An improved nonlinear filter based on adaptive fading factor applied in alignment of SINS

Zha, Feng; Guo, Shiluo; Li, Feng

doi:10.1016/j.ijleo.2019.01.100

Cited by 14 publications

(13 citation statements)

References 17 publications

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“…In other studies [30] [32], the factor c is only applied toP xx . However, the literature claims that the modification of the algorithm is based on the UKF using UT twice in each loop.…”

Section: Algorithm Improvementmentioning

confidence: 99%

“…The method presented in [30] and [31] are only applicable to GNSS and related types of attitude measurement methods. In [32], an improved fading UKF is proposed. The new alignment algorithm performs better in terms of robustness and convergence in the condition of complex measurement noise.…”

Section: Introductionmentioning

confidence: 99%

“…A step-by-step expression of the state equations for KF is presented in subsection IV-C. The algorithms of the adaptive UKF [36] [30] [32] are discussed in subsection IV-D to estimate the true geomagnetic signal and the disturbing magnetic field. The filtering results and a comparison are presented in subsection IV-E.…”

Section: Introductionmentioning

confidence: 99%

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Online Correction Method for Ratio Methods by Using Geomagnetic Sensors Based on Kalman Filter

Wang

Liu

2019

IEEE Access

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By eliminating the data offset caused by a disturbing magnetic field during the flying condition of a spinning projectile, an adaptive unscented Kalman filter is applied to estimate the actual data of a geomagnetic field and the false value introduced by a disturbing magnetic field. For the ratio methods related to geomagnetic attitude measurement, the physical significance of the ratios obtained by all ratio methods is clarified. A new definition for the ratio is presented to ensure online estimation and numerical stability. The variation in the sensor outputs along the lateral directions is suggested to be a harmonic motion with the assumption that the spindle is fixed or in slow motion. The true data employed as a reference for comparison is generated by a dynamics model for spinning objects and by projecting the geomagnetic field to the measurement frame. Signal gathering and data preprocessing are considered. A signal-disturbing ratio is introduced to indicate the proportions of the real geomagnetic signal and the interference signal in the data acquisition process. The algorithm of the adaptive unscented Kalman filter is discussed. Simulations are performed using a 1 kHz sample rate and a duration of 60 seconds. The filtering results supported by a conventional unscented Kalman filter and adaptive unscented Kalman filters are compared with actual data for the conditions of different signal-disturbing ratios and 10 shifts of the disturbing magnetic field. The results of the simulation indicate that the proposed adaptive unscented Kalman filter with the introduced state equation achieves better performance than other filters mentioned in the paper. The filter has the ability to provide clean data from the noisy data flow measured from a spinning projectile for the ratio methods based on the geomagnetic measurements. INDEX TERMS Geomagnetic measurement, attitude estimation, Kalman filter, data correction, ratio methods.

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“…In other studies [30] [32], the factor c is only applied toP xx . However, the literature claims that the modification of the algorithm is based on the UKF using UT twice in each loop.…”

Section: Algorithm Improvementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Online Correction Method for Ratio Methods by Using Geomagnetic Sensors Based on Kalman Filter

Wang

Liu

2019

IEEE Access

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“…In Ref. [10][11][12], unscented Kalman filter (UKF) uses lossless transformation to make nonlinear system equation applicable to standard Kalman filter system under linear assumption, instead of recursive filtering by linearizing nonlinear function as EKF does. The filtering accuracy of the system has high strength.…”

Section: Introductionmentioning

confidence: 99%

Combination of IMM Algorithm and ASTRWCKF for Maneuvering Target Tracking

Jin

Guo

2020

IEEE Access

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In this paper, an improved interactive multiple model adaptive strong tracking random weighted cubature Kalman filter (IIMM-ASTRWCKF) algorithm is developed to overcome the low tracking accuracy and easy divergence when dealing with complex maneuvering situations. The algorithm is improved in two aspects: On the one hand, ASTRWCKF is used as the sub filter of IMM algorithm to filter different motion models. By introducing the random weight factor to replace the original weight factor, the accuracy of the algorithm is improved. At the same time, the adaptive strong tracking filter is added to update the prediction covariance matrix and noise covariance matrix for the stability of the algorithm. On the other hand, this algorithm proposes a new method to improve the probability conversion accuracy of IMM by adding time-varying factor to adjust Markov probability transfer matrix. Compared with the performance of IMM-CKF and in dealing with maneuvering problems, IIMM-ASTRWCKF algorithm has better tracking accuracy in solving maneuvering problem.

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“…For SINS initial alignment with the nonlinear error model and disturbance noise uncertainty, the H∞ filter was adopted [ 29 , 30 , 31 ]; thus, the accuracy and robustness of alignment were improved. For SINS initial alignment under inaccurate system model and non-Gaussian observation noise, various fading nonlinear filters were proposed to improve the filtering robustness and convergence, which included the fading UKF filter [ 32 ], robust fading CKF filter [ 33 ], robust H-infinity CKF/KF hybrid filter (RHCHF) [ 34 ], robust adaptive cubature particle filter [ 35 ], robust state-dependent Riccati equation (SDRE) filter [ 36 ], etc. Combined with fuzzy inference system (FIS), the initial alignment schemes based on fuzzy adaptive filters, such as the fuzzy adaptive Kalman filter [ 37 ], fuzzy simplified UKF [ 38 ] and fuzzy strong tracking UKF [ 39 , 40 ], etc., were proposed to improve the performance of SINS initial alignment under large alignment angles and disturbance.…”

Section: Introductionmentioning

confidence: 99%

A Novel Alignment Method for SINS with Large Misalignment Angles Based on EKF2 and AFIS

Zhao

Yan

Yong-yuan

et al. 2020

Sensors

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In order to achieve the fine alignment of strapdown inertial navigation (SINS) under large misalignment angles, a novel filtering alignment method is proposed based on the second-order extended Kalman filter (EKF2) and adaptive fuzzy inference system (AFIS). Firstly, the quaternion is employed to represent the attitude errors of SINS. A second-order nonlinear state equation is made based on the nonlinear velocity error model and attitude error model, and the linear measurement equation is based on the velocity outputs from SINS. Then, the filtering scheme is designed based on EKF2 and AFIS. The error estimation and fine alignment can be achieved by using the proposed filtering scheme. The results of Monte Carlo Simulation show that the errors of pitch, roll and yaw misalignment angles quickly decrease to about 14″, 15″ and 7.62′ respectively in 350 s under the condition of any misalignment angles with pitch error from −40° to 40°, roll error from −40° to 40°, and yaw error from −50° to 50°. Even when the initial misalignment angles are all very large such as (80°, 120°, 170°), the proposed nonlinear alignment method still can converge normally by utilizing the adaptive fuzzy inference system (AFIS) to adjust the covariance matrix Pk/k−1. Finally, the turntable experiment was performed, and the effectiveness and superiority of the proposed method were further verified by compared with other nonlinear methods.

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An improved nonlinear filter based on adaptive fading factor applied in alignment of SINS

Cited by 14 publications

References 17 publications

Online Correction Method for Ratio Methods by Using Geomagnetic Sensors Based on Kalman Filter

Online Correction Method for Ratio Methods by Using Geomagnetic Sensors Based on Kalman Filter

Combination of IMM Algorithm and ASTRWCKF for Maneuvering Target Tracking

A Novel Alignment Method for SINS with Large Misalignment Angles Based on EKF2 and AFIS

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