Optimal Cooperative Reconnaissance Using Multiple Vehicles

A study on multivehicle trajectory planning for cooperative reconnaissance problems is presented. Specifically, this work develops understanding and insights into how vehicles cooperate in reconnaissance type missions in which target information is maximized. The performance metric used to guide the cooperation study is the amount of information, defined using the Fisher information matrix, that the sensing vehicles gather over their planned trajectory. A receding horizon optimal control formulation is developed and solved for trajectories that yield maximum information. High-risk zones and vehicle/terminal constraints are also are considered. Trends include the following: 1) vehicles with nonsymmetric sensors tend to triangulate as they get close to the target; 2) vehicles tend to move toward stationary targets as quickly as possible; 3) the addition of a third vehicle exhibits at least 50% less performance improvement than the addition of the second vehicle, and even less for nonsymmetric sensors; 4) optimization for multiple vehicles and targets is a strong function of target to target distances and sensor uncertainty symmetry; and 5) short planning horizons are preferable for moving targets.