle filter. Simulation results showed that the tracking performance of this method has greatly improved compared with classical extended Kalman filter and unscented Kalman filter.

scholarly journals A Zero-Velocity Update Method for Adaptive Particle Filtering

2020 ◽  
Vol 38 (2) ◽  
pp. 427-433
Author(s):  
Yuyang Guo ◽  
Xiangbo Xu ◽  
Miaoxin Ji
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Particle Filtering ◽  
State Space Model ◽  
Classical Particle ◽  
Measurement Unit ◽  
Correction Algorithm ◽  
Zero Velocity ◽  
Non Gaussian ◽  
Navigation Errors

Aiming at the low precision of Kalman filter in dealing with non-linear and non-Gaussian models and the serious particle degradation in standard particle filter, a zero-velocity correction algorithm of adaptive particle filter is proposed in this paper. In order to improve the efficiency of resampling, the adaptive threshold is combined with particle filter. In the process of resampling, the degradation co-efficient is introduced to judge the degree of particle degradation, and the particles are re-sampled to ensure the diversity of particles. In order to verify the effectiveness and feasibility of the proposed algorithm, a hardware platform based on the inertial measurement unit (IMU) is built, and the state space model of the system is established by using the data collected by IMU, and experiments are carried out. The experimental results show that, compared with Kalman filter and classical particle filter, the positioning accuracy of adaptive particle filter in zero-velocity range is improved by 40.6% and 19.4% respectively. The adaptive particle filter (APF) can correct navigation errors better and improve pedestrian trajectory accuracy.

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 Ship tracking based on the fusion of Kalman filter and particle filter

2021 ◽  
Vol 2113 (1) ◽  
pp. 012017
Author(s):  
Wanjin Xu ◽  
Jiying Li ◽  
Junjie Bai ◽  
Yingying Zhang
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
State Estimation ◽  
Particle Filtering ◽  
Unscented Kalman Filter ◽  
Optimal Estimation ◽  
Estimation Algorithm ◽  
Tracking Accuracy ◽  
Gps Navigation ◽  
Degradation Degree

Abstract Aiming at the problem of low filtering accuracy and even divergence caused by model mismatch when using extended Kalman filter in ship GPS navigation and positioning state estimation, a positioning ship state estimation algorithm based on the fusion of improved unscented Kalman filter and particle filter is proposed. Compared with the traditional particle filtering algorithm, the algorithm has two improvements: first, the algorithm uses untraced Kalman as the main framework, and uses the optimal estimation of particle updating state by particle algorithm; Secondly, in the resampling process, a resampling algorithm based on weight optimization is proposed to increase the diversity of particles. The simulation results show that not only the particle degradation degree of the particle filter is reduced, but also the particle tracking accuracy is improved.

Sensor fusion-based dynamic positioning of ships using Extended Kalman and Particle Filtering

Robotica ◽  
2012 ◽  
Vol 31 (3) ◽  
pp. 389-403 ◽  
Author(s):  
Gerasimos G. Rigatos
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Sensor Fusion ◽  
Particle Filtering ◽  
State Vector ◽  
Dynamic Positioning ◽  
Horizontal Position ◽  
Control Loop ◽  
Simulation Experiments ◽  
Distance Measurements

SUMMARYThe paper examines the problem of dynamic ship positioning with the use of Kalman Filter- and Particle Filter-based sensor fusion algorithms. The proposed approach enables to estimate accurately the ship's state vector by fusing the vessel's position and heading measurements coming from on-board sensors together with distance measurements coming from sensors located at the coast (e.g. radar). The estimated state vector is used in turn, in a control loop, to regulate the horizontal position and heading of the vessel. The performance of dynamic positioning of the ship based on Kalman and Particle Filtering is evaluated through simulation experiments.

Near Space Pseudolite Navigation System Design and High-Performance Filtering Algorithm

2013 ◽  
Vol 411-414 ◽  
pp. 931-935
Author(s):  
She Sheng Gao ◽  
Wen Hui Wei ◽  
Li Xue
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Navigation System ◽  
Particle Filtering ◽  
Autonomous Navigation ◽  
Unscented Kalman Filter ◽  
Navigation Systems ◽  
Filtering Algorithm ◽  
Near Space ◽  
Autonomous Navigation System

This paper analyzes the defects of satellite navigation systems that exist in positioning and precision-guided weapons and pointes out the advantages and military needs of pseudolite. The autonomous navigation nonlinear mathematical model of Near Space Pseudolite SINS/CNS/SAR autonomous navigation system is established. Based on the merits of fading filter, robust adaptive filtering and particle filter, we propose a fading adaptive Unscented Particle Filtering algorithm. The proposed filtering algorithm is applied to SINS/CNS/SAR autonomous navigation system and conducted simulation calculation with the Unscented Kalman filter and particle filter comparison. The results show that the new algorithm that is proposed meets the needs of pseudolite autonomous navigation, and the navigation accuracy is significantly higher than the Unscented Kalman filter and particle filter algorithm.

scholarly journals A distributed, dynamic, parallel computational model: the role of noise in velocity storage

2012 ◽  
Vol 108 (2) ◽  
pp. 390-405 ◽  
Author(s):  
Faisal Karmali ◽  
Daniel M. Merfeld
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Time Constant ◽  
Particle Filtering ◽  
Imperfect Information ◽  
Storage Time ◽  
Velocity Storage ◽  
Body Parts ◽  
Observer Model ◽  
The Brain

Networks of neurons perform complex calculations using distributed, parallel computation, including dynamic “real-time” calculations required for motion control. The brain must combine sensory signals to estimate the motion of body parts using imperfect information from noisy neurons. Models and experiments suggest that the brain sometimes optimally minimizes the influence of noise, although it remains unclear when and precisely how neurons perform such optimal computations. To investigate, we created a model of velocity storage based on a relatively new technique–“particle filtering”–that is both distributed and parallel. It extends existing observer and Kalman filter models of vestibular processing by simulating the observer model many times in parallel with noise added. During simulation, the variance of the particles defining the estimator state is used to compute the particle filter gain. We applied our model to estimate one-dimensional angular velocity during yaw rotation, which yielded estimates for the velocity storage time constant, afferent noise, and perceptual noise that matched experimental data. We also found that the velocity storage time constant was Bayesian optimal by comparing the estimate of our particle filter with the estimate of the Kalman filter, which is optimal. The particle filter demonstrated a reduced velocity storage time constant when afferent noise increased, which mimics what is known about aminoglycoside ablation of semicircular canal hair cells. This model helps bridge the gap between parallel distributed neural computation and systems-level behavioral responses like the vestibuloocular response and perception.

scholarly journals The Salted Kalman Filter: Kalman filtering on hybrid dynamical systems

Automatica ◽  
2021 ◽  
Vol 131 ◽  
pp. 109752
Author(s):  
Nathan J. Kong ◽  
J. Joe Payne ◽  
George Council ◽  
Aaron M. Johnson
Keyword(s):  
Kalman Filter ◽  
Dynamical Systems ◽  
Kalman Filtering ◽  
Hybrid Dynamical Systems
Sign in / Sign up

Export Citation Format

Share Document