Adaptive fading Kalman filter with an application

Xia, Qijun; Rao, Ming; Ying, Yiqun; Shen, Xuemin

doi:10.1016/0005-1098(94)90112-0

Cited by 215 publications

(144 citation statements)

References 9 publications

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“…The first one is employing the unmodeled state which increases the complexity of the system for its computation [8]. The second is employing the process noise to improve the confidence when using white Gaussian noise so that it can prevent the EKF to reject new measurements for estimating the state vector [7,9,10]. This paper suggests using Fuzzy Inference System (FIS) based on the second solution, which is employed for self-tuning the EKF parameters by observing the covariance measurements.…”

Section: Accuracy Requirement Of Location-based Telematics Services (mentioning

confidence: 99%

A New Survey on Self-Tuning Integrated Low-Cost GPS/Ins Vehicle Navigation System in Harsh Environment

Navidi

Landry

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Nowadays, Global Positioning System (GPS) receivers are aided by some complementary radio navigation systems and Inertial Navigation Systems (INS) to obtain more accuracy and robustness in land vehicular navigation. Extended Kalman Filter (EKF) is an acceptable conventional method to estimate the position, the velocity, and the attitude of the navigation system when INS measurements are fused with GPS data. However, the usage of the low-cost Inertial Measurement Units (IMUs) based on the MicroElectro-Mechanical Systems (MEMS), for the land navigation systems, reduces the precision and stability of the navigation system due to their inherent errors. The main goal of this paper is to provide a new model for fusing low-cost IMU and GPS measurements. The proposed model is based on EKF aided by Fuzzy Inference Systems (FIS) as a promising method to solve the mentioned problems. This model considers the parameters of the measurement noise to adjust the measurement and noise process covariance. The simulation results show the efficiency of the proposed method to reduce the navigation system errors compared with EKF.

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Section: Accuracy Requirement Of Location-based Telematics Services (mentioning

confidence: 99%

A New Survey on Self-Tuning Integrated Low-Cost GPS/Ins Vehicle Navigation System in Harsh Environment

Navidi

Landry

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Covariance matching techniques have been proved to CKF and UKF to build adaptive CKF [6] and adaptive UKF, which possess better adaptability and robustness. Strong tracking filter [7] is an innovation covariance matching based adaptive EKF with a time-varying suboptimal fading factor. STF effectively solves the drawback of EKF when the process model is uncertain.…”

Section: Introductionmentioning

confidence: 99%

Improved Strong Tracking Cubature Kalman Filter for Target Tracking

Bao¹,

Zhang²

2017

Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)

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Abstract-Cubature Kalman filter (CKF) is a very popular nonlinear filter algorithm recently. CKF obtains better numerical stability and accuracy in high dimensional situation compared to UKF. However, in case of process model uncertainty, the performance of CKF will greatly degrade or even provoke divergence. An improved strong tracking CKF (ISTCKF) is proposed to keep the numerical stability and improve the robustness. First, the theoretical framework of strong tracking filter (STF) is combined with CKF. Then, an enhanced fault detection and isolation technique is established to overcome the drawback in STCKF. ISTCKF only performs correction phase when the process model uncertainty is detected and isolated. The ISTCKF is tested and validated via a target tracking model.

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“…To prevent the divergence problem due to modeling errors in the EKF approach, the adaptive filter algorithm can be one of the good strategies for estimating the state vector. This chapter suggests the adaptive fading Kalman filter (AFKF) (Levy, 1997;Xia et al, 1994) approach as a robust solution. The AFKF essentially employs suboptimal fading factors to improve the tracking capability.…”

Section: Introductionmentioning

confidence: 99%