  This work proposes a low-complexity estimation approach for an orthogonal
time frequency space (OTFS)-based integrated sensing and communication (ISAC)
system. In particular, we first define four low-dimensional matrices used to
compute the channel matrix through simple algebraic manipulations. Secondly, we
establish an analytical criterion, independent of system parameters, to
identify the most informative elements within these derived matrices,
leveraging the properties of the Dirichlet kernel. This allows the distilling
of such matrices, keeping only those entries that are essential for detection,
resulting in an efficient, low-complexity implementation of the sensing
receiver. Numerical results, which refer to a vehicular scenario, demonstrate
that the proposed approximation technique effectively preserves the sensing
performance, evaluated in terms of root mean square error (RMSE) of the range
and velocity estimation, while concurrently reducing the computational effort
enormously.
