Skip to main content
SpringerLink
Log in

Sky Region Obstacle Detection and Tracking for Vision-Based UAS Sense and Avoid

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

A customized detection and tracking algorithm for vision-based non cooperative UAS sense and avoid aimed at obstacles approaching from above the horizon is presented in this paper. The proposed approach comprises two main steps. Specifically, the first processing step is relevant to obstacle detection and tentative tracking for track confirmation and is based on top-hat and bottom-hat morphological filtering, local image analysis for a limited set of regions of interest, and multi-frame processing in stabilized coordinates. Once firm tracking is achieved, template matching and state estimation based on Kalman filtering are used to track the intruder aircraft and estimate its angular position and velocity. An extensive experimental analysis is presented which is based on a large set of flight data gathered in realistic near collision scenarios, in different operating conditions in terms of weather and illumination, and adopting different navigation units onboard the ownship. In particular, the focus is set on flight segments at a range between 3 km and 1.3 km, since the major interest is in understanding algorithm potential for relatively large time to collision. System performance is evaluated in terms of declaration range, probability of correct declaration, average number of false positives, tracking accuracy (angles and angular rates in a stabilized North-East-Down reference frame) and robustness with respect to track loss phenomena. Promising results are achieved regarding the trade-off between declaration range and false alarm probability, while the onboard navigation unit is found to heavily impact tracking accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Federal Aviation Administration: Integration of civil Unmanned Aircraft Systems (UAS) in the National Airspace System (NAS) roadmap, First Edition (2013)

  2. European RPAS Steering Group: Roadmap for the integration of civil remotely-piloted aircraft systems into the European Aviation System (2013)

  3. Foerster, K., Mullins, M., Kaabouch, N., Semke, W.: Flight testing of a right-of-way compliant ADS-B-based miniature sense and avoid system. In: AIAA Infotech@Aerospace Conference 2012, Garden Grove, CA (2012)

  4. Melega, M., Lazarus, S., Savvaris, A., Tsourdos, A.: Multiple threats sense and avoid algorithm for static and dynamic obstacles. J. Intell. Robot. Syst. 77(1), 215–228 (2015)

    Article  Google Scholar 

  5. Moses, A., Rutherford, M.J., Kontitsis, K., Valavanis, K.P.: UAV-borne X-band radar for collision avoidance. Robotica 32, 97–114 (2014)

    Article  Google Scholar 

  6. Mackie, J., Spencer, J., Warnick, K.F.: Compact FMCW radar for a UAS Sense and Avoid system. In: 2014 IEEE Antennas and Propagation Society International Symposium (APSURSI), p. 989, 990 (2014)

  7. Utt, J., McCalmont, J., Deschenes, M.: Development of a sense and avoid system. In: AIAA Infotech@Aerospace Conference 2005, Arlington, VA, pp. 1–10 (2005)

  8. Lai, J., Ford, J.J., Mejias, L., O’Shea, O.: Characterization of sky-region morphological-temporal airborne collision detection. J. Field Rob. 30(2), 171–193 (2013)

    Article  Google Scholar 

  9. Dey, D., Geyer, C.M., Singh, S., Digioia, M.: A cascaded method to detect aircraft in video imagery. Int. J. Robot. Res. 30(12), 1527–1540 (2011)

    Article  Google Scholar 

  10. Vanek, B., Péni, T., Zarándy, A., Bokor, J., Zsedrovits, T., Roska, T.: Performance Characteristics of a Complete Vision Only Sense and Avoid System. In: AIAA Guidance, Navigation, and Control Conference 2012, Minneapolis, MN, 13 - 16, AIAA paper 2012-4703 (2012)

  11. Fasano, G., Accardo, D., Moccia, A., Carbone, C., Ciniglio, U., Corraro, F., Luongo, S.: Multi-sensor-based fully autonomous non-cooperative collision avoidance system for unmanned air vehicles. AIAA J. Aerosp. Comput. Inf. Commun. 5, 338–360 (October 2008)

    Article  Google Scholar 

  12. Chen, R.H., Gevorkian, A., Fung, A., Chen, W.Z.: Multi-sensor data integration for autonomous sense and avoid (2011)

  13. Accardo, D., Fasano, G., Forlenza, L., Moccia, A., Rispoli, A.: Flight test of a radar-based tracking system for UAS sense and avoid. IEEE Trans. Aerosp. Electron. Syst. 49(2), 1139–1160 (2013)

