Distributed Signal Processing for Distributed Sensor Networks

The world is moving towards an environment of pervasive interconnected sensing. This is driven by the falling cost of hardware and significant advances in communications networks. In the defence sector many companies have realized the significance of this network enabled capability and have instigated their own programmes. Ideally the signal processing algorithms for such networks should be: computationally efficient - to minimize power and/or cooling requirements; communications aware - to minimize the costs of communications between sensors;non-hierarchical - so the network is robust to the removal and addition of sensors; globally convergent - so solutions are as accurate as those that would be obtained if each node had direct access to all the information across the network.In this proposal we offer a truly distributed signal processing solution to the distributed sensor problem of source localization. This is in contrast to the recent body of work on distributed adaptive processing algorithms which offer only parallel processing solutions to single input/ single output temporal filltering problems. Distributed sensor networks, by design and intent, provide a spatial processing capability, enabling source location. The distributed signal processing algorithms we seek facilitate this spatial processing capability. While our starting point is framed in terms of parallel processing adaptive filtering techniques, the theoretical framework we develop facilitates extensions into spatial signal processing and source location.