scholarly journals Maximum Correntropy Based Unscented Particle Filter for Cooperative Navigation with Heavy-Tailed Measurement Noises

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3183 ◽  
Author(s):  
Ying Fan ◽  
Yonggang Zhang ◽  
Guoqing Wang ◽  
Xiaoyu Wang ◽  
Ning Li
Keyword(s):  
Particle Filter ◽  
Autonomous Underwater Vehicles ◽  
Estimation Accuracy ◽  
Cooperative Navigation ◽  
Unscented Particle Filter ◽  
Robust Filters ◽  
Measurement Noises ◽  
Heavy Tailed ◽  
Maximum Correntropy Criterion ◽  
Number Of Particles

In this paper, a novel robust particle filter is proposed to address the measurement outliers occurring in the multiple autonomous underwater vehicles (AUVs) based cooperative navigation (CN). As compared with the classic unscented particle filter (UPF) based on Gaussian assumption of measurement noise, the proposed robust particle filter based on the maximum correntropy criterion (MCC) exhibits better robustness against heavy-tailed measurement noises that are often induced by measurement outliers in CN systems. Furthermore, the proposed robust particle filter is computationally much more efficient than existing robust UPF due to the use of a Kullback-Leibler distance-resampling to adjust the number of particles online. Experimental results based on actual lake trial show that the proposed maximum correntropy based unscented particle filter (MCUPF) has better estimation accuracy than existing state-of-the-art robust filters for CN systems with heavy-tailed measurement noises, and the proposed MCUPF has lower computational complexity than existing robust particle filters.

Multipath Parameter Estimation Using the Unscented Particle Filter Based Code Tracking Loop for Non-Gaussian Noises

2015 ◽  
Vol 764-765 ◽  
pp. 565-569
Author(s):  
Dah Jing Jwo ◽  
Chia Sheng Hsu
Keyword(s):  
Parameter Estimation ◽  
Particle Filter ◽  
Superior Performance ◽  
Estimation Accuracy ◽  
Tracking Loop ◽  
Proposal Distribution ◽  
Unscented Particle Filter ◽  
Heavy Tailed ◽  
Non Gaussian ◽  
Alternative Estimator

In this paper, an unscented particles filter (UPF) based DLL tracking loop with multipath parameter estimation and mitigation capability is proposed for the Global Positioning System (GPS). The I (in-phase) and Q (quadrature) accumulator outputs from the GPS correlators are used as the observational measurements of UPF to estimate the multipath parameters such as amplitude, code delay, phase, and carrier Doppler. The particle filter (PF) possesses superior performance as compared to EKF and UKF as an alternative estimator for dealing with the nonlinear, non-Gaussian system. To handle the problem of heavy-tailed probability distribution, one of the strategies is to incorporate the UKF into the PF as the proposal distribution, leading to the unscented particle filter (UPF). The results show that the tracking loop using the proposed design can effectively estimate the multipath parameters and has demonstrated substantial estimation accuracy improvement as compared to that of the conventional EKF and UKF approaches.

A New Adaptive UPF Algorithm through Improved Relative Entropy

2013 ◽  
Vol 658 ◽  
pp. 569-573
Author(s):  
Wen Tao Yu ◽  
Jun Peng ◽  
Xiao Yong Zhang
Keyword(s):  
Particle Filter ◽  
Relative Entropy ◽  
Particle Number ◽  
Time Step ◽  
Unscented Particle Filter ◽  
Posterior Probability Density ◽  
The Difference ◽  
Particle Filter Algorithm ◽  
Non Gaussian ◽  
Number Of Particles

Unscented particle filter (UPF) has high accuracy of state estimation for nonlinear system with non-Gaussian noise. While the computation of traditional unscented particle filter is huge and this depends on the particle number. In this paper we propose a new adaptive unscented particle filter algorithm AUPF through improved relative entropy which can adaptively adjust the particle number during filtering. Firstly the relative entropy is used to measure the distance between the posterior probability density and the importance proposal and the least number of particles for the next time step is decided according to the relative entropy. Then the least number is adjusted to offset the difference between the importance proposal and the true distribution. This algorithm can effectively reduce unnecessary particles meanwhile reduce the computation. The simulation results show the effectiveness of AUPF.

INS/GPS Tightly-coupled Integration using Adaptive Unscented Particle Filter

2010 ◽  
Vol 63 (3) ◽  
pp. 491-511 ◽  
Author(s):  
Junchuan Zhou ◽  
Stefan Knedlik ◽  
Otmar Loffeld
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Unscented Kalman Filter ◽  
Estimation Accuracy ◽  
Proposal Distribution ◽  
Sampling Step ◽  
Computational Burden ◽  
Monte Carlo Experiments ◽  
Unscented Particle Filter ◽  
Tightly Coupled

With the rapid developments in computer technology, the particle filter (PF) is becoming more attractive in navigation applications. However, its large computational burden still limits its widespread use. One approach for reducing the computational burden without degrading the system estimation accuracy is to combine the PF with other filters, i.e., the extended Kalman filter (EKF) or the unscented Kalman filter (UKF). In this paper, the a posteriori estimates from an adaptive unscented Kalman filter (AUKF) are used to specify the PF importance density function for generating particles. Unlike the sequential importance sampling re-sampling (SISR) PF, the re-sampling step is not required in the algorithm, because the filter does not reuse the particles. Hence, the filter computational complexity can be reduced. Besides, the latest measurements are used to improve the proposal distribution for generating particles more intelligently. Simulations are conducted on the basis of a field-collected 3D UAV trajectory. GPS and IMU data are simulated under the assumption that a NovAtel DL-4plus GPS receiver and a Landmark™ 20 MEMS-based IMU are used. Navigation under benign and highly reflective signal environments are considered. Monte Carlo experiments are made. Numerical results show that the AUPF with 100 particles can present improved system estimation accuracy with an affordable computational burden when compared with the AEKF and AUKF algorithms.

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.

A Novel Hybrid Unscented Particle Filter based on Firefly Algorithm for Tightly-Coupled Stereo Visual-Inertial Vehicle Positioning

2019 ◽  
Vol 73 (3) ◽  
pp. 613-627
Author(s):  
Xiuyuan Li ◽  
Wenxue Gao ◽  
Jiashu Zhang
Keyword(s):  
Kalman Filter ◽  
Computational Complexity ◽  
Particle Filter ◽  
Firefly Algorithm ◽  
Unscented Kalman Filter ◽  
Estimation Accuracy ◽  
Positioning Systems ◽  
Unscented Particle Filter ◽  
Tightly Coupled ◽  
Vehicle Positioning

This paper presents a hybrid unscented particle filter (UPF) based on the firefly algorithm for tightly-coupled stereo visual-inertial vehicle positioning systems (VIVPS). Compared with standard UPF, this novel approach can achieve similar estimation accuracy with much less computational complexity. To reduce the computational complexity, the time updating of the hybrid unscented Kalman filter is conducted via the formula of standard linear Kalman filter on the basis of the constructed linear/nonlinear mixed filter model. The particle updating of the particle filter is optimised by modified firefly algorithm to reduce the number of particles needed by means of moving particles towards high likelihood regions via the attraction and movement of fireflies, leading to a significant reduction of computational complexity. Experimental results show the average execution time of the proposed approach is 23·8% that of the standard UPF with similar accuracy, indicating the designed method for tightly-coupled stereo VIVPS can better satisfy the real-time requirement of the system.

Nonlinear Non-Gaussian Filtering Algorithm Based on Cubature Kalman and Particle Filter

2013 ◽  
Vol 380-384 ◽  
pp. 1323-1326
Author(s):  
Zhang Kai ◽  
Gan Lin Shan
Keyword(s):  
Particle Filter ◽  
Transition Density ◽  
Estimation Accuracy ◽  
Linear Regression Method ◽  
Nonlinear Functions ◽  
Filtering Algorithm ◽  
Filter Performance ◽  
Unscented Particle Filter ◽  
Cubature Kalman Filter ◽  
Non Gaussian

To resolve the nonlinear non-Gaussian tracking problem effectively, a novel filtering algorithm based on Cubature Kalman Filter (CKF) and Particle Filters (PF) is proposed, which is called Cubature Kalman Particle Filter (CPF). CKF is used to generate the importance density function for PF. It linearizes the nonlinear functions using statistical linear regression method through a set of Gaussian cubature points. It need not compute the Jacobian matrix. Moreover, it makes efficient use of the latest observation information into system state transition density, thus greatly improving the filter performance. The simulation results show that CPF has higher estimation accuracy and less computational load comparing against the widely used Unscented Particle Filter (UPF).

Robust information unscented particle filter based on M‐estimate

2019 ◽  
Vol 13 (1) ◽  
pp. 14-20 ◽  
Author(s):  
Xiao‐Hang Wu ◽  
Shen‐Min Song
Keyword(s):  
Particle Filter ◽  
Unscented Particle Filter ◽  
M Estimate

scholarly journals Unscented Particle Filter Algorithm Based on Divide-and-Conquer Sampling for Target Tracking

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2236
Author(s):  
Sichun Du ◽  
Qing Deng
Keyword(s):  
Particle Filter ◽  
State Space ◽  
Adaptive Filtering ◽  
Divide And Conquer ◽  
Particle Swarm Algorithm ◽  
Tracking Accuracy ◽  
Filtering Algorithm ◽  
Maneuvering Target ◽  
Unscented Particle Filter ◽  
Particle Filter Algorithm

Unscented particle filter (UPF) struggles to completely cover the target state space when handling the maneuvering target tracing problem, and the tracking performance can be affected by the low sample diversity and algorithm redundancy. In order to solve this problem, the method of divide-and-conquer sampling is applied to the UPF tracking algorithm. By decomposing the state space, the descending dimension processing of the target maneuver is realized. When dealing with the maneuvering target, particles are sampled separately in each subspace, which directly prevents particles from degeneracy. Experiments and a comparative analysis were carried out to comprehensively analyze the performance of the divide-and-conquer sampling unscented particle filter (DCS-UPF). The simulation result demonstrates that the proposed algorithm can improve the diversity of particles and obtain higher tracking accuracy in less time than the particle swarm algorithm and intelligent adaptive filtering algorithm. This algorithm can be used in complex maneuvering conditions.

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