Distributed Detection and Data Fusion
Distributed Detection and Data Fusion
Statistical results for system identification based on quantized observations
Automatica (Journal of IFAC)
Hyperplane-based vector quantization for distributed estimation in wireless sensor networks
IEEE Transactions on Information Theory
IEEE Transactions on Signal Processing - Part II
Location Estimation of a Random Signal Source Based on Correlated Sensor Observations
IEEE Transactions on Signal Processing
On identification of FIR systems having quantized output data
Automatica (Journal of IFAC)
A Parametric Copula-Based Framework for Hypothesis Testing Using Heterogeneous Data
IEEE Transactions on Signal Processing
Input design in worst-case system identification with quantized measurements
Automatica (Journal of IFAC)
Identification of ARMA models using intermittent and quantized output observations
Automatica (Journal of IFAC)
| Hi-index | 22.14 |
In this paper, we consider the distributed maximum likelihood estimation (MLE) with dependent quantized data under the assumption that the structure of the joint probability density function (pdf) is known, but it contains unknown deterministic parameters. The parameters may include different vector parameters corresponding to marginal pdfs and parameters that describe the dependence of observations across sensors. Since MLE with a single quantizer is sensitive to the choice of thresholds due to the uncertainty of pdf, we concentrate on MLE with multiple groups of quantizers (which can be determined by the use of prior information or some heuristic approaches) to fend off against the risk of a poor/outlier quantizer. The asymptotic efficiency of the MLE scheme with multiple quantizers is proved under some regularity conditions and the asymptotic variance is derived to be the inverse of a weighted linear combination of Fisher information matrices based on multiple different quantizers which can be used to show the robustness of our approach. As an illustrative example, we consider an estimation problem with a bivariate non-Gaussian pdf that has applications in distributed constant false alarm rate (CFAR) detection systems. Simulations show the robustness of the proposed MLE scheme especially when the number of quantized measurements is small.