Estimation with non-white Gaussian observation noise using a generalised ENSEMBLE KALMAN filter

Čurn, Jan; Marinescu, Dan C.; Lacey, Gerard; Cahill, Vinny

doi:10.1109/rose.2012.6402618

Cited by 7 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…State prediction using Equation (10) Absolute positioning using Equation (19) Calculating relative positions for its neighbors using Equation ( 20) and determining the corresponding covariance matrices using Equation (26) Propagation and reception of relative information…”

Section: Initializationmentioning

confidence: 99%

See 1 more Smart Citation

Cooperative Vehicle Localization in Multi-Sensor Multi-Vehicle Systems Based on an Interval Split Covariance Intersection Filter with Fault Detection and Exclusion

Shan,

Cabani,

Chafouk

2024

Vehicles

View full text Add to dashboard Cite

In the cooperative multi-sensor multi-vehicle (MSMV) localization domain, the data incest problem yields inconsistent data fusion results, thereby reducing the accuracy of vehicle localization. In order to address this problem, we propose the interval split covariance intersection filter (ISCIF). At first, the proposed ISCIF method is applied to the absolute positioning step. Then, we combine the interval constraint propagation (ICP) method and the proposed ISCIF method to realize relative positioning. Additionally, in order to enhance the robustness of the MSMV localization system, a Kullback–Leibler divergence (KLD)-based fault detection and exclusion (FDE) method is implemented in our system. Three simulations were carried out: Simulation scenarios 1 and 2 aimed to assess the accuracy of the proposed ISCIF with various capabilities of absolute vehicle positioning, while simulation scenario 3 was designed to evaluate the localization performance when faults were present. The simulation results of scenarios 1 and 2 demonstrated that our proposed vehicle localization method reduced the root mean square error (RMSE) by 8.9% and 15.5%, respectively, compared to the conventional split covariance intersection filter (SCIF) method. The simulation results of scenario 3 indicated that the implemented FDE method could effectively reduce the RMSE of vehicles (by about 55%) when faults were present in the system.

show abstract

Section: Initializationmentioning

confidence: 99%

“…For cooperative vehicle localization in MSMVs, one serious problem is that of data incest [26], which yields inconsistent fusion localization results. When the same data are reused multiple times and these data are treated as independent in the data fusion process, the data incest problem will occur.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Vehicle Localization in Multi-Sensor Multi-Vehicle Systems Based on an Interval Split Covariance Intersection Filter with Fault Detection and Exclusion

Shan,

Cabani,

Chafouk

2024

Vehicles

View full text Add to dashboard Cite

show abstract

“…The UKF approximates the mean and the covariance of the state estimation using unscented transformation (UT) through sigma points [ 26 ]. The ensemble Kalman filter (EnKF) is another algorithm to capture state estimation using samples set for the purpose of handling non-Gaussian noises [ 27 ]. Another popular method is the Huber-based Kalman filter.…”

Section: Introductionmentioning

confidence: 99%

Maximum Correntropy Unscented Kalman Filter for Ballistic Missile Navigation System based on SINS/CNS Deeply Integrated Mode

Hou

Liu³

et al. 2018

Sensors

View full text Add to dashboard Cite

Strap-down inertial navigation system/celestial navigation system (SINS/CNS) integrated navigation is a high precision navigation technique for ballistic missiles. The traditional navigation method has a divergence in the position error. A deeply integrated mode for SINS/CNS navigation system is proposed to improve the navigation accuracy of ballistic missile. The deeply integrated navigation principle is described and the observability of the navigation system is analyzed. The nonlinearity, as well as the large outliers and the Gaussian mixture noises, often exists during the actual navigation process, leading to the divergence phenomenon of the navigation filter. The new nonlinear Kalman filter on the basis of the maximum correntropy theory and unscented transformation, named the maximum correntropy unscented Kalman filter, is deduced, and the computational complexity is analyzed. The unscented transformation is used for restricting the nonlinearity of the system equation, and the maximum correntropy theory is used to deal with the non-Gaussian noises. Finally, numerical simulation illustrates the superiority of the proposed filter compared with the traditional unscented Kalman filter. The comparison results show that the large outliers and the influence of non-Gaussian noises for SINS/CNS deeply integrated navigation is significantly reduced through the proposed filter.

show abstract

NIRK-based mixed-type accurate continuous–discrete Gaussian filters with deterministically sampled expectation and covariance for state estimation in continuous-time stochastic process models with discrete measurements

Kulikov,

Kulikova

2024

Journal of the Franklin Institute

View full text Add to dashboard Cite

Estimation with non-white Gaussian observation noise using a generalised ENSEMBLE KALMAN filter

Cited by 7 publications

References 13 publications

Cooperative Vehicle Localization in Multi-Sensor Multi-Vehicle Systems Based on an Interval Split Covariance Intersection Filter with Fault Detection and Exclusion

Cooperative Vehicle Localization in Multi-Sensor Multi-Vehicle Systems Based on an Interval Split Covariance Intersection Filter with Fault Detection and Exclusion

Maximum Correntropy Unscented Kalman Filter for Ballistic Missile Navigation System based on SINS/CNS Deeply Integrated Mode

NIRK-based mixed-type accurate continuous–discrete Gaussian filters with deterministically sampled expectation and covariance for state estimation in continuous-time stochastic process models with discrete measurements

Contact Info

Product

Resources

About