Novel strong tracking square‐root cubature kalman filter for GNSS/INS integrated navigation system

Yue, Zhe; Lian, Baowang; Tong, Kaixiang; Chen, Shaohua

doi:10.1049/iet-rsn.2018.5528

Cited by 12 publications

(5 citation statements)

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“…As an important navigation system in the aircraft, the inertial navigation system (INS) can obtain high-precision attitude, velocity and position through the integration of angular velocity and linear acceleration [1]. However, accumulated errors will occur under long flight, so it needs the assistance of other navigation systems through the Kalman filter [2]. INS and air data system (ADS) integrated navigation is a typical navigation system based on multisource fusion [3].…”

Section: Introductionmentioning

confidence: 99%

Fault detection approach applied to inertial navigation system/air data system integrated navigation system with time‐offset

Chen

Wang

et al. 2021

IET Radar Sonar & Navi

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The false alarm of a fault detection module in aircraft will interfere with flight. If there is a time-offset between the inertial navigation system (INS) and air data system (ADS), the probability of a false alarm (PFA) of the fault detection module will increase, which is ignored in existing approaches. To address the problem, a fault detection approach is proposed applied to INS/ADS with a time-offset. An INS/ADS fault detection model based on kinematic equations is developed, and we combine an unscented Kalman filter (UKF) with Runge-Kutta to deal with the non-linear and discretisation problem. A timeoffset estimator is designed and the observability of time-offset is analysed, showing that time-offset is observable only in the manoeuvre phase. A fault detection architecture applied to INS/ADS with a time-offset is designed, which solves the problem of the high PFA of INS/ADS fault detection under a time-offset. Simulation results show that under multiple time-offset scenarios, the root mean square error of the proposed approach can reach 0.0134 s minimally, and after time alignment, the PFA of the fault detection can be reduced to 1.8%.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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Section: Introductionmentioning

confidence: 99%

Fault detection approach applied to inertial navigation system/air data system integrated navigation system with time‐offset

Chen

Wang

et al. 2021

IET Radar Sonar & Navi

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“…But, the estimation accuracy of the UKF is limited for higher-order systems analysis. The CKF [3] can be developed and being widely applied into various real world estimation problems in [6], [7], [8], [9], [10], [11], [12].…”

Section: Introductionmentioning

confidence: 99%

“…System uncertainty impact on His with the Department of Mechanical Engineering, University of Surrey, Surrey, GU2 7XH, UK e-mail: (see n.mundla@surrey.ac.uk. filter performance and sub-optimal when the noise covariance matrices are varied under the fault measurements [7], [14]. By introducing the time-dependent variable, called fading factor, named it as Adaptive Fading Kalman Filter (AFKF) [18].…”

Section: Introductionmentioning

confidence: 99%

A new state estimation approach-Adaptive Fading Cubature Kalman filter

Narasimhappa

2021

Preprint

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This paper presents a novel adaptive fading cubature Kalman filter (AFCKF) based on double transitive factors. The developed adaptive algorithm is explained in two stages; stage (i) a single transitive factor is used to update the predicted state error covariance, Pk − based on innovation or residual vector, whereas, in stage (ii), the measurement noise covariance matrix, R * k is scaled by another transitive factor. Furthermore, showing the proof concept for estimation of the process noise, Q * k and measurement noise covariance matrices by combining the innovation and residual vector in the AFCKF algorithm. It can provide reliable state estimation in the presence of unknown noise statistics. Bench-marking target tracking example is consider to show the performance improvement of the developed algorithms. As compared with existing adaptive approaches, the proposed fading algorithm can provide better estimation results.

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“…The cubature Kalman filter (CKF) [1] is an approximate non-linear filtering algorithm based on the Bayesian filtering framework proposed by Canadian researcher Arasaratnam. Compared with extended Kalman filter (EKF), unscented Kalman filter (UKF), central difference Kalman filter (CDKF), particle filter (PF), and other traditional non-linear filter algorithms, it has higher filter accuracy [2,3], and widely used in manoeuvring target tracking [4], integrated navigation system [5,6], power system [7], and other fields.…”

Section: Introductionmentioning

confidence: 99%