4 research outputs found
Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks
Spatial regression of random fields based on potentially biased sensing
information is proposed in this paper. One major concern in such applications
is that since it is not known a-priori what the accuracy of the collected data
from each sensor is, the performance can be negatively affected if the
collected information is not fused appropriately. For example, the data
collector may measure the phenomenon inappropriately, or alternatively, the
sensors could be out of calibration, thus introducing random gain and bias to
the measurement process. Such readings would be systematically distorted,
leading to incorrect estimation of the spatial field. To combat this
detrimental effect, we develop a robust version of the spatial field model
based on a mixture of Gaussian process experts. We then develop two different
approaches for Bayesian spatial field reconstruction: the first algorithm is
the Spatial Best Linear Unbiased Estimator (S-BLUE), in which one considers the
quadratic loss function and restricts the estimator to the linear family of
transformations; the second algorithm is based on empirical Bayes, which
utilises a two-stage estimation procedure to produce accurate predictive
inference in the presence of "misbehaving" sensors. In addition, we develop the
distributed version of these two approaches to drastically improve the
computational efficiency in large-scale settings. We present extensive
simulation results using both synthetic datasets and semi-synthetic datasets
with real temperature measurements and simulated distortions to draw useful
conclusions regarding the performance of each of the algorithms
Informed Nonnegative Matrix Factorization Methods for Mobile Sensor Network Calibration
International audienceIn this paper, we consider the problem of blindly calibrating a mobile sensor networkâi.e., determining the gain and the offset of each sensorâfrom heterogeneous observations on a defined spatial area over time. For that purpose, we propose to revisit blind sensor calibration as an informed Nonnegative Matrix Factorization (NMF) problem with missing entries. In the considered framework, one matrix factor contains the calibration structure of the sensorsâand especially the values of the sensed phenomenonâwhile the other one contains the calibration parameters of the whole sensor network. The available information is taken into account by using a specific parameterization of the NMF problem. Moreover, we also consider additional NMF constraints which can be independently taken into account, i.e., an approximate constraint over the mean calibration parameters and a sparse approximation of the sensed phenomenon over a known dictionary. The enhancement of our proposed approaches is investigated through more than 5000 simulations and is shown to be accurate for the considered application and to outperform a multi-hop micro-calibration technique as well as a method based on low-rank matrix completion and nonnegative least squares