    Article  Google Scholar 

  14. Fasano, G., Accardo, D., Tirri, A.E., Moccia, A., De Lellis, E.: Radar/electro-optical data fusion for non-cooperative sense and avoid. Aerosp. Sci. Technol. 46, 436–450 (2015)

    Article  Google Scholar 

  15. Fasano, G., Accardo, D., Moccia, A., Luongo, S., Di Vito, V.: In-flight performance analysis of a non-cooperative radar-based sense and avoid system. In: Proc IMechE Part G: J Aerospace Engineering, published online before print October 6 (2015), doi:10.1177/0954410015607892

  16. Nussberger, A., Grabner, H., Van Gool, L.: Aerial object tracking from an airborne platform. In: International Conference on Unmanned Aircraft Systems (ICUAS) 2014, Orlando, FL, USA (2014)

  17. Fu, C., Carrio, A., Olivares-Mendez, M.A., Suarez-Fernandez, R., Campoy, P.: Robust real-time vision-based aircraft tracking from unmanned aerial vehicles. In: 2014 IEEE International Conference on Robotics & Automation (ICRA), Hong Kong, China (2014)

  18. Tulpan, D., Belacel, N., Famili, F., Ellis, K.: Experimental evaluation of four feature detection methods for close range and distant airborne targets for unmanned aircraft systems applications. In: International Conference on Unmanned Aircraft Systems (ICUAS) 2014, Orlando, FL, USA (2014)

  19. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, Third Edition, Prentice Hall (Pearson International Edition)

  20. Bai, X., Zhou, F., Xie, Y., Jin, T.: Enhanced detectability of point target using adaptive morphological clutter elimination by importing the properties of the target region. Signal Process. 89(10), 1973–1989 (2009)

    Article  MATH  Google Scholar 

  21. Fasano, G., Accardo, D., Moccia, A., Rispoli, A.: An innovative procedure for calibration of strapdown electro-optical sensors onboard unmanned air vehicles. Sensors 10(1), 639–654 (2010)

    Article  Google Scholar 

  22. Collinson, R.P.G.: Introduction to Avionics, vol. 11 of Microwave Technology Series, Chapman&Hall (1996)

  23. Blackman, S.S., Popoli, R.F.: Design and Analysis of Modern Tracking Systems, pp. 360–372. Artech House, Norwood (1999)

    MATH  Google Scholar 

  24. Shakernia, O., Chen, W.-Z., Raska, V.M.: Passive Ranging for UAV Sense and Avoid Applications. In: AIAA Infotech@Aerospace 2005, 26-29, paper AIAA 2005-7179 (2005)

  25. Stallard, D.V: Angle-Only Tracking Filter in Modified Spherical Coordinates. J. Guid. Control. Dyn. 14N(3), 694–696 (1991)

    Article  Google Scholar 

  26. Gelb, A.: Applied Optimal Estimation, pp. 72–75. MIT Press, Cambridge (1974)

    Google Scholar 

  27. Carrio, A., Fu, C., Puerta, J.P., Campoy, P.: A Ground-Truth Video Dataset for the Development and Evaluation of Vision-based Sense-and-Avoid Systems. In: International Conference on Unmanned Aircraft Systems (ICUAS) 2014, Orlando, FL, USA (2014)

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering - Aerospace Engineering Division, University of Naples Federico II, P.le Tecchio 80, 80125, Napoli, Italy

    Giancarmine Fasano, Domenico Accardo, Anna Elena Tirri & Antonio Moccia

  2. On-Board Systems And ATM Information Technologies Department, Italian Aerospace Research Center (CIRA), Via Maiorise, 81043, Capua, CE, Italy

    Ettore De Lellis

Authors
  1. Giancarmine Fasano
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Domenico Accardo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anna Elena Tirri
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Antonio Moccia
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ettore De Lellis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Giancarmine Fasano.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fasano, G., Accardo, D., Tirri, A.E. et al. Sky Region Obstacle Detection and Tracking for Vision-Based UAS Sense and Avoid. J Intell Robot Syst 84, 121–144 (2016). https://doi.org/10.1007/s10846-015-0285-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-015-0285-0

Keywords

Search

Navigation