scholarly journals A drift homotopy implicit particle filter method for nonlinear filtering problems

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Xin Li ◽  
Feng Bao ◽  
Kyle Gallivan
Keyword(s):  
Particle Filter ◽  
Nonlinear Filtering ◽  
Numerical Experiments ◽  
Filter Method ◽  
Resampling Procedure ◽  
Effectiveness And Efficiency ◽  
Filtering Problem

<p style='text-indent:20px;'>In this paper, we develop a drift homotopy implicit particle filter method. The methodology of our approach is to adopt the concept of drift homotopy in the resampling procedure of the particle filter method for solving the nonlinear filtering problem, and we introduce an implicit particle filter method to improve the efficiency of the drift homotopy resampling procedure. Numerical experiments are carried out to demonstrate the effectiveness and efficiency of our drift homotopy implicit particle filter.</p>

An Implicit Algorithm of Solving Nonlinear Filtering Problems

2014 ◽  
Vol 16 (2) ◽  
pp. 382-402
Author(s):  
Feng Bao ◽  
Yanzhao Cao ◽  
Xiaoying Han
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Nonlinear Filtering ◽  
State Equation ◽  
Nonlinear Filter ◽  
Filter Method ◽  
Simulation Methods ◽  
Current State ◽  
Numerical Simulation Methods ◽  
Kalman Filter Method

AbstractNonlinear filter problems arise in many applications such as communications and signal processing. Commonly used numerical simulation methods include Kalman filter method, particle filter method, etc. In this paper a novel numerical algorithm is constructed based on samples of the current state obtained by solving the state equation implicitly. Numerical experiments demonstrate that our algorithm is more accurate than the Kalman filter and more stable than the particle filter.

scholarly journals A Kusuoka–Lyons–Victoir particle filter

2013 ◽  
Vol 469 (2156) ◽  
pp. 20130076 ◽  
Author(s):  
Dan Crisan ◽  
Salvador Ortiz-Latorre
Keyword(s):  
Particle Filter ◽  
Continuous Time ◽  
Nonlinear Filtering ◽  
Computational Effort ◽  
Observation Data ◽  
Minimal Variance ◽  
Discretization Grid ◽  
Filtering Problem ◽  
Grid Mesh ◽  
Number Of Particles

The aim of this paper is to introduce a new numerical algorithm for solving the continuous time nonlinear filtering problem. In particular, we present a particle filter that combines the Kusuoka–Lyons–Victoir (KLV) cubature method on Wiener space to approximate the law of the signal with a minimal variance ‘thinning’ method, called the tree-based branching algorithm (TBBA) to keep the size of the cubature tree constant in time. The novelty of our approach resides in the adaptation of the TBBA algorithm to simultaneously control the computational effort and incorporate the observation data into the system. We provide the rate of convergence of the approximating particle filter in terms of the computational effort (number of particles) and the discretization grid mesh. Finally, we test the performance of the new algorithm on a benchmark problem (the Beneš filter).

Numerical analysis of rating transition matrix depending on latent macro factor via nonlinear particle filter method

2014 ◽  
Vol 01 (03) ◽  
pp. 1450026 ◽  
Author(s):  
Hidetoshi Nakagawa ◽  
Hideyuki Takada
Keyword(s):  
Particle Filter ◽  
Financial Market ◽  
Transition Matrix ◽  
Nonlinear Filtering ◽  
Credit Rating ◽  
Filter Method ◽  
Contagion Effect ◽  
Macroeconomic Factor ◽  
History Of

We propose a new nonlinear filtering model for a better estimation of credit rating transition matrix consistent with the hypothesis that rating transition intensities as well as dynamics of financial asset prices depend on some unobservable macroeconomic factor. We attempt a branching particle filter method to numerically obtain the conditional distribution of the latent factor. For an illustration, we analyze a rating transition history of Japanese enterprises. As a result, we realize that our model can capture some contagion effect of credit events and an interpolative role of financial market information on the rating transition intensities.

