Target location is a problem where the application of multiple sensors that are geographically distributed can determine or improve the location estimate of a target. If these sensors are capable of cooperative behaviour then the information from each sensor can be autonomously fused to provide an estimate of the target position. The individual sensors may be quite unsophisticated, yet the observation system that is created through cooperation and adaptive networking of these sensors provides sufficient process gain to achieve target location accuracies similar to those of expensive centralized sensor systems. The accuracy of target location estimates depends heavily on the separation distance between the sensors. Large baseline geometry takes advantage of many seemingly unsophisticated bearing measurements that are organised into a coordinated observation system to locate a target. Team formation is one method to address coordination of distributed sensors, data fusion, sensor resource and energy management, and communication link control based on the concept of cooperating machines1,2,3. We apply an algorithm for agent team formation4 inspired by the self-organising behaviour observed in colonies of ants, to the problem of integrating the sensors of a group of networked mini-Autonomous Air Vehicles (AAVs). The mini-AAVs are tasked to locate targets within a region of interest. The challenge we address is to make the location estimation system adaptive to a dynamic environment and robust to failure. Simulation results are presented which address issues in distributed data fusion, sensor resource and energy management, and communication link control, for a group of mini-AAVs.