Carrier Tracking Estimation Analysis by Using the Extended Strong Tracking Filtering

Ge, Quanbo; Shao, Teng; Chen, Shaodong; Wen, Chenglin

doi:10.1109/tie.2016.2610403

Cited by 52 publications

(23 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Reference [20], He et al used the residual signal to detect sensor bias faults first and then employed residual analysis for faults isolation. Ge et al [21] studied a performance comparison of the strong tracking filter and Kalman filter. In Reference [22], Zhao et al designed an adaptive robust square-root cubature Kalman filter (CKF) with the noise statistic estimator to solve the decline or divergence problem of the accuracy of the CKF.…”

Section: Introductionmentioning

confidence: 99%

A Novel Low-Complexity Fault Diagnosis Algorithm for Energy Internet in Smart Cities

et al. 2020

View full text Add to dashboard Cite

The smart energy system, viewed as an “Energy Internet”, consists of the intelligent integration of decentralized sustainable energy sources, efficient distribution, and optimized power consumption. That implies the fault diagnosis for a smart energy system should be of low complexity. In this paper, we propose a Strong Tracking Unscented Kalman Filter ( S T U K F ) and modified Bayes’ classification-based Modified Three Sigma test ( M T S ), abbreviated as S F B T , for smart energy networks. The theoretical analysis and simulations indicate that S F B T detects faults with a high accuracy and a low complexity of O ( n ) .

show abstract

Section: Introductionmentioning

confidence: 99%

A Novel Low-Complexity Fault Diagnosis Algorithm for Energy Internet in Smart Cities

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In practical applications, the characteristics of the noise in the positioning system which affect the positioning accuracy cannot be determined. Adaptive filtering algorithms have been adopted to reduce the drifts and errors, including the fuzzy logic adaptive filter [ 30 ], Sage–Husa Adaptive Filter (SHAF) [ 31 ], and Strong Tracking Filter (STF) [ 32 ]. The SHAF can estimate the statistical characteristics of noise in real time, but cannot identify outliers within the measurement data; this reduces the fault tolerance of the positioning systems.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Enhanced Kalman Filter for a ZUPT-Aided Pedestrian Positioning Coupling Model

Fan

Zhang

Sun

et al. 2018

Sensors

View full text Add to dashboard Cite

Aimed at overcoming the problems of cumulative errors and low positioning accuracy in single Inertial Navigation Systems (INS), an Optimal Enhanced Kalman Filter (OEKF) is proposed in this paper to achieve accurate positioning of pedestrians within an enclosed environment. Firstly, the errors of the inertial sensors are analyzed, modeled, and reconstructed. Secondly, the cumulative errors in attitude and velocity are corrected using the attitude fusion filtering algorithm and Zero Velocity Update algorithm (ZUPT), respectively. Then, the OEKF algorithm is described in detail. Finally, a pedestrian indoor positioning experimental platform is established to verify the performance of the proposed positioning system. Experimental results show that the accuracy of the pedestrian indoor positioning system can reach 0.243 m, giving it a high practical value.

show abstract

“…Nonlinear systems have many filtering methods including the extended Kalman filter (EKF) [ 9 , 10 ], unscented Kalman filter (UKF) [ 11 , 12 ], cubature Kalman filter (CKF) [ 13 ], Sequence Monte Carlo (SMC) [ 14 , 15 ], Markov Chain Monte Carlo (MCMC) [ 16 ], particle filter (PF) [ 17 , 18 , 19 , 20 , 21 ], and so on. As PF does not demand the system noises to be Gaussian, it can be applied to more situations.…”

Section: Introductionmentioning

confidence: 99%

Weighted Measurement Fusion Particle Filter for Nonlinear Systems with Correlated Noises

Zhang

Hao

Sun

2018

Sensors

View full text Add to dashboard Cite

The multi-sensor information fusion particle filter (PF) has been put forward for nonlinear systems with correlated noises. The proposed algorithm uses the Taylor series expansion method, which makes the nonlinear measurement functions have a linear relationship by the intermediary function. A weighted measurement fusion PF (WMF-PF) was put forward for systems with correlated noises by applying the full rank decomposition and the weighted least square theory. Compared with the augmented optimal centralized fusion particle filter (CF-PF), it could greatly reduce the amount of calculation. Moreover, it showed asymptotic optimality as the Taylor series expansion increased. The simulation examples illustrate the effectiveness and correctness of the proposed algorithm.

show abstract

Carrier Tracking Estimation Analysis by Using the Extended Strong Tracking Filtering

Cited by 52 publications

References 9 publications

A Novel Low-Complexity Fault Diagnosis Algorithm for Energy Internet in Smart Cities

A Novel Low-Complexity Fault Diagnosis Algorithm for Energy Internet in Smart Cities

An Optimal Enhanced Kalman Filter for a ZUPT-Aided Pedestrian Positioning Coupling Model

Weighted Measurement Fusion Particle Filter for Nonlinear Systems with Correlated Noises

Contact Info

Product

Resources

About