Sensor calibration is usually performed only on one error signal although calibration sessions often collect repeated samples. This work formally puts forward and studies adequate solutions to consider all replicates to improve predictive accuracy.
Using a wavelet-decompositon of a random field, the aim is to efficiently estimate (latent) spatial models.
There is considerable research on improving navigation accuracy of unmanned (aerial) vehicles. This direction of research focuses on improving stochastic sensor calibration to integrate navigation filters and produce more accurate navigation performance.
This work puts forward an approach to test causal links between irregularly sampled signals with applications to nitrogen signalling between roots and shoots of plants
This work compares current moment-matching techniques used to calibrate inertial sensors and identifies the optimal technique from a theoretical (and applied) perspective
Low-cost inertial sensors are increasingly being used jointly in order to improve their navigation performance and this work provides a computationally feasible solution to solve these complex problems
This work combines the information from multiple signals to improve the estimation of stochastic model estimation for sensor calibration