Decision factors for cooperative multiple warhead UAV target classification and attack with control applications

Autonomous wide area search, classification and attack using Unmanned Combat Air Vehicles (UCAVs) is considered. The wide area search and attack scenario is modelled, capturing the important problem parameters of target density in the battle space, the density of false targets, the seeker and Autonomous Target Recognition (ATR) modules' performance parameters, as well as munition parameters such as search rate, time, and warhead lethality. The analysis in this research is an important stepping stone towards establishing benefits of cooperative search and engagement in a multi-vehicle scenario. This research uses probabilistic analysis to formulate and analytically solve for the probability of success in search and engagement as well as probabilities of other events of interest. Two methods are used to compute these probabilities. The first method utilizes a detailed examination of the sub-events required for the event of interest to occur. The second method utilizes a Markov chain approach. In each method, general expressions are first obtained that are applicable to any assumed a priori distributions of targets and false targets. These expressions are subsequently applied to a multiple warhead munition/UCAV operating in a single target/multiple false target scenario and then several multiple target/multiple false target scenarios. This research shows how the analytically derived results can be applied to all facets of the balanced system design and operation of Wide Area Search Munitions (WASM) including the evaluation of cooperation schemes and rules of engagement. This dissertation also formulates the problem as a control problem and examines the possibility of utilizing this formulation in the real-time estimation of the target and false target distribution parameters.