This paper describes a fully distributed approach to target tracking that we have implemented and tested in a military setting. The approach is built upon local sharing of robust statistics that summarize local events. Local collaboration extracts detection information such as time, velocity, position, heading and target type from the summary statistics. The groups of nodes used for local collaboration are determined dynamically at run time. Local collaboration information is compared with a list of tracks in the immediate vicinity. Associating detections to tracks is currently done using a variation of the nearest-neighbor metric. This paper extends our previous work by using mobile code daemons to support multiple hypothesis tracking methods. This is done in a resourceconstrained environment by using the network to swap sohare modules dynamically. Results from field tests of the approach are provided. This includes a dependability analysis of the distributed approach versus centralized systems.