Adaptive Kalman Filter for INS/GPS Integrated Navigation System

Xu, Tian

doi:10.4028/www.scientific.net/amm.336-338.332

Cited by 6 publications

(7 citation statements)

References 4 publications

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“…

ϵ^{bold-italicn} = [δ R, δ P, δ Y]

, and

δ R, δ P, δ Y

are roll, pitch, and yaw errors. Eventually, the error‐based model propagation is obtained as shown in [4, 11].

δ {\overset{r}{true}}^{bold-italicn} = {bold-italicF}_{r r} δ {bold-italicr}^{bold-italicn} + {bold-italicF}_{r v} δ {bold-italicv}^{bold-italicn}

δ {\overset{v}{true}}^{bold-italicn} = {bold-italicF}_{bold-italicvr} δ {bold-italicr}^{bold-italicn} + {bold-italicF}_{bold-italicvv} δ {bold-italicv}^{bold-italicn} + ({bold-italicf}^{bold-italicn} \times) ϵ^{bold-italicn} + {bold-italicC}_{bold-italicb}^{bold-italicn} δ {bold-italicf}^{bold-italicb}

{\overset{ϵ}{true}}^{bold-italicn} = {bold-italicF}_{e r} δ {bold-italicr}^{bold-italicn} + {bold-italicF}_{e v} δ {bold-italicv}^{bold-italicn} - ({bold-italicω}_{i n}^{bold-italicn} \times) ϵ^{bold-italicn} - {bold-italicC}_{bold-italicb}^{bold-italicn} δ {bold-italicω}^{bold-italicb}

where

{bold-italicF}_{bold-italicrr}

{bold-italicF}_{bold-italicrv}

{bold-italicF}_{bold-italicvr}

{bold-italicF}_{}

…”

Section: Modelling Of Ins Error Dynamic Based On Lc Structureunclassified

“…Hence, a wide range of research works has been carried out to improve the system accuracy and robustness, such as multiple structures of adaptive and robust KFs [6, 8, 9]. The multiple model adaptive estimation and the multiple model adaptive KF use cumulative samples of innovation vector to tune

{bold-italicR}_{k}

and

{bold-italicQ}_{k}

through a bank of KFs, which can increase the computation burden, especially in practical implementation [7, 10, 11]. The innovation‐based Adaptive Estimation (IAE) is assumed as another type of adaptive KFs, which uses correspondingly the innovation/residual vector to adapt the process and covariance matrices.…”

Section: Introductionmentioning

confidence: 99%

“…The IAE Adaptive Kalman Filtering is used in [6], in which the adaption section is based on the memory attenuation factor to diminish the influence of previous data. In some cases, the adaption procedure is based on the innovation vector, adaptive scale factor, or weighing these covariance matrices designed to tune the

{bold-italicR}_{k}

, while in most situations the adaption of either

{bold-italicR}_{k}

{bold-italicQ}_{k}

is required [11, 12]. The Sage–Husa adaptive KF technique is employed in [9, 12, 13], which uses the covariance matching technique to achieve higher accuracy by estimating the noise feature and obtains good results.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

INS/GPS Sensor Fusion based on Adaptive Fuzzy EKF with Sensitivity to Disturbances

Sabzevari

Chatraei

2021

IET Radar Sonar & Navi

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The estimation accuracy of the inertial navigation system integrated with the global positioning system (GPS) through multiple kinds of Kalman filters (KFs) has been widely considered. Since the classical KFs could not overcome environmental disturbances and noises, adaptive and robust structures are utilised in sensor fusion techniques. Here, different types of adaptive structures have been assumed. The fuzzy inference system benefits the adaption of the measurement covariance matrix, a scale factor employed to tune the process covariance matrix and the Chi-square algorithm to detect and bound the disturbances. The estimation accuracy and robustness of the adaptive fuzzy extended Kalman filter (AFEKF) are compared with the unscented Kalman filter (UKF) and extended Kalman filter (EKF) structure in various scenarios involving position, velocity, attitude, accelerometer and gyroscope bias estimation errors. Likewise, in order to evaluate the AFEKF approach practically, an embedded electronic board is designed involving an ARM microcontroller, an inertial measurement unit sensor, and a GPS receiver, which was installed on a land vehicle. The results demonstrated the superiority of the AFEKF over the UKF and EKF in the case of lower estimation error and higher robustness against disturbances and outliers.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…

ϵ^{bold-italicn} = [δ R, δ P, δ Y]

, and

δ R, δ P, δ Y

are roll, pitch, and yaw errors. Eventually, the error‐based model propagation is obtained as shown in [4, 11].

δ {\overset{r}{true}}^{bold-italicn} = {bold-italicF}_{r r} δ {bold-italicr}^{bold-italicn} + {bold-italicF}_{r v} δ {bold-italicv}^{bold-italicn}

δ {\overset{v}{true}}^{bold-italicn} = {bold-italicF}_{bold-italicvr} δ {bold-italicr}^{bold-italicn} + {bold-italicF}_{bold-italicvv} δ {bold-italicv}^{bold-italicn} + ({bold-italicf}^{bold-italicn} \times) ϵ^{bold-italicn} + {bold-italicC}_{bold-italicb}^{bold-italicn} δ {bold-italicf}^{bold-italicb}

{\overset{ϵ}{true}}^{bold-italicn} = {bold-italicF}_{e r} δ {bold-italicr}^{bold-italicn} + {bold-italicF}_{e v} δ {bold-italicv}^{bold-italicn} - ({bold-italicω}_{i n}^{bold-italicn} \times) ϵ^{bold-italicn} - {bold-italicC}_{bold-italicb}^{bold-italicn} δ {bold-italicω}^{bold-italicb}

where

{bold-italicF}_{bold-italicrr}

{bold-italicF}_{bold-italicrv}

{bold-italicF}_{bold-italicvr}

{bold-italicF}_{}

…”

Section: Modelling Of Ins Error Dynamic Based On Lc Structureunclassified

{bold-italicR}_{k}

and

{bold-italicQ}_{k}

Section: Introductionmentioning

confidence: 99%

{bold-italicR}_{k}

, while in most situations the adaption of either

{bold-italicR}_{k}

{bold-italicQ}_{k}

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

INS/GPS Sensor Fusion based on Adaptive Fuzzy EKF with Sensitivity to Disturbances

Sabzevari

Chatraei

2021

IET Radar Sonar & Navi

View full text Add to dashboard Cite

show abstract

“…Furthermore, both robust and adaptive robust Kalman filters [ 23 , 24 , 25 , 26 , 27 , 28 ] have been discussed regarding controller and measurement outliers. These approaches can lead to better performance [ 29 , 30 , 31 , 32 , 33 , 34 ] in term of robustness and adaptivity due to the IAE method and corresponding equivalent weight matrix derived from the Huber function [ 35 ]. However, they could be further improved, especially under the situations that the statistics of both measurement and state noise have to be adapted.…”

Section: Introductionmentioning

confidence: 99%

A Modified Extended Kalman Filter for a Two-Antenna GPS/INS Vehicular Navigation System

Hao

Xin

et al. 2018

Sensors

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Recently, the integration of an inertial navigation system (INS) and the Global Positioning System (GPS) with a two-antenna GPS receiver has been suggested to improve the stability and accuracy in harsh environments. As is well known, the statistics of state process noise and measurement noise are critical factors to avoid numerical problems and obtain stable and accurate estimates. In this paper, a modified extended Kalman filter (EKF) is proposed by properly adapting the statistics of state process and observation noises through the innovation-based adaptive estimation (IAE) method. The impact of innovation perturbation produced by measurement outliers is found to account for positive feedback and numerical issues. Measurement noise covariance is updated based on a remodification algorithm according to measurement reliability specifications. An experimental field test was performed to demonstrate the robustness of the proposed state estimation method against dynamic model errors and measurement outliers.

show abstract

“…The Kalman filter (KF) is an effective optimal estimation algorithm and has been widely used in integrated navigation system since 1970s [6][7][8][9]. On one hand, in order to achieve a better performance with a KF, an accurate system model and reliable observation data are indispensable.…”

Section: Introductionmentioning

confidence: 99%

A Fault-Tolerant Filtering Algorithm for SINS/DVL/MCP Integrated Navigation System

Liu

2015

Mathematical Problems in Engineering

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The Kalman filter (KF), which recursively generates a relatively optimal estimate of underlying system state based upon a series of observed measurements, has been widely used in integrated navigation system. Due to its dependence on the accuracy of system model and reliability of observation data, the precision of KF will degrade or even diverge, when using inaccurate model or trustless data set. In this paper, a fault-tolerant adaptive Kalman filter (FTAKF) algorithm for the integrated navigation system composed of a strapdown inertial navigation system (SINS), a Doppler velocity log (DVL), and a magnetic compass (MCP) is proposed. The evolutionary artificial neural networks (EANN) are used in self-learning and training of the intelligent data fusion algorithm. The proposed algorithm can significantly outperform the traditional KF in providing estimation continuously with higher accuracy and smoothing the KF outputs when observation data are inaccurate or unavailable for a short period. The experiments of the prototype verify the effectiveness of the proposed method.

show abstract

Adaptive Kalman Filter for INS/GPS Integrated Navigation System

Cited by 6 publications

References 4 publications

INS/GPS Sensor Fusion based on Adaptive Fuzzy EKF with Sensitivity to Disturbances

INS/GPS Sensor Fusion based on Adaptive Fuzzy EKF with Sensitivity to Disturbances

A Modified Extended Kalman Filter for a Two-Antenna GPS/INS Vehicular Navigation System

A Fault-Tolerant Filtering Algorithm for SINS/DVL/MCP Integrated Navigation System

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