  This paper presents a novel and efficient wireless channel estimation scheme
based on a tapped delay line (TDL) model of wireless signal propagation, where
a data-driven machine learning approach is used to estimate the path delays and
gains. The key motivation for our novel channel estimation model is to gain
environment awareness, i.e., detecting changes in path delays and gains related
to interesting objects and events in the field. The estimated channel state
provides a more detailed measure to sense the field than the single-tap channel
state indicator (CSI) in current OFDM systems. Advantages of this approach also
include low computation time and training data requirements, making it suitable
for environment awareness applications.
  We evaluate this model's performance using Matlab's ray-tracing tool under
static and dynamic conditions for increased realism instead of the standard
evaluation approaches that rely on classical statistical channel models. Our
results show that our TDL-based model can accurately estimate the path delays
and associated gains for a broad-range of locations and operating conditions.
Root-mean-square estimation error was less than $10^{-4}$, or $-40$dB, for SNR
$\geq 60$dB in all of our experiments. Our results show that interference of a
flying drone on signal multipaths, in a preliminary experiment, can be detected
in estimated channel states which, otherwise, remains obscured in conventional
CSI.
