## Contingency Estimation of States for Unmanned Aerial Vehicle using a Spherical Simplex Unscented Filter

Publication: Research - peer-review › Article in proceedings – Annual report year: 2012

### Standard

**Contingency Estimation of States for Unmanned Aerial Vehicle using a Spherical Simplex Unscented Filter.** / Hahn, Tobias ; Hansen, Søren; Blanke, Mogens.

Publication: Research - peer-review › Article in proceedings – Annual report year: 2012

### Harvard

*System Identification.*vol. 16, International Federation of Automatic Control, pp. 1797-1802. IFAC Proceedings Volumes (IFAC-PapersOnline) , DOI: 10.3182/20120711-3-BE-2027.00245

### APA

*System Identification.*(Vol. 16, pp. 1797-1802). International Federation of Automatic Control. (IFAC Proceedings Volumes (IFAC-PapersOnline) ). DOI: 10.3182/20120711-3-BE-2027.00245

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### MLA

*System Identification.*International Federation of Automatic Control. 2012. 1797-1802. (IFAC Proceedings Volumes (IFAC-PapersOnline) ). Available: 10.3182/20120711-3-BE-2027.00245

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### Bibtex

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### RIS

TY - GEN

T1 - Contingency Estimation of States for Unmanned Aerial Vehicle using a Spherical Simplex Unscented Filter

AU - Hahn,Tobias

AU - Hansen,Søren

AU - Blanke,Mogens

PY - 2012

Y1 - 2012

N2 - Aiming at survival from contingency situations for unmanned aerial vehicles, a square root spherical simplex unscented Kalman filter is applied for state and parameter estimation and a rough model is used for state prediction when essential measurements are lost. Processing real flight data, received by telemetry at quite low sampling rate, the paper shows that filter performance of reasonable quality can be achieved despite the low sampling rate and the result<br/>is a low order model that can be useful during contingency operation. It is shown that the filter-estimator approach can cope with the low rate measurements requiring very little system knowledge and very limited tuning efforts. A generic aircraft model is utilised as process model where the non dimensional coefficients are identified online with joint estimation of states. Numerical stability is guaranteed by mathematically efficient square root implementation of the<br/>filter algorithm. A case of loss of GPS signal demonstrates the use of the state estimates to obtain return of the UAV to close to it’s home base where safe recovery is possible.

AB - Aiming at survival from contingency situations for unmanned aerial vehicles, a square root spherical simplex unscented Kalman filter is applied for state and parameter estimation and a rough model is used for state prediction when essential measurements are lost. Processing real flight data, received by telemetry at quite low sampling rate, the paper shows that filter performance of reasonable quality can be achieved despite the low sampling rate and the result<br/>is a low order model that can be useful during contingency operation. It is shown that the filter-estimator approach can cope with the low rate measurements requiring very little system knowledge and very limited tuning efforts. A generic aircraft model is utilised as process model where the non dimensional coefficients are identified online with joint estimation of states. Numerical stability is guaranteed by mathematically efficient square root implementation of the<br/>filter algorithm. A case of loss of GPS signal demonstrates the use of the state estimates to obtain return of the UAV to close to it’s home base where safe recovery is possible.

KW - Data handling

KW - Kalman filters

KW - Parameter estimation

KW - Spheres

KW - State estimation

KW - Unmanned aerial vehicles (UAV)

KW - Estimation

KW - Spherical simplex

KW - Unmanned aerial vehicle

KW - Contingency operations

KW - Filter algorithm

KW - Filter performance

KW - Flight data

KW - GPS signals

U2 - 10.3182/20120711-3-BE-2027.00245

DO - 10.3182/20120711-3-BE-2027.00245

M3 - Article in proceedings

SN - 978-3-902823-06-9

VL - 16

SP - 1797

EP - 1802

BT - System Identification

PB - International Federation of Automatic Control

ER -