Iterative spherical simplex unscented particle filter for CNS/Redshift integrated navigation system

Fu, Kui; Zhao, Guangqiong; Li, Xiajing; Tang, Zhong-Liang; He, Wei

doi:10.1007/s11432-015-0189-9

Cited by 14 publications

(11 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to the redshift principle of a spectrum and the Doppler frequency shift formula, we can obtain the following equation [ 13 ]:

where

denotes the spectral redshift value of the celestial body calculated in the target vehicle;

denotes the velocity vector of the vehicle in the inertial frame (I-frame);

denotes the velocity vector of the celestial body in the I-frame, which can be obtained by querying the celestial ephemeris; c is the velocity of light; and

denotes the radial velocity along the direction from the target vehicle to the observed celestial body.…”

Section: Relationship Between Velocity and Redshift In The East-nomentioning

confidence: 99%

“…In the SRS, velocity can be obtained from the spectral redshift information of celestial spectra. Compared with other navigation systems, the SRS has the advantage of simple navigation principles, easy star selection, and no time delay [ 13 ]. In order to improve the autonomy of the integrated navigation of spacecraft, the SRS is widely used as an auxiliary navigation system to assist in correcting the velocity error of the main navigation system.…”

Section: Introductionmentioning

confidence: 99%

“…In order to improve the autonomy of the integrated navigation of spacecraft, the SRS is widely used as an auxiliary navigation system to assist in correcting the velocity error of the main navigation system. For example, the authors of [ 13 ] used both the CNS and the SRS to correct the divergence caused by model errors of the orbital dynamics equations used in deep space exploration. The authors of [ 14 ] investigated the INS/SRS/GNS integrated navigation system, which used geomagnetic navigation and spectral redshift navigation to correct for the error of the INS.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Robust INS/SRS/CNS Integrated Navigation System with the Chi-Square Test-Based Robust Kalman Filter

Gao

Hong

et al. 2020

Sensors

View full text Add to dashboard Cite

In order to achieve a highly autonomous and reliable navigation system for aerial vehicles that involves the spectral redshift navigation system (SRS), the inertial navigation (INS)/spectral redshift navigation (SRS)/celestial navigation (CNS) integrated system is designed and the spectral-redshift-based velocity measurement equation in the INS/SRS/CNS system is derived. Furthermore, a new chi-square test-based robust Kalman filter (CSTRKF) is also proposed in order to improve the robustness of the INS/SRS/CNS navigation system. In the CSTRKF, the chi-square test (CST) not only detects measurements with outliers and in non-Gaussian distributions, but also estimates the statistical characteristics of measurement noise. Finally, the results of our simulations indicate that the INS/SRS/CNS integrated navigation system with the CSTRKF possesses strong robustness and high reliability.

show abstract

“…According to the redshift principle of a spectrum and the Doppler frequency shift formula, we can obtain the following equation [ 13 ]:

where

denotes the spectral redshift value of the celestial body calculated in the target vehicle;

denotes the velocity vector of the vehicle in the inertial frame (I-frame);

denotes the velocity vector of the celestial body in the I-frame, which can be obtained by querying the celestial ephemeris; c is the velocity of light; and

denotes the radial velocity along the direction from the target vehicle to the observed celestial body.…”

Section: Relationship Between Velocity and Redshift In The East-nomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Robust INS/SRS/CNS Integrated Navigation System with the Chi-Square Test-Based Robust Kalman Filter

Gao

Hong

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Hence, the PF performance is improved, and the particle degeneracy problem can be solved. Fu et al [12] improved the UPF for the celestial navigation system/Redshift (CNS/Redshift) integrated navigation system. The sphere simplex unscented transform and iterated UKF are combined in [12], which can achieve lower computational burden and better performance than UPF.…”

Section: Introductionmentioning

confidence: 99%

“…Fu et al [12] improved the UPF for the celestial navigation system/Redshift (CNS/Redshift) integrated navigation system. The sphere simplex unscented transform and iterated UKF are combined in [12], which can achieve lower computational burden and better performance than UPF. Lee et al [13] proposed the adaptive coarse-to-fine particle filter, which reduced unnecessary calculations by controlling the number of particles required.…”

Section: Introductionmentioning

confidence: 99%

Robust information unscented particle filter based on M‐estimate

Song

2019

IET signal process.

View full text Add to dashboard Cite

Herein, inspired by the unscented particle filter (UPF), an improved particle filter (PF) using an unscented information filter based on M-estimate (UIF-BM) to approximate the importance density function (IDF) is designed. The filtering method is proposed to obtain an accurate state estimation for a non-linear discrete time dynamical system with non-Gaussian system noises and observation noises contaminated by some Gaussian impulsive noises. The PF framework is used as a common method to handle non-Gaussian system noises. In order to obtain a precise IDF, UIF based on M-estimate is used for heavytailed observation noises. Meanwhile, instead of the robust Gaussian filter, the robust IF can avoid the numerical problem that zero weight functions cannot be incorporated into the framework. The simulation results indicate the estimation accuracy and efficiency of the proposed filter. Compared with the UIF-BM, PF, and UPF, the superiority of the proposed filter against the nonideal system and observation noises is obvious.

show abstract

An improved adaptive spherical unscented Kalman filtering method for the accurate state‐of‐charge estimation of lithium‐ion batteries

Wang

Cao

et al. 2022

Circuit Theory & Apps

View full text Add to dashboard Cite

The state of charge (SOC) of lithium-ion batteries is the main parameter of the battery management system. To improve the accuracy of lithium-ion battery SOC estimation, this paper uses a double RC physical characteristic circuit network to model the polarization reaction inside the battery and realizes the full parameter identification of the model based on the double-exponential fitting strategy. Then, using the spherical unscented transform (SUT) to realize the selection of sigma points and the calculation of weight coefficients, at the same time, the adaptive factor is introduced to correct the error covariance matrix in real time and an adaptive spherical unscented Kalman filter (AS-UKF) algorithm. Finally, the algorithm is compared with the unscented Kalman filter (UKF) and adaptive unscented Kalman filter (AUKF) algorithms through simulation. The results show that the average error of the AS-UKF algorithm is reduced by 0.5% and 1.18% under the Hybrid Pulse Power Characterization (HPPC) and the Beijing Bus Dynamic Street Test (BBDST) conditions. The AS-UKF algorithm not only improves the accuracy but also is more stable.

show abstract

Iterative spherical simplex unscented particle filter for CNS/Redshift integrated navigation system

Cited by 14 publications

References 14 publications

A Robust INS/SRS/CNS Integrated Navigation System with the Chi-Square Test-Based Robust Kalman Filter

A Robust INS/SRS/CNS Integrated Navigation System with the Chi-Square Test-Based Robust Kalman Filter

Robust information unscented particle filter based on M‐estimate

An improved adaptive spherical unscented Kalman filtering method for the accurate state‐of‐charge estimation of lithium‐ion batteries

Contact Info

Product

Resources

About