  In the present article we analyze data from two temperature sensors of the
Mars Science Laboratory, which has been active in Mars since August 2012.
Temperature measurements received from the rover are noisy and must be
processed and validated before being delivered to the scientific community.
Currently, a simple Moving Average (MA) filter is used to perform signal
denoising. The application of this basic method relies on the assumption that
the noise is stationary and statistically independent from the underlying
structure of the signal, an arguable assumption in this kind of harsh
environment. In this paper, we analyze the application of two alternative
methods to process the temperature sensor measurements: the Discrete Wavelet
Transform (DWT) and the Hilbert-Huang Transform (HHT). We consider two
different datasets, one belonging to the current Martian measurement campaigns,
and the other to the Thermal Vacuum Tests. The processing of these datasets
allows to separate the random noise from the interference created by other
systems. The experiments show that the MA filter may provide useful results
under given circumstances. However, the proposed methods allow a better fitting
for all the realistic scenarios, while providing the possibility to identify
and analyze other interesting signal features and artifacts that could be later
studied and classified. The large amount of data to be processed makes
computational efficiency an important requirement in this mission. Considering
the computational cost and the filtering performance, we propose the method
based on DWT as more suitable for this application.
