Optimal maximum likelihood estimates fusion in distributed network of sensors

A distributed network of sensors leverages its performance by aggregating information gathered by individual sensors through the process of sensor data fusion. Estimating parameters using a centralized scheme entails transporting data from multiple sensors to a centralized fusion center, leading to high network bandwidth consumption. Additionally, fusing raw sensor data from sensors with different sensing modalities may not be feasible. We propose an alternative approach in which each sensor first individually estimates the unknown parameters based solely on its own sensor data. Since sensors may not have a-priori knowledge of the probability distribution of the unknown parameters, each sensor independently computes its individual maximum likelihood estimates. Individual estimates along with their sufficient statistics are then communicated to the fusion center, which treats these estimates as observations to compute the optimum aggregated maximum likelihood estimates by maximizing the new likelihood function of these observations. The proposed technique offers two significant advantages: (i) Since each sensor computes its individual estimates based solely on its own sensed data, it is easily applicable to sensor networks having multi-modal sensors, and (ii) As compared to raw sensor data, communicating estimates and their sufficient statistics to the fusion center requires substantially less network bandwidth. Performance of the aggregated estimates is evaluated through simulations and by computing the Cramer-Rao lower bound.