  In this paper, we consider the estimation of the unknown parameters of the
multiple chirp signal model in presence of additive error. The chirp signals
are quite common in many areas of science and engineering, specially sonar,
radar, audio signals etc. The observed signals are usually corrupted by noise.
In different signal processing applications it is observed that the errors may
be heavy tailed. In this paper it is assumed that the additive errors have mean
zero but may not have finite variance and are independent and identically
distributed. We consider the least squares estimators and the approximate least
squares estimators which maximize a periodogram like function. It has been
observed that both the estimators are strongly consistent. The asymptotic
distribution of the least squares estimators is obtained under the assumption
that the additive errors are from a symmetric stable distribution. The
approximate least squares estimators have the same asymptotic distribution as
the least squares estimators. We perform some numerical simulations to see how
the proposed estimators work. It is observed that the least squares estimators
perform slightly better than the approximately least squares estimators in
terms of the biases and mean absolute deviation.
