Accommodating Obstacle Avoidance in the Weapons Allocation Problem for Tactical Air Defence

In the defence domain, weapons allocation is defined to be the reactive assignment of weapon systems to engage or counter identified threats. From a command perspective, this refers to the allocation of friendly and coalition force elements (e.g. fighter aircraft, frigates etc.) to engage or interdict adversaries which are posing threats, not only to themselves, but also to defended areas and high-value assets. In an earlier work, a conceptual rule-based approach to weapons allocation in the air domain was outlined in terms of so-called critical and sorting parameters, which may be used to determine the capability of each friendly airborne interceptor to engage or counter each threat, and to rank the candidate interceptor-threat pairings respectively. An issue of relevance to the evaluation of the (interdependent) parameters of fuel sufficiency, egress safety and time-to-intercept is how to determine the shortest path from a given interceptor to a static or dynamic threat which avoids prohibited areas such as missile engagement zones, neutral and enemy territories and other exclusion zones. However, in general finding the shortest path is a non-trivial exercise and so determining sub optimal paths through the prohibited areas is often necessary. In the current paper, the problem is investigated from both perspectives. In particular, a technique developed for, and applied to, the field of robotics for finding the shortest path from a source to a fixed destination through a flat earth environment littered with obstacles is adapted to solve the shortest path problem for a spherical earth geometry. This is then used as the basis for determining efficient paths from an interceptor to engage or counter a moving threat

[1]  G. Winskel What Is Discrete Mathematics , 2007 .

[2]  W. P. Malcolm On the Character and Complexity of Certain Defensive Resource Allocation Problems , 2004 .

[3]  David M. Mount,et al.  An Output Sensitive Algorithm for Computing Visibility Graphs , 1987, FOCS.

[4]  J. Sack,et al.  Handbook of computational geometry , 2000 .

[5]  A. Benaskeur,et al.  Threat evaluation and weapons allocation in network-centric warfare , 2005, 2005 7th International Conference on Information Fusion.

[6]  Samuel S. Blackman,et al.  Design and Analysis of Modern Tracking Systems , 1999 .

[7]  P.R.E. Cutler,et al.  Description of a rule-based model for the automatic allocation of airborne assets , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

[8]  Samuel D. Conte,et al.  Elementary Numerical Analysis: An Algorithmic Approach , 1975 .

[9]  Michael Townsend Discrete mathematics - applied combinatorics and graph theory , 1987 .

[10]  David M. Mount,et al.  An output sensitive algorithm for computing visibility graphs , 1987, 28th Annual Symposium on Foundations of Computer Science (sfcs 1987).

[11]  F. Frances Yao,et al.  Computational Geometry , 1991, Handbook of Theoretical Computer Science, Volume A: Algorithms and Complexity.

[12]  Zvi Shiller,et al.  Optimal obstacle avoidance based on the Hamilton-Jacobi-Bellman equation , 1994, IEEE Trans. Robotics Autom..

[13]  Mark de Berg,et al.  Computational geometry: algorithms and applications , 1997 .

[14]  Jur P. van den Berg,et al.  The visibility--voronoi complex and its applications , 2005, EuroCG.

[15]  M. G. Oxenham Automatic air target to airlane association , 2000, Proceedings of the Third International Conference on Information Fusion.

[16]  Martin G. Oxenham Enhancing situation awareness for air defence via automated threat analysis , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.