scholarly journals Innovative And Additive Outlier Robust Kalman Filtering With A Robust Particle Filter

2021 ◽  
pp. 1-1
Author(s):  
Alexander Tristan Fisch ◽  
Idris Eckley ◽  
Paul Fearnhead
Keyword(s):  
Particle Filter ◽  
Kalman Filtering ◽  
Additive Outlier

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.

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.

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.

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.

A mutual information‐based likelihood function for particle filter flood extent assimilation

2021 ◽  
Author(s):  
Antara Dasgupta ◽  
Renaud Hostache ◽  
RAAJ Ramasankaran ◽  
Guy J.‐P Schumann ◽  
Stefania Grimaldi ◽  
...  
Keyword(s):  
Mutual Information ◽  
Particle Filter ◽  
Likelihood Function

Delivering Sustainable Water Systems Using Kalman Filter and Statistical Approach

2017 ◽  
Vol 4 (1) ◽  
pp. 41-52
Author(s):  
Dedy Loebis
Keyword(s):  
Kalman Filtering ◽  
Statistical Approach ◽  
Water Systems ◽  
Data Sets ◽  
Pressure Data ◽  
Burst Event ◽  
Sample Data ◽  
Other Information ◽  
Supply Systems ◽  
Frequency Features

This paper presents the results of work undertaken to develop and test contrasting data analysis approaches for the detection of bursts/leaks and other anomalies within wate r supply systems at district meter area (DMA)level. This was conducted for Yorkshire Water (YW) sample data sets from the Harrogate and Dales (H&D), Yorkshire, United Kingdom water supply network as part of Project NEPTUNE EP/E003192/1 ). A data analysissystem based on Kalman filtering and statistical approach has been developed. The system has been applied to the analysis of flow and pressure data. The system was proved for one dataset case and have shown the ability to detect anomalies in flow and pres sure patterns, by correlating with other information. It will be shown that the Kalman/statistical approach is a promising approach at detecting subtle changes and higher frequency features, it has the potential to identify precursor features and smaller l eaks and hence could be useful for monitoring the development of leaks, prior to a large volume burst event.

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