Multi-component chirp signal models with equal chirp rates appear in various
radar applications, e.g., synthetic aperture radar, echo signal of a rapid
mobile target, etc. Many sub-optimal estimators have been developed for such
models, however, these suffer from the problem of either identifiability or
error propagation effect. In this paper, we have developed theoretical
properties of the least squares estimators (LSEs) of the parameters of
multi-component chirp model with equal chirp rates, where the model is
contaminated with linear stationary errors. We also propose two computationally
efficient estimators as alternative to LSEs, namely sequential combined
estimators and sequential plugin estimators. Strong consistency and asymptotic
normality of these estimators have been derived. Interestingly, it is observed
that sequential combined estimator of the chirp rate parameter is
asymptotically efficient. Extensive numerical simulations have been performed,
which validate satisfactory computational and theoretical performance of all
three estimators. {We have also analysed a simulated radar data with the help
of our proposed estimators of multi-component chirp model with equal chirp
rates, which performs efficiently in recovery of inverse synthetic aperture
radar (ISAR) image of a target from a noisy data