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.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6359554PMC
http://dx.doi.org/10.3390/s19020401DOI Listing

Publication Analysis

Top Keywords

transfer alignment
28
adaptive compensation
20
compensation h∞
20
complex dynamic
12
dynamic environment
12
h∞ filter
12
improved adaptive
8
h∞ filtering
8
filtering method
8
inertial navigation
8

Similar Publications

Enhancing Activation Energy Predictions under Data Constraints Using Graph Neural Networks.

J Chem Inf Model

January 2025

Department of Chemical Engineering, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 10617, Taiwan.

Accurately predicting activation energies is crucial for understanding chemical reactions and modeling complex reaction systems. However, the high computational cost of quantum chemistry methods often limits the feasibility of large-scale studies, leading to a scarcity of high-quality activation energy data. In this work, we explore and compare three innovative approaches (transfer learning, delta learning, and feature engineering) to enhance the accuracy of activation energy predictions using graph neural networks, specifically focusing on methods that incorporate low-cost, low-level computational data.

View Article and Find Full Text PDF

This study investigated silicone composites with distributed boron nitride platelets and carbon microfibers that are oriented electrically. The process involved homogenizing and dispersing nano/microparticles in the liquid polymer, aligning the particles with DC and AC electric fields, and curing the composite with IR radiation to trap particles within chains. This innovative concept utilized two fields to align particles, improving the even distribution of carbon microfibers among BN in the chains.

View Article and Find Full Text PDF

Field implementations of fully underground sensor networks face many practical challenges that have limited their overall adoption. Power management is a commonly cited issue, as operators are required to either repeatedly excavate batteries for recharging or develop complex underground power infrastructures. Prior works have proposed wireless inductive power transfer (IPT) as a potential solution to these power management issues, but misalignment is a persistent issue in IPT systems, particularly in applications involving moving vehicles or obscured (e.

View Article and Find Full Text PDF

With rising demand for wood products and reduced wood harvesting due to the European Green Deal, alternative lignocellulosic materials for insulation are necessary. In this work, we manufactured reference particleboard from industrial particles and fifteen different board variants from alternative lignocellulosic plants material, i.e.

View Article and Find Full Text PDF

Mechanical force-induced interlayer sliding in interfacial ferroelectrics.

Nat Commun

January 2025

Key Laboratory of Polar Materials and Devices (Ministry of Education), Shanghai Center of Brain-Inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai, 200241, China.

Moiré superlattices in two-dimensional stacks have attracted worldwide interest due to their unique electronic properties. A typical example is the moiré ferroelectricity, where adjacent moirés exhibit opposite spontaneous polarization that can be switched through interlayer sliding. However, in contrast to ideal regular ferroelectric moiré domains (equilateral triangles) built in most theoretical models, the unavoidable irregular moiré supercells (non-equilateral triangles) induced by external strain fields during the transfer process have been given less attention.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!