  We investigate a semiparametric regression model where one gets noisy non
linear non invertible functions of the observations. We focus on the
application to bearings-only tracking. We first investigate the least squares
estimator and prove its consistency and asymptotic normality under mild
assumptions. We study the semiparametric likelihood process and prove local
asymptotic normality of the model. This allows to define the efficient Fisher
information as a lower bound for the asymptotic variance of regular estimators,
and to prove that the parametric likelihood estimator is regular and
asymptotically efficient. Simulations are presented to illustrate our results.
