Fast Coherent Signal Subspