An unbiased homotopy particle filter and its application to the INS/GPS integrated navigation system

2017 ◽  
Author(s):  
Xuemei Wang ◽  
Wenbo Ni
Keyword(s):  
Particle Filter ◽  
Navigation System ◽  
Integrated Navigation ◽  
Integrated Navigation System

Federated Particle Filter Technology Based on JIDS/SINS/GPS Integrated Navigation System

2013 ◽  
Vol 347-350 ◽  
pp. 1544-1548
Author(s):  
Zi Yu Li ◽  
Yan Liu ◽  
Ping Zhu ◽  
Cheng Ying
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Navigation System ◽  
Integrated System ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Fusion Algorithm ◽  
Integrated Navigation System ◽  
Non Linear ◽  
Federated Kalman Filter

In multi-sensor integrated navigation systems, when sub-systems are non-linear and with Gaussian noise, the federated Kalman filter commonly used generates large error or even failure when estimating the global fusion state. This paper, taking JIDS/SINS/GPS integrated navigation system as example, proposes a federated particle filter technology to solve problems above. This technology, combining the particle filter with the federated Kalman filter, can be applied to non-linear non-Gaussian integrated system. It is proved effective in information fusion algorithm by simulated application, where the navigation information gets well fused.

Kalman/Particle Filter for Integrated Navigation System

2013 ◽  
Vol 756-759 ◽  
pp. 2142-2146 ◽  
Author(s):  
Zhun Jiao ◽  
Rong Zhang
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Navigation System ◽  
The Other ◽  
New Method ◽  
Integrated Navigation ◽  
Computation Complexity ◽  
Integrated Navigation System ◽  
Simulation Results

Particle filter is introduced. Since the particle filter would bring hard computation, a new Kalman/Particle mixed filter used on SINS/GPS integrated navigation system was proposed. The new method divides the system into two sub-models, one is linear, the other one is nonlinear, and then implement Kalman filter and particle filter separately. The simulation results show that their performance is almost equal, but the computation complexity of the Kalman/particle filter is much lower than traditional particle filter.

Improved Particle Filter for Integrated Navigation System

2014 ◽  
Vol 543-547 ◽  
pp. 1278-1281 ◽  
Author(s):  
Zhun Jiao ◽  
Rong Zhang
Keyword(s):  
Monte Carlo ◽  
Particle Filter ◽  
Navigation System ◽  
Particle Filtering ◽  
Bayesian Filtering ◽  
Integrated Navigation ◽  
Proposal Distribution ◽  
Gaussian Distributions ◽  
Integrated Navigation System ◽  
Non Gaussian

As a new method for dealing with any nonlinear or non-Gaussian distributions, based on the Monte Carlo methods and Bayesian filtering, particle filters (PF) are favored by researchers and widely applied in many fields. Based on particle filtering, an improved particle filter (IPF) proposal distribution is presented. Evaluation of the weights is simplified and other improved techniques including the residual resampling step and Markov Chain Monte Carlo method are introduced for SINS/GPS integrated navigation system. The simulation results confirm that the improved particle filter outperforms the others.

scholarly journals Robust Interacting Multiple Model Unscented Particle Filter for Navigation

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Li Xue ◽  
Yulan Han ◽  
Chunning Na
Keyword(s):  
Particle Filter ◽  
Navigation System ◽  
Measurement Information ◽  
Interacting Multiple Model ◽  
Integrated Navigation ◽  
Multiple Model ◽  
Integrated Navigation System ◽  
Filter Performance ◽  
Unscented Particle Filter ◽  
Particle Filter Algorithm

In order to solve the problems of particle degradation and difficulty in selecting importance density function in particle filter algorithm, a robust interacting multiple model unscented particle filter algorithm is presented, which is based on the advantages of interacting multiple model and particle filter algorithms. This algorithm can use the unscented transformation to get the particles that contain the latest measurement information of each model and calculate the robust equivalent weight function. This robust factor is designed to adjust the estimation and variance, and the important distribution function adaptively obtained is closer to the true distribution. Then, the particles weights can be flexibly adjusted in real time by using Euclidean distance to improve the computational efficiency during the resampling process. In addition, this filter process can comprehensively describe the uncertainty of the statistics characteristic of observation noise between different models. The diversity of available particles is increased, and the filter precision is improved. The proposed algorithm is applied to the SINS/GPS integrated navigation system, and the simulation analysis results demonstrate that the algorithm can effectively improve the filter performance and the calculation precision in positioning of integrated navigation system; thus, it provides a new method for nonlinear model filter.

scholarly journals A Quaternion-Based Robust Adaptive Spherical Simplex Unscented Particle Filter for MINS/VNS/GNS Integrated Navigation System

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Ke Jia ◽  
Yifei Pei ◽  
Zhaohui Gao ◽  
Yongmin Zhong ◽  
Shesheng Gao ◽  
...  
Keyword(s):  
Particle Filter ◽  
Navigation System ◽  
Unscented Kalman Filter ◽  
Model Simulation ◽  
Adaptive Estimation ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Unscented Particle Filter ◽  
Spherical Simplex ◽  
Robust Adaptive

An improved filtering algorithm-robust adaptive spherical simplex unscented particle filter (RASSUPF) is proposed to achieve high accuracy, induce the amount of computation, and resist the influence of abnormal interference for the MINS/VNS/GNS integrated navigation system. This algorithm adopts spherical simplex unscented transformation (SSUT) to approximate the probability distribution, employs the spherical simplex unscented Kalman filter (SSUKF) to generate the importance sampling density of particle filter, and applies robust and adaptive estimation to control the influence of the abnormal information on the state model and the observation model. Simulation results demonstrate the proposed algorithm can effectively reduce the navigation error, improve the navigation positioning precision, and decrease the computation cost.

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