Existing source localization methods mostly assume that the sources are pure near-field sources or pure far-field sources.While in practical localization systems

both far-field and near-field sources may exist simultaneously.To classify far-field and near-field sources

and also to achieve high-precision source localization

a novel mixed far-field and near-field source localization algorithm is proposed in sparse signal reconstruction framework.The algorithm first utilizes anti-diagonal elements of array covariance matrix and reweighted

l

1

-norm penalty to obtain DOA estim

ation of all sources

then classifies far-field and near-field sources and successively obtains range estimation of near-field sources via one-dimensional search

by exploring the feature that the range parameters of far-field and near-field sources are located in different areas.Theoretically

we analyze the reconstruction performance of the reweighted

l

1

-norm penalty algorithm.The proposed algorithm is not only suitable for dealing with Gaussian signals and non-Gaussian signals

but also without multi-dimensional search and parameter pairing process

and also without knowing the number of sources.Meanwhile

the proposed algorithm can even provide good estimation accuracy.Computer simulation results validate the effectiveness of the proposed algorithm.