2017/10/25 by Sudeep Salgia, Animesh Kumar, Salgia, Sudeep +1
Computer Science · Environmental Science · Mathematics · #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #Information Theory (cs.IT) #Soil Geostatistics and Mapping #Target Tracking and Data Fusion in Sensor Networks #cs.IT #math.IT
paper · pdf · doi:10.48550/arxiv.1710.09451
Abridged version submitted to ICASSP 2018
openalex publication_date 2017/10/25 · arxiv created 2017/12/05 · arxiv updated 2017/12/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Sampling of physical fields with mobile sensors is an upcoming field of interest. This offers greater advantages in terms of cost as often just a single sensor can be used for the purpose and this can be employed almost everywhere without sensing stations and has nominal operational costs. In a mobile sensing setup, the accurate knowledge of sampling locations may lead to a manifold increase in the costs. Moreover, the inertia of the moving vehicle constrains the independence between the intersample distances, making them correlated. This work, thus, aims at estimating spatially bandlimited fields from samples, corrupted with measurement noise, collected on sampling locations obtained from an autoregressive model on the intersample distances. The autoregressive model is used to capture the correlation between the intersample distances. In this setup of sampling at unknown sampling locations obtained from an autoregressive model on intersample distances, the mean squared error between the field and its estimated version has been shown to decrease as O(1/n), where n is the average number of samples taken by the moving sensor.