A Theoretical Foundation for Cooperative Search, Classification, and Target Attack

Abstract

Wide area search and attack using Unmanned Air Vehicles or autonomous munitions is considered. In order to build towards a multi-vehicle cooperative behavior scheme, task benefits for search and engagement need to be established. This chapter uses applied probability theory to formulate and solve for the probability of success in search and engagement. Average longevity for the munition and targets are also available using this formulation. A variety of multiple target/multiple false target scenarios are considered, with varying assumptions on the probability distributions of the target and false target vehicles. Area search through target attack is modelled, and the potential benefits accruing from cooperative target classification and cooperative target engagement are addressed. A general approach for defining task benefits for cooperative behavior algorithms is presented, and methods for implementation are discussed. While the discussion in this chapter is limited to a single warhead munition or Unmanned Air Vehicles, the results can be extended to include the multi-warhead case.

The views expressed in this article are those of the authors and do not reflect the official policy of the U.S. Air Force, Department of Defense, or the U.S. Government.

This research was supported in part by a grant from DARPA on the MICA-SHARED program with ohio State University.