Signal reconstruction for multi-source variable-rate samples with autocorrelated errors in variables
Aggregating data from multiple sensors has become a critical requirement in cyberphysical systems (CPS) to increase the effective sampling rate for signal reconstruction. Depending on the application, these sensors can be geo-distributed, mobile, or only intermittently functional. These factors cause the aggregated sample set to be nonuniformly spaced with varying amounts of data collected per sensor. Due to the nature of how the timing or location measurements are made from the different sensors (e.g., indexed by GPS location), the samples may have significant errors in variables (EIV), where the location error from the different sensors follows an exponential autocorrelation function. In this work we demonstrate how to reconstruct signals using such noisy multi-source, variablerate (MSVR) data samples, and show that the proposed approach improves the error over existing EIV signal reconstruction algorithms.