Dynamic Target Tracking Based on Particle Filter in Actual Environment

2013 ◽  
Vol 683 ◽  
pp. 824-827
Author(s):  
Tian Ding Chen ◽  
Chao Lu ◽  
Jian Hu
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Target Tracking ◽  
Monitoring System ◽  
Filter Method ◽  
Complex Scene ◽  
Actual Environment ◽  
Dynamic Target ◽  
Gaussian System ◽  
Non Gaussian

With the development of science and technology, target tracking was applied to many aspects of people's life, such as missile navigation, tanks localization, the plot monitoring system, robot field operation. Particle filter method dealing with the nonlinear and non-Gaussian system was widely used due to the complexity of the actual environment. This paper uses the resampling technology to reduce the particle degradation appeared in our test. Meanwhile, it compared particle filter with Kalman filter to observe their accuracy .The experiment results show that particle filter is more suitable for complex scene, so particle filter is more practical and feasible on target tracking.

The Extended Integrated Particle Filter Method (IPFx) as a High-Performance Earthquake Early Warning System

2021 ◽  
Author(s):  
Masumi Yamada ◽  
Koji Tamaribuchi ◽  
Stephen Wu
Keyword(s):  
Particle Filter ◽  
Early Warning ◽  
Seismic Intensity ◽  
Earthquake Early Warning ◽  
Japan Meteorological Agency ◽  
Estimation Accuracy ◽  
Filter Method ◽  
Source Estimation ◽  
Single Station ◽  
Seismic Networks

ABSTRACT An earthquake early warning (EEW) system rapidly analyzes seismic data to report the occurrence of an earthquake before strong shaking is felt at a site. In Japan, the integrated particle filter (IPF) method, a new source-estimation algorithm, was recently incorporated into the EEW system to improve the source-estimation accuracy during active seismicity. The problem of the current IPF method is that it uses the trigger information computed at each station in a specific format as the input and is therefore applicable to only limited seismic networks. This study proposes the extended IPF (IPFx) method to deal with continuous waveforms and merge all Japanese real-time seismic networks into a single framework. The new source determination algorithm processes seismic waveforms in two stages. The first stage (single-station processing) extracts trigger and amplitude information from continuous waveforms. The second stage (network processing) accumulates information from multiple stations and estimates the location and magnitude of ongoing earthquakes based on Bayesian inference. In 10 months of continuous online experiments, the IPFx method showed good performance in detecting earthquakes with maximum seismic intensity ≥3 in the Japan Meteorological Agency (JMA) catalog. By merging multiple seismic networks into a single EEW system, the warning time of the current EEW system can be improved further. The IPFx method provides accurate shaking estimation even at the beginning of event detection and achieves seismic intensity error &lt;0.25  s after detecting an event. This method correctly avoided two major false alarms on 5 January 2018 and 30 July 2020. The IPFx method offers the potential of expanding the JMA IPF method to global seismic networks.

scholarly journals Particle Filter with Gaussian Weighting for Vehicle Tracking

2011 ◽  
Vol 15 (6) ◽  
pp. 681-686
Author(s):  
Indah Agustien Siradjuddin ◽  
◽  
Muhammad Rahmat Widyanto ◽  
Keyword(s):  
Particle Filter ◽  
Object Tracking ◽  
Gaussian Distribution ◽  
Likelihood Function ◽  
Target Object ◽  
Video Data ◽  
Vehicle Tracking ◽  
Computational Time ◽  
Filter Method ◽  
Vehicle Motion

To track vehicle motion in data video, particle filter with Gaussian weighting is proposed. This method consists of four main stages. First, particles are generated to predict target’s location. Second, certain particles are searched and these particles are used to build Gaussian distribution. Third, weight of all particles is calculated based on Gaussian distribution. Fourth, particles are updated based on each weight. The proposed method could reduce computational time of tracking compared to that of conventional method of particle filter, since the proposed method does not have to calculate all particles weight using likelihood function. This method has been tested on video data with car as a target object. In average, this proposed method of particle filter is 60.61% times faster than particle filter method meanwhile the accuracy of tracking with this newmethod is comparable with particle filter method, which reach up to 86.87%. Hence this method is promising for real time object tracking application.

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