Evaluating 3D local descriptors for future LIDAR missiles with automatic target recognition capabilities

ABSTRACT

Future light detection and ranging seeker missiles incorporating 3D automatic target recognition (ATR) capabilities can improve the missile’s effectiveness in complex battlefield environments. Considering the progress of local 3D descriptors in the computer vision domain, this paper evaluates a number of these on highly credible simulated air-to-ground missile engagement scenarios. The latter take into account numerous parameters that have not been investigated yet by the literature including variable missile – target range, 6-degrees-of-freedom missile motion and atmospheric disturbances. Additionally, the evaluation process utilizes our suggested 3D ATR architecture that compared to current pipelines involves more post-processing layers aiming at further enhancing 3D ATR performance. Our trials reveal that computer vision algorithms are appealing for missile-oriented 3D ATR.

KEYWORDS:

• 3D automatic target recognition
• light detection and ranging
• missile engagement scenarios

Acknowledgements

The authors would like to thank MBDA UK for providing the evaluation scenarios.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Odysseas Kechagias-Stamatis received the M.Sc. degree in guided weapon systems from Cranfield University, U.K., in 2011. Since 2014, he has been working towards the Ph.D. degree in 3D ATR for time-critical missile system applications. His research interests include 2D and 3D object recognition, computer vision, robotics and navigation systems.

Nabil Aouf is a professor with the Centre of Electronic Warfare, Cranfield University. He has written more than 100 papers in his domains of interest. His research interests are aerospace and defense systems, information fusion and vision systems, guidance and navigation, tracking and control and autonomy of systems. He is an associate editor of the International Journal of Computational Intelligence in Control.

Additional information

Funding

This work was supported by MBDA UK: [Grant Number HP29012014].