An Evolutionary Approach for Fusion of Active and Passive Sonar Contact Information

The core problem for networked systems for underwater surveillance revolves around the requirement to blend information from different sensors and platforms into a common operating picture. Data fusion of disparate data types can be problematic given the variety of potential surveillance systems in the network. Active and passive sonar in particular provide different data types with different accuracies. The acoustic environment is highly stochastic and errors are prevalent that can be promulgated throughout the system. An evolutionary algorithm approach for fusing active and passive sonar contact information is used to explore the issue of robustness when large contact errors prevail from multiple platforms. The algorithm may have the ability to recover from erroneous input at the expense of lower track accuracy. This may have a significant utility as an operator aid for construction of the global picture of fused active and passive data. A multitude of erroneous sources can still result in higher overall track errors and further research is required; however, the evolutionary approach may still provide significant benefit