  In this paper, we investigate the application of continuous sparse signal
reconstruction algorithms for the estimation of the ranges and speeds of
multiple moving targets using an FMCW radar. Conventionally, to be
reconstructed, continuous sparse signals are approximated by a discrete
representation. This discretization of the signal's parameter domain leads to
mismatches with the actual signal. While increasing the grid density mitigates
these errors, it dramatically increases the algorithmic complexity of the
reconstruction. To overcome this issue, we propose a fast greedy algorithm for
off-the-grid detection of multiple moving targets. This algorithm extends
existing continuous greedy algorithms to the framework of factorized sparse
representations of the signals. This factorized representation is obtained from
simplifications of the radar signal model which, up to a model mismatch,
strongly reduces the dimensionality of the problem. Monte-Carlo simulations of
a K-band radar system validate the ability of our method to produce more
accurate estimations with less computation time than the on-the-grid methods
and than methods based on non-factorized representations.
