Scalable distributed data fusion

Measurement and track fusion in decentralised sensor network architectures is investigated. The investigation employs FLAMES/sup TM/, an advanced military scenario generator. This was specifically customised for distributed data fusion experiments and involves a model of the delays in a realistic communication system. Here the delays were used to modify communication bandwidth and evaluate how this affected the performance of the fusion architectures/algorithms. Under certain scenario conditions, it was found that the decentralised measurement fusion system was severely affected by reduced bandwidth. This is because it does not scale: each node loads its communication buffer with every measurement and consequently some measurements are never transmitted. The decentralised track fusion system is a better performer because it does scale: measurements are fused into tracks prior to transmission and thereby more effective use of bandwidth is made. Moreover, it was found that a partially connected decentralised track fusion system achieved almost optimal fused track performance.