  Soft sensing is a way to indirectly obtain information of signals for which
direct sensing is difficult or prohibitively expensive. It may not a priori be
evident which sensors provide useful information about the target signal. There
may be sensors irrelevant for the estimation as well as sensors for which the
information is very poor. It is often required that the soft sensor should
cover a wide range of operating points. This means that some sensors may be
useful in certain operating conditions while irrelevant in others, while others
may have no bearing on the target signal whatsoever. However, this type of
structural information is typically not available but has to be deduced from
data. A further compounding issue is that multiple operating conditions may be
described by the same model, but which ones is not known in advance either. In
this contribution, we provide a systematic method to construct a soft sensor
that can deal with these issues. While the different models can be used, we
adopt the multi-input single output finite impulse response models since they
are linear in the parameters. We propose a single estimation criterion, where
the objectives are encoded in terms of model fit, model sparsity (reducing the
number of different models), and model parameter coefficient sparsity (to
exclude irrelevant sensors). A post-processing model clustering step is also
included. As proof of concept, the method is tested on field test datasets from
a prototype vehicle.
