Robust adaptive filter allowing systematic model errors for transfer alignment

Gao, Zhaohui; Mu, Dejun; Gao, Shesheng; Zhong, Yongmin; Gu, Chengfan

doi:10.1016/j.ast.2016.10.002

Cited by 17 publications

(18 citation statements)

References 11 publications

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“…During the period of transfer alignment, when the H∞ filter with delay compensation diverges, the error covariance matrix is unbounded and the real estimation error is bigger than the theoretical estimation error [19]. Therefore, we can evaluate the performance of H∞ filter with delay compensation by using filter innovation

η_{k - τ}

.…”

Section: Adaptive H∞ Filtering Methods With Delay Compensationmentioning

confidence: 99%

“…When the base is moving, there exist interference factors such as external noises or motion disturbances, which can decrease the accuracy of a S-SINS’s transfer alignment [19,20]. …”

Section: Introductionmentioning

confidence: 99%

“…The traditional Kalman filter requires an accurate system mathematical model and detailed information on the statistical properties of the signal [19,20,21,22,23], but in practical applications, the statistical characteristics of signals are not accurate, and the system model itself also has some indetermination, so there is uncertainty in the system [20,21,22,24,25,26,27]. When the uncertainty reaches a certain extent, the estimation accuracy of the Kalman filter will be reduced, and Kalman filter may even have divergence problems in serious cases [20].…”

Section: Introductionmentioning

confidence: 99%

“…In [20] an H∞ optimization method was used to conduct transfer alignment, which estimated the misalignment angle and improved the standard Kalman filter performance when the system noises were not fully known. Gao et al [19] devised a robust adaptive filter, which lowered the requirement for precise kinematic and observation models for transfer alignment compared with a traditional Kalman filter. By adding robust elements in the filtering process, the influence caused by systematic model errors and the disturbances of dynamic external environment can be effectively reduced [20,22,23,24,25,26,27,28,29,30,31,32].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Adaptive H∞ Filtering Method with Delay Compensation for the Transfer Alignment of Strapdown Inertial Navigation Systems

Lyu

Cheng

2017

Sensors

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Transfer alignment is always a key technology in a strapdown inertial navigation system (SINS) because of its rapidity and accuracy. In this paper a transfer alignment model is established, which contains the SINS error model and the measurement model. The time delay in the process of transfer alignment is analyzed, and an H∞ filtering method with delay compensation is presented. Then the H∞ filtering theory and the robust mechanism of H∞ filter are deduced and analyzed in detail. In order to improve the transfer alignment accuracy in SINS with time delay, an adaptive H∞ filtering method with delay compensation is proposed. Since the robustness factor plays an important role in the filtering process and has effect on the filtering accuracy, the adaptive H∞ filter with delay compensation can adjust the value of robustness factor adaptively according to the dynamic external environment. The vehicle transfer alignment experiment indicates that by using the adaptive H∞ filtering method with delay compensation, the transfer alignment accuracy and the pure inertial navigation accuracy can be dramatically improved, which demonstrates the superiority of the proposed filtering method.

show abstract

η_{k - τ}

.…”

Section: Adaptive H∞ Filtering Methods With Delay Compensationmentioning

confidence: 99%

“…When the base is moving, there exist interference factors such as external noises or motion disturbances, which can decrease the accuracy of a S-SINS’s transfer alignment [19,20]. …”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Adaptive H∞ Filtering Method with Delay Compensation for the Transfer Alignment of Strapdown Inertial Navigation Systems

Lyu

Cheng

2017

Sensors

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show abstract

“…] to be the system state vector, Equation 1 and Equation 3 can be grouped together to form (12 × 12dimensional) state transition matrix as described as Equation 4. The measurement vector involves 3 × 1 velocity matched measurements only, resulting in the 3 × 12 measurement sensitivity matrix of Equation 5.…”

Section: Basic Ta Error Propagation Equationsmentioning

confidence: 99%

Tuning of evolutionary particle filtering approach for estimation of trajectory deviation

Mukherjee

Chattaraj

Chakraborty³

2019

Asian Journal of Control

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An autonomous vehicle launched from moving platform is subjected to flexure-like disturbance and needs to stabilize itself before embarking on the target tracking phase. Control and guidance remains inactive during stabilization phase. Hence, estimating the deviation from the intended trajectory is important in the absence of control and guidance commands for trajectory correction. This estimation problem is here mapped to the misalignment estimation problem, with the intended trajectory data resembling mother data and the followed trajectory data resembling daughter data. The main requirement is fast convergence for early commencement of target tracking phase. Kalman filter and its variants fail to converge due to the inability to model time varying nonlinearities and flexure in the system model. Conventional particle filter converges, but computation time is high. This work explores evolutionary particle filter variants that may provide faster convergence through adaptive tuning of the range from where particle support points are selected. Such heuristics is based on the observed residual trend. Monte Carlo simulation results underline the importance of designing estimator based on adaptive tuned evolutionary particle filter algorithm.

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Adaptive Random Weighted H∞ Estimation for System Noise Statistics

Gao,

Zhong,

Zong

et al. 2024

Adaptive Control & Signal

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The Kalman filter is an important technique for system state estimation. It requires the exact knowledge of system noise statistics to achieve optimal state estimation. However, in practice, this knowledge is often unknown or inaccurate due to uncertainties and disturbances involved in the dynamic environment, leading to degraded or even divergent Kalman filtering solutions. This paper proposes a novel method of H∞ filtering‐based on adaptive random weighted estimation to address this issue. It combines the H∞ filter with random weighted concept to estimate system noise statistics. Random weighting theories are established based on the state estimate and state error covariance of the H∞ filter to estimate both process noise statistics and measurement noise statistics. Subsequently, the estimated system noise statistics are fed back into the Kalman filtering process for system state estimation. Simulation and experimental results show that the proposed method can effectively estimate system noise statistics, leading to improved accuracy for system state estimation.

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Robust adaptive filter allowing systematic model errors for transfer alignment

Cited by 17 publications

References 11 publications

A Novel Adaptive H∞ Filtering Method with Delay Compensation for the Transfer Alignment of Strapdown Inertial Navigation Systems

A Novel Adaptive H∞ Filtering Method with Delay Compensation for the Transfer Alignment of Strapdown Inertial Navigation Systems

Tuning of evolutionary particle filtering approach for estimation of trajectory deviation

Adaptive Random Weighted H∞ Estimation for System Noise Statistics

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