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Sensor fusion and flight path reconstruction of the ACT/FHS rotorcraft

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Abstract

DLR’s active control technology/flying helicopter simulator (ACT/FHS) research rotorcraft supports research in a variety of fields. This paper presents the flight path reconstruction (FPR) of the ACT/FHS for post-flight data processing and its online sensor fusion during flight. Both are fundamental for system identification and flight control research. First, the ACT/FHS rotorcraft, its system architecture and the used sensor instrumentation are described. Then, the implemented unscented and extended Kalman filters are briefly explained and the applied kinematic and measurement models of the FPR are introduced. The wind estimation performance of the FPR is evaluated using simulation and flight test data accordingly. Subsequently, the online sensor fusion is motivated and its behaviour following a simulated differential GPS failure is analysed and explained.

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Abbreviations

CG:

Centre of gravity

EKF, UKF:

Extended, unscented Kalman Filter

FPR:

Flight path reconstruction

(B):

Body-fixed coordinate system

(E):

Earth-fixed coordinate system

\(a_x\), \(a_y\), \(a_z\) :

Translatory accelerations (B)

p, q, r :

Roll, pitch and yaw rates (B)

u, v, w :

Airspeed components (B)

\(u_g\), \(v_g\), \(w_g\) :

Geodetic speed components (B)

V :

True airspeed (B)

\(\Delta V\), \(\Delta V_{\text {NB}}\) :

True airspeed bias (B), i.e. of noseboom

\(W_{\text {N}}\), \(W_{\text {E}}\) :

Wind speed from north and east (E)

|W|, \(\angle W\) :

Wind speed, wind direction (E)

x, y, z :

Aircraft position (E)

\(\varvec{T}_{\text {BE}}\) :

Transformation matrix from (B) to (E)

\(\alpha\), \(\beta\) :

Angle of attack, angle of sideslip (B)

\(\phi\), \(\theta\), \(\psi\) :

Roll, pitch and yaw attitude angles (B)

\(_k\) (subscript):

Discrete time, at kth time step

\(^{-}\) (superscript):

Predicted vector or matrix

a, b, c, k :

UKF sigma point parameters

n :

System order

\(\varvec{f}\), \(\varvec{h}\) :

State and observation function

\(\varvec{F}_{x_k}\), \(\varvec{H}_{x_k}\) :

Jacobian matrix of \(\varvec{f}\) and \(\varvec{h}\) at \(\varvec{x}_k\)

\(\varvec{K}_k\) :

Kalman Filter gain

\(\varvec{P}_k\) :

State covariance matrix

\(\varvec{q}_k\), \(\varvec{Q}_k\) :

Process noise, covariance matrix

\(\varvec{r}_k\), \(\varvec{R}_k\) :

Measurement noise, measurement covariance matrix

\(\varvec{S}_k\) :

Predicted measurement covariance

\(\varvec{u}_k\), \(\varvec{x}_k\), \(\varvec{y}_k\) :

Input, state and output vector

\(\varvec{w}_m\), \(\varvec{w}_c\), \(\varvec{W}_c\) :

UKF weights, UKF weight matrix

\(\varvec{X}_{s,k}\), \(\varvec{X}_{s,k}^{-}\) :

UKF sigma points around \(\varvec{x}_k\) and \(\varvec{x}_k^{-}\)

\(\varvec{X}_{k}^{-}\), \(\varvec{Y}_{k}^{-}\) :

Transformed sigma points using \(\varvec{f}\), \(\varvec{h}\)

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Authors and Affiliations

  1. Deutsches Zentrum für Luft- und Raumfahrt, Institute of Flight Systems, Lilienthalplatz 7, 38108, Braunschweig, Germany

    Johannes Wartmann, Jens Wolfram & Martin Gestwa

Authors
  1. Johannes Wartmann
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  2. Jens Wolfram
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  3. Martin Gestwa
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Corresponding author

Correspondence to Johannes Wartmann.

Additional information

This paper is based on a presentation at the German Aerospace Congress, September 16–18, 2014, Augsburg, Germany.

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Wartmann, J., Wolfram, J. & Gestwa, M. Sensor fusion and flight path reconstruction of the ACT/FHS rotorcraft. CEAS Aeronaut J 6, 529–539 (2015). https://doi.org/10.1007/s13272-015-0162-3

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  • DOI: https://doi.org/10.1007/s13272-015-0162-3

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