Accelerometer Fault-Tolerant Model-Aided State Estimation for High-Altitude Long-Endurance UAV

Wonkeun Youn, Hyoungsik Choi, Am Cho, Sungyug Kim, Matthew B. Rhudy

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


This article proposes a novel fault-Tolerant dynamic model-Aided navigation filter to cope with accelerometer faults. An algorithm to estimate the three-Axis accelerations of a high-Altitude long-endurance (HALE) unmanned aerial vehicle (UAV) utilizing control input signals and aerodynamic coefficient parameters is newly proposed. To address the fault of the accelerometer, two model-Aided navigation filters that utilize the measured acceleration, denoted as Acc-measure algorithm, and estimated acceleration, denoted as Acc-free algorithm, respectively, are effectively combined under the interacting multiple model (IMM) framework to integrate the optimality of Acc-measure algorithm and robustness of Acc-free algorithm. Flight test results demonstrated that the proposed algorithm yields robust attitude and wind estimation results in the presence of different types of accelerometer faults compared with Acc-measure and Acc-free algorithms while accurately detecting the fault of the accelerometer.

Original languageEnglish (US)
Article number9072188
Pages (from-to)8539-8553
Number of pages15
JournalIEEE Transactions on Instrumentation and Measurement
Issue number10
StatePublished - Oct 2020

All Science Journal Classification (ASJC) codes

  • Instrumentation
  • Electrical and Electronic Engineering


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