Model-Aided State Estimation of HALE UAV with Synthetic AOA/SSA for Analytical Redundancy

Wonkeun Youn, Hyoung Sik Choi, Hyeok Ryu, Sungyug Kim, Matthew B. Rhudy

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


This paper proposes a novel dynamic model-aided navigation filter to estimate the safety-critical states of a high-altitude long-endurance (HALE) UAV without measurement of angle of attack (AOA) and sideslip angle (SSA). The major contribution of the proposed algorithm is that the synthetic AOA and SSA measurements are newly formulated for analytical redundancy. In the proposed filter, aerodynamic coefficients and control signals are utilized along with inertial measurement unit (IMU), Global Positioning System (GPS), and pitot tube measurements to estimate the navigation states as well as the steady and turbulent effects of 3D wind using random walk (RW) and Dryden wind models, respectively. Flight test results of a HALE UAV demonstrated that the proposed algorithm yields accurate estimated airspeed, AOA, SSA, attitude, angular rates, and 3D wind states, demonstrating its effectiveness for analytical redundancy.

Original languageEnglish (US)
Article number9037278
Pages (from-to)7929-7940
Number of pages12
JournalIEEE Sensors Journal
Issue number14
StatePublished - Jul 15 2020

All Science Journal Classification (ASJC) codes

  • Instrumentation
  • Electrical and Electronic Engineering


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