Integrated Target Allocation and Guidance Strategy using Virtual Targets
| The research investigates a Shoot-Shoot-Look engagement, in which two waves of multiple pursuers are deployed to intercept multiple targets. The first wave is assigned to targets at the beginning of the scenario. The second wave consists of backup pursuers, whose assignments to the actual targets are delayed until the outcomes of the first-wave intercepts become known. Before their final assignments, the backup pursuers move toward virtual targets to allow several subsequent reallocation options. The allocation aims to minimize the final expected number of surviving targets. Each interception attempt is probabilistic and depends on the time of flight and heading change required to reach the target. Discrete allocations, together with the continuous evolution of pursuer and target kinematic states under simple motion models, are naturally modeled as a stochastic Markov Decision Process. A general multi-stage formulation with repeated virtual target reallocations can be solved optimally using Dynamic Programming, but the associated curse of dimensionality leads to a rapidly increasing computational cost. To mitigate this complexity, the problem is reduced to a single decision stage, assuming that first-wave intercepts occur within a narrow time window. The proposed reduction yields an equivalent finite-dimensional nonlinear optimization problem with respect to the initial headings of the backup pursuers. In addition, a simplified sequential Greedy allocation algorithm is developed. Monte Carlo simulations show that it closely approximates the optimal solution for scenarios in which the first-wave pursuers have similar intercept probabilities, whereas, for highly heterogeneous first-wave outcomes, Dynamic Programming yields significantly superior results.
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