In this paper, we consider distributed estimation where the measurement at each of the distributed sensor nodes is quantized before being transmitted to a fusion node which produces an estimate of the parameter of interest. Since each quantized measurement can be linked to a region where the parameter is found, aggregating the information obtained from multiple nodes corresponds to generating intersections between the regions. Thus, we develop estimation algorithms that seek to find the intersection region with the maximum likelihood rather than the parameter itself. Specifically, we propose two practical techniques that facilitate fast search with significantly reduced complexity and apply the proposed techniques to a system where an acoustic amplitude sensor model is employed at each node for source localization. Our simulation results show that our proposed algorithms achieve good performance with reasonable complexity as compared with the minimum mean squared error (MMSE) and the maximum likelihood (ML) estimators.

We consider the problem of optimizing the quantizer design for distributed estimation systems where all nodes located at different sites collect measurements and transmit quantized data to a fusion node, which then produces an estimate of the parameter of interest. For this problem, the goal is to minimize the amount of information that the nodes have to transmit in order to attain a certain application accuracy. We propose an iterative quantizer design algorithm that seeks to find a non-regular mapping between quantization partitions and their codewords so as to minimize global distortion such as the estimation error. We apply the proposed algorithm to a system where an acoustic amplitude sensor model is employed at each node for source localization. Our experiments demonstrate that a significant performance gain can be achieved by our technique as compared with standard typical designs and even with distributed novel designs recently published.

We consider the problem of finding the best subset of sensors in wireless sensor networks where linear Bayesian parameter estimation is conducted from the selected measurements corrupted by correlated noise. We aim to directly minimize the estimation error which is manipulated by using the QR and LU factorizations. We derive an analytic result which expedites the sensor selection in a greedy manner. We also provide the complexity of the proposed algorithm in comparison with previous selection methods. We evaluate the performance through numerical experiments using random measurements under correlated noise and demonstrate a competitive estimation accuracy of the proposed algorithm with a reasonable increase in complexity as compared with the previous selection methods.