Author:

Abstract:

The first part of this thesis focuses on a concrete application, investigating the improvements that can be achieved in terms of noise reduction performance and spatial cue preservation by adding a single remote sensor (microphone) to a binaural noise reduction system, e.g. binaural hearing aids or cochlear implants. The increase in the noise reduction performance by the addition of a remote sensor signal serves as the motivation for later chapters where many nodes consisting of multiple sensors are available. In the case where multiple remote microphones or nodes are available, a method for choosing which (sub)set of signals or nodes should be included in the estimation for maximum benefit is also discussed. The utility is introduced as a way to limit the effect of node removal and can be used to aid in node addition. While first introduced in a centralized scenario, the utility is extended to that of a distributed scenario. However, due to the distributed nature of the signal estimation, the utility is shown to represent a bound as opposed to an exact quantity in the centralized scenario. The second part of this thesis introduces linear compression and fusion rules of the distributed adaptive node-specific signal estimation (DANSE) algorithm for use in heterogeneous and mixed-topology wireless sensor networks (WSN)s. These mixed-topology WSNs are divided into a set of smaller substructures that are based on either clique or cluster formation. It is shown, that the nodes are able to converge to the same optimal solution as if they were to receive all of the uncompressed signals from every other node. Supporting techniques are also introduced relating to different broadcast strategies and topology formation. In the third part of this thesis, each node implements a novel method to linearly compress its sensor signals in order to transmit to the other nodes in the WSN. This leads to the introduction of the topology-independent DANSE (TI-DANSE) algorithm. While the TI-DANSE algorithm is first introduced in a fully connected topology, the convergence properties are shown to be applicable in any topology, as long as the nodes have access to a network wide summed signal. An attractive attribute of the TI-DANSE algorithm is that since it relies on a network wide summed signal, it is less sensitive to link failures and also becomes applicable in WSNs with dynamic topologies. A method to compute this network wide summed signal is proposed that relies on a maximum of two transmissions per node.nbsp;