An Improved Adaptive Compensation H∞ Filtering Method for the SINS’ Transfer Alignment Under a Complex Dynamic Environment

Abstract

Transfer alignment on a moving base under a complex dynamic environment is one of the toughest challenges in a strapdown inertial navigation system (SINS). With the aim of improving rapidity and accuracy, velocity plus attitude matching is applied in the transfer alignment model. Meanwhile, the error compensation model is established to calibrate and compensate the errors of inertial sensors online. To suppress the filtering divergence during the process of transfer alignment, this paper proposes an improved adaptive compensation H∞ filtering method. The cause of filtering divergence has been analyzed carefully and the corresponding adjustment and optimization have been made in the proposed adaptive compensation H∞ filter. In order to balance accuracy and robustness of the transfer alignment system, the robustness factor of the adaptive compensation H∞ filter can be dynamically adjusted according to the complex external environment. The aerial transfer alignment experiments illustrate that the adaptive compensation H∞ filter can effectively improve the transfer alignment accuracy and the pure inertial navigation accuracy under a complex dynamic environment, which verifies the advantage of the proposed method.

Paper-based microfluidic devices based on 3D network polymer hydrogel for the determination of glucose in human whole blood

“…The analytical performance of the analytical device was also compared with those of other reported luminescent glucose sensing devices using gel capping (Table 1). [27][28][29][30][31][32][33][34] As shown in Table 1, the proposed method provides detection sensitivity for glucose comparable with those of some reported methods and could meet the requirement for glucose detection in clinical glucose monitoring and the food industry (mM levels).…”

Adaptive UT-H∞ Filter for SINS’ Transfer Alignment Under Uncertain Disturbances

“…However, in each application, the value of the robustness factor ξ in the UT-H∞ filter has to be artificially chosen by experience, and once selected, the value of ξ is a constant [19], [27], [31]. This will require significant time in determining the value of ξ before using the UT-H∞ filter, and the selected value of ξ is not guaranteed to be optimal in various situations [7], [30]. Moreover, there are always external disturbances in a real system, and the disturbances are time-varying and uncertain [17], [18].…”

“…Compared with the other two alignment modes, transfer alignment has two significant advantages. One is that transfer alignment is more rapid, so it can be completed in a shorter time [7]. The other is that self-alignment and combination alignment require high sensor accuracy [3], [4], [6]; when the SINS has low sensor The associate editor coordinating the review of this manuscript and approving it for publication was Halil Ersin Soken .…”

“…The error propagation model and filtering algorithm are two important aspects of the transfer alignment of the SINS [9]. The error sources of the SINS include inertial measurement errors, initial condition errors, external disturbances, environmental model errors and others [1], [7]. The error propagation model of SINS is described by a group of nonlinear differential equations [9], [10].…”

“…In this paper, a novel adaptive UT-H∞ filter is designed for SINS' transfer alignment under uncertain disturbances. Different from the linear transfer alignment method in [7], this paper aims to solve the nonlinear transfer alignment problem, and a nonlinear filtering method for transfer alignment is proposed. The adaptive UT-H∞ filter combines the UKF technology and H∞ filter to increase the robustness of the nonlinear system.…”