scholarly journals Multisensor decentralized nonlinear fusion using adaptive cubature information filter

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0241517
Author(s):  
Binglei Guan ◽  
Xianfeng Tang
Keyword(s):  
Estimation Accuracy ◽  
Fusion Center ◽  
Unknown Variance ◽  
Information Filter ◽  
State Changes ◽  
Fading Factor ◽  
Tracking Filter ◽  
Fusion Framework ◽  
Equivalent Description ◽  
New Algorithms

In nonlinear multisensor system, abrupt state changes and unknown variance of measurement noise are very common, which challenges the majority of the previously developed models for precisely known multisensor fusion techniques. In terms of this issue, an adaptive cubature information filter (CIF) is proposed by embedding strong tracking filter (STF) and variational Bayesian (VB) method, and it is extended to multi-sensor fusion under the decentralized fusion framework with feedback. Specifically, the new algorithms use an equivalent description of STF, which avoid the problem of solving Jacobian matrix during determining strong trace fading factor and solve the interdependent problem of combination of STF and VB. Meanwhile, A simple and efficient method for evaluating global fading factor is developed by introducing a parameter variable named fading vector. The analysis shows that compared with the traditional information filter, this filter can effectively reduce the data transmission from the local sensor to the fusion center and decrease the computational burden of the fusion center. Therefore, it can quickly return to the normal error range and has higher estimation accuracy in response to abrupt state changes. Finally, the performance of the developed algorithms is evaluated through a target tracking problem.

A Strong Tracking Cubature Kalman Filter for Nonlinear Estimation

2013 ◽  
Vol 313-314 ◽  
pp. 1115-1119
Author(s):  
Yong Qi Wang ◽  
Feng Yang ◽  
Yan Liang ◽  
Quan Pan
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Scaling Factor ◽  
Estimation Accuracy ◽  
Nonlinear State Estimation ◽  
Cubature Kalman Filter ◽  
Novel Method ◽  
Simulation Results ◽  
Tracking Filter ◽  
Nonlinear State

In this paper, a novel method based on cubature Kalman filter (CKF) and strong tracking filter (STF) has been proposed for nonlinear state estimation problem. The proposed method is named as strong tracking cubature Kalman filter (STCKF). In the STCKF, a scaling factor derived from STF is added and it can be tuned online to adjust the filtering gain accordingly. Simulation results indicate STCKF outperforms over EKF and CKF in state estimation accuracy.

Sensor Fusion Based on Strong Tracking Filter for Augmented Reality Registration

2011 ◽  
Vol 467-469 ◽  
pp. 108-113
Author(s):  
Xin Yu Li ◽  
Dong Yi Chen
Keyword(s):  
Augmented Reality ◽  
Inertial Sensor ◽  
Measurement Data ◽  
Sensor Data ◽  
Data Synchronization ◽  
Error Covariance ◽  
Tightly Coupled ◽  
Strong Tracking Filter ◽  
Fading Factor ◽  
Tracking Filter

Accurate tracking for Augmented Reality applications is a challenging task. Multi-sensors hybrid tracking generally provide more stable than the effect of the single visual tracking. This paper presents a new tightly-coupled hybrid tracking approach combining vision-based systems with inertial sensor. Based on multi-frequency sampling theory in the measurement data synchronization, a strong tracking filter (STF) is used to smooth sensor data and estimate position and orientation. Through adding time-varying fading factor to adaptively adjust the prediction error covariance of filter, this method improves the performance of tracking for fast moving targets. Experimental results show the efficiency and robustness of this proposed approach.

scholarly journals Kriging with Unknown Variance Components for Regional Ionospheric Reconstruction

2016 ◽  
Author(s):  
Ling Huang ◽  
Hongping Zhang ◽  
Peiliang Xu ◽  
Jianghui Geng ◽  
Cheng Wang ◽  
...  
Keyword(s):  
Ordinary Kriging ◽  
Variance Components ◽  
Harmonic Functions ◽  
Measurement Errors ◽  
Spatial Interpolation ◽  
Interpolation Method ◽  
Single Frequency ◽  
Estimation Accuracy ◽  
Spherical Cap ◽  
Unknown Variance

Ionospheric delay has been a critical issue that limits the accuracy of GNSS precise positioning and navigation for single-frequency users, especially in mid- and low-latitude regions where irregularity of ionosphere is often significant. Kriging spatial interpolation techniques have been recently introduced to model the spatial correlation and variability of ionosphere, which intrinsically assume that the ionosphere field is stochastically stationary but does not take the random observational errors into account. In this paper, by treating the spatial statistical information on ionosphere as prior knowledge and based on TEC semivariogram analysis, we use Kriging techniques to spatially interpolate TEC values. By assuming that the stochastic models of both the ionospheric signals and measurement errors are only known up to some unknown factors, we propose a new Kriging spatial interpolation method with unknown variance components for both the signals of ionosphere and TEC measurements. Variance component estimation has been integrated with Kriging to reconstruct regional ionospherical delay. The method has been applied to data from the Crustal Movement Observation Network of China (CMONOC) and compared with the ordinary Kriging and polynomial interpolations with spherical cap harmonic functions, polynomial functions and low-degree spherical harmonic functions. The results have shown that the interpolation accuracy of the new proposed method is better than the ordinary Kriging and polynomial interpolation by about 1.2 TECU and 0.7 TECU, respectively. The root mean squared error of the proposed new Kriging with variance components is within 1.5 TECU and is smaller than those from other methods under comparison by about 1 TECU. When compared with ionospheric grid points, the mean squared error of the proposed method is within 6 TECU and smaller than Kriging, indicating that the proposed method can produce more accurate ionospheric delays and better estimation accuracy.

Robust high-degree cubature information filter and its application to trajectory estimation for ballistic missile

2017 ◽  
Vol 232 (12) ◽  
pp. 2364-2377 ◽  
Author(s):  
Xiaogang Wang ◽  
Wutao Qin ◽  
Naigang Cui ◽  
Yu Wang
Keyword(s):  
State Estimation ◽  
Estimation Algorithm ◽  
Ballistic Missile ◽  
Measurement Noise ◽  
Estimation Accuracy ◽  
Trajectory Estimation ◽  
Information Filter ◽  
Non Gaussian ◽  
Time Update ◽  
High Degree

This paper presents a new recursive filter algorithm, the robust high-degree cubature information filter, which can provide reliable state estimation in the presence of non-Gaussian measurement noise. The novel algorithm is developed in the framework of the conventional information filter. The fifth-degree Cubature rule is utilized to improve the estimation accuracy and numerical stability during the time update, while the Huber technique is adopted in the measurements update stage. As the Huber technique is a combined minimum l1 and l2 norm estimation algorithm, the proposed algorithm could exhibit robustness to the non-Gaussian measurement noise, especially the glint noise. In addition, Monte Carlo simulation and the trajectory estimation for ballistic missile experiments demonstrate that the robust high-degree cubature information filter can provide improved state estimation performance over extended information filter and high-degree cubature information filter.

Structural-parametric synthesis of the tracking filter based on decomposition by the target functional with adaptation to trajectory disturbances

2021 ◽  
Author(s):  
A.A. Kostoglotov ◽  
A.S. Penkov ◽  
S.V. Lazarenko
Keyword(s):  
Numerical Modeling ◽  
State Assessment ◽  
Estimation Accuracy ◽  
Motion Model ◽  
Parametric Synthesis ◽  
Adaptive Motion ◽  
Accuracy Criterion ◽  
Dynamic Errors ◽  
Tracking Filter ◽  
Kinematic Motion

Traditional Kalman-type tracking filters are based on a kinematic motion model, which leads to the occurrence of dynamic errors, which significantly increase during target maneuvering. One of the solutions to this problem is to develop a model of motion dynamics with the ability to adapt its structure to external influences. It is shown that the use of a dynamic model of motion in the filter, which takes into account the inertia of the target and the forces acting on it, makes it possible to significantly increase the efficiency of the state assessment. Purpose is to development of an algorithm for assessing the position of a maneuvering object, effective in terms of accuracy criterion. The use of an adaptive motion model as part of the filter provides an increase in the estimation accuracy in comparison with the classical Kalman filter, which is confirmed by the performed numerical modeling.

scholarly journals A Strong Tracking SLAM Algorithm Based on the Suboptimal Fading Factor

2018 ◽  
Vol 2018 ◽  
pp. 1-14
Author(s):  
Yunpei Liang ◽  
Jiahui Dai ◽  
Kequan Wang ◽  
Xiaobo Li ◽  
Pengcheng Xu
Keyword(s):  
State Estimation ◽  
Unscented Kalman Filter ◽  
Sigma Point ◽  
True Value ◽  
Localization And Mapping ◽  
Adaptive Adjustment ◽  
Fading Factor ◽  
Slam Algorithm ◽  
Tracking Filter ◽  
Initial Selection

This paper proposes an innovative simultaneous localization and mapping (SLAM) algorithm which combines a strong tracking filter (STF), an unscented Kalman filter (UKF), and a particle filter (PF) to deal with the low accuracy of unscented FastSLAM (UFastSLAM). UFastSLAM lacks the capacity for online self-adaptive adjustment, and it is easily influenced by uncertain noise. The new algorithm updates each Sigma point in UFastSLAM by an adaptive algorithm and obtains optimized filter gain by the STF adjustment factor. It restrains the influence of uncertain noise and initial selection. Therefore, the state estimation would converge to the true value rapidly and the accuracy of system state estimation would be improved eventually. The results of simulations and practical tests show that strong tracking unscented FastSLAM (STUFastSLAM) has a significant improvement in accuracy and robustness.

scholarly journals Adaptive Fifth-Degree Cubature Information Filter for Multi-Sensor Bearings-Only Tracking

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3241 ◽  
Author(s):  
Haonan Jiang ◽  
Yuanli Cai
Keyword(s):  
Information Filtering ◽  
Estimation Procedure ◽  
Estimation Accuracy ◽  
Selection Strategy ◽  
Bayesian Filtering ◽  
Process Noise ◽  
Covariance Matching ◽  
Information Filter ◽  
Cubature Kalman Filter ◽  
Noise Covariance

Standard Bayesian filtering algorithms only work well when the statistical properties of system noises are exactly known. However, this assumption is not always plausible in real target tracking applications. In this paper, we present a new estimation approach named adaptive fifth-degree cubature information filter (AFCIF) for multi-sensor bearings-only tracking (BOT) under the condition that the process noise follows zero-mean Gaussian distribution with unknown covariance. The novel algorithm is based on the fifth-degree cubature Kalman filter and it is constructed within the information filtering framework. With a sensor selection strategy developed using observability theory and a recursive process noise covariance estimation procedure derived using the covariance matching principle, the proposed filtering algorithm demonstrates better estimation accuracy and filtering stability. Simulation results validate the superiority of the AFCIF.

scholarly journals Adaptive Consensus-Based Unscented Information Filter for Tracking Target with Maneuver and Colored Noise

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3069 ◽  
Author(s):  
Li ◽  
Wang ◽  
Zheng
Keyword(s):  
State Estimation ◽  
Situation Awareness ◽  
Information Matrix ◽  
Estimation Method ◽  
Measurement Noise ◽  
Distributed State Estimation ◽  
Information Filter ◽  
Fading Factor ◽  
Stability And Accuracy ◽  
The Impact

Distributed state estimation plays a key role in space situation awareness via a sensor network. This paper proposes two adaptive consensus-based unscented information filters for tracking target with maneuver and colored measurement noise. The proposed filters can fulfill the distributed estimation for non-linear systems with the aid of a consensus strategy, and can reduce the impact of colored measurement noise by employing the state augmentation and measurement differencing methods. In addition, a fading factor that shrinks the predicted information state and information matrix can suppress the impact of dynamical model error induced by target maneuvers. The performances of the proposed algorithms are investigated by considering a target tracking problem using a space-based radar network. This shows that the proposed algorithms outperform the traditional consensus-based distributed state estimation method in aspects of tracking stability and accuracy.

Modified Strong Tracking Filter for the SOC Estimation of Lithium Battery in Electric Vehicle

2012 ◽  
Vol 236-237 ◽  
pp. 1362-1367
Author(s):  
Ze Cheng ◽  
Yu Hui Zhang ◽  
Yan Li Liu
Keyword(s):  
Electric Vehicle ◽  
Lithium Battery ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Original Algorithm ◽  
Soc Estimation ◽  
Tracking Ability ◽  
Strong Tracking Filter ◽  
Vehicle Management ◽  
Tracking Filter

In electric vehicle management system, the key technology is to estimate the SOC (state of charge) of battery in dynamic process. This paper builds the state space equation of lithium battery based on improved Randle’s model, realizes the tracking estimation of SOC using modified STF(strong tracking filter) algorithm. The simulation result proves that compared with the original algorithm, modified algorithm can greatly improve the system’s tracking ability as well as the estimation accuracy of SOC.

Strong Tracking Filter Based on Extended Kalman Filter for Data Processing of Underwater Vehicle

2011 ◽  
Vol 219-220 ◽  
pp. 569-573
Author(s):  
Ye Li ◽  
Zhen Lu ◽  
Yong Jie Pang
Keyword(s):  
Kalman Filter ◽  
Nonlinear System ◽  
Data Processing ◽  
Extended Kalman Filter ◽  
Underwater Vehicle ◽  
Underwater Navigation ◽  
Strong Tracking Filter ◽  
Fading Factor ◽  
Tracking Filter ◽  
Navigation Information

A strong tracking filter based on suboptimal fading extended Kalman filter was proposed to ensure the perception for the motion state of underwater vehicles accurate in the paper. For the uncertainty of nonlinear system model, the strong tracking filter theory was introduced, orthogonality principle was put forward. Then suboptimal fading factor was pulled in, and extended Kalman filter for nonlinear system was established. The strong tracking filter was applied to data processing of underwater vehicle, and results indicate that it can effectively improve the accuracy and robustness of underwater navigation information.

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