Robust stability analysis of H∞-SGQKF and its application to transfer alignment

Chen, Hongmei; Cheng, Xianghong; Dai, Chenxi; Liu, Fengli

doi:10.1016/j.sigpro.2015.05.019

Cited by 16 publications

(9 citation statements)

References 39 publications

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“…Furthermore, the slave and master SINS are always positioned at distance from each other, and there will be relative motion between them while the vehicle turns or manoeuvres, this will introduce additional errors into the TA. The TA method based on inertial measurement matching have received extensive attention in recent years 18,[27][28][29][30][31][32][33][34][35][36][37][38][39][40] , which can be depicted in Fig. 1.…”

Section: Transfer Alignment Of Sinsmentioning

confidence: 99%

Rapid Transfer Alignment of SINS with Measurement Packet Dropping based on a Novel Suboptimal Estimator

Dai

Tang³

et al. 2019

Def. Sc. Jl.

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Transfer alignment (TA) is an important step for strapdown inertial navigation system (SINS) starting from a moving base, which utilises the information proposed from the higher accurate and well performed master inertial navigation system. But the information is often delayed or even lost in real application, which will seriously affect the accuracy of TA. This paper models the stochastic measurement packet dropping as an independent identically distributed (IID) Bernoulli random process, and introduces it into the measurement equation of rapid TA, and the influence of measurement packet dropping is analysed. Then, it presents a suboptimal estimator for the estimation of the misalignment in TA considering the random arrival of the measurement packet. Simulation has been done for the performance comparison about the suboptimal estimator, standard Kalman filter and minimum mean squared estimator. The results show that the suboptimal estimator has better performance, which can achieve the best TA accuracy. 7. dai, h.; dai, S.; Cong, y.; Zhao, g. & wu, g. Rapid transfer alignment of laser sins using quaternion based angular measurement.

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Section: Transfer Alignment Of Sinsmentioning

confidence: 99%

Rapid Transfer Alignment of SINS with Measurement Packet Dropping based on a Novel Suboptimal Estimator

Dai

Tang³

et al. 2019

Def. Sc. Jl.

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“…the filter can be designed to achieve the desired accuracy by iterating to arrive at a suboptimal solution [23,24]. It can be easily shown that REKF reverts to EKF with → ∞.…”

Section: Rekf Algorithmmentioning

confidence: 99%

Novel SINS Initial Alignment Method under Large Misalignment Angles and Uncertain Noise Based on Nonlinear Filter

Yang

Zhang

et al. 2017

Mathematical Problems in Engineering

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For the SINS initial alignment problem under large misalignment angles and uncertain noise, two novel nonlinear filters, referred to as transformed unscented quadrature Kalman filter (TUQKF) and robust transformed unscented quadrature Kalman filter (RTUQKF), are proposed in this paper, respectively. The TUQKF sets new deterministic sigma points to address the nonlocal sampling problem and improve the numerical accuracy. The RTUQKF is the combination ofH∞technique and TUQKF. It improves the accuracy and robustness of state estimation. Simulation results indicate that TUQKF performs better than traditional filters when misalignment angles are large. Turntable and vehicle experiments results indicate that, under the condition of uncertain noise, the performances of RTUQKF are better than other filters and more robust. These two methods can effectively further increase precision and convergence speed of SINS initial alignment.

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“…For the system above, we consider a generalised SGQ non-linear recursive formulation, as follows (Jia et al, 2012; Jia and Xin, 2013; Chen et al, 2014; 2015; Heiss and Winschel, 2008). where , and are the predictions of the state, a posteriori estimation of the state, and the measurement, respectively.…”

Section: The Non-linear R-sgqfmentioning

confidence: 99%

“…The optimal H ∞ filter is more robust against model uncertainty; trials to minimise the influence of the worst possible disturbances on the estimation errors have been reported (Grimble and Ahmed, 1990). H ∞ solutions for the non-linear filtering problem have been proposed using three different strategies, which derive filtering algorithms according to various objectives (Piché et al, 2012; Xiong et al, 2011), described as norm-bounded uncertainties (Ishihara et al, 2006), external disturbances (Jia and Xin, 2013; Li and Jia, 2010; Chen et al, 2015; Xiong et al, 2008; Xie et al, 1994), and multiplicative noise (Piché et al, 2012; Xiong et al, 2011). Although the filter algorithms were devised for non-linear systems, they adopt linear filtering strategies for non-linear systems with the linearized approximations of non-linear functions, such as the extended H ∞ filter (Ishihara et al, 2006; Huang et al, 2012; Reif and Unbehauen, 1999; Souto et al, 2009; Seo et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

A Novel Robust Sparse-Grid Quadrature Kalman Filter Design for HCV Transfer Alignment Against Model Parameter Uncertainty

Chen

Liu

2017

J. Navigation

Self Cite

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A novel robust scheme for Transfer Alignment (TA) is proposed for improving the accuracy of the navigation of a Hypersonic Cruise Vehicle (HCV). The main goal is to instil robustness in the safety and accuracy of the attitude determination, despite mode uncertainties. This article focuses on Robust Sparse-Grid Quadrature Filtering (R-SGQF) using two given robust factors for norm-bounded model uncertainties in non-linear systems. Missile dynamic and measurement model uncertainties are established to validate TA technologies. The nominal stability of the R-SGQF is defined by estimating error dynamics. The technique gives sufficient conditions for the R-SGQF in terms of two parameterised Riccati equations. Robust stability is analysed using Lyapunov theory and the accuracy level of the Sparse-Grid Quadrature (SGQ) algorithm. Embedding the SGQ technique into the robust filter structure, R-SGQF is not only of robust stability against uncertainty but also of higher accuracy. The simulation results of the TA algorithm demonstrate that attitude determinations validate the effectiveness of the R-SGQF algorithm.

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Robust stability analysis of H∞-SGQKF and its application to transfer alignment

Cited by 16 publications

References 39 publications

Rapid Transfer Alignment of SINS with Measurement Packet Dropping based on a Novel Suboptimal Estimator

Rapid Transfer Alignment of SINS with Measurement Packet Dropping based on a Novel Suboptimal Estimator

Novel SINS Initial Alignment Method under Large Misalignment Angles and Uncertain Noise Based on Nonlinear Filter

A Novel Robust Sparse-Grid Quadrature Kalman Filter Design for HCV Transfer Alignment Against Model Parameter Uncertainty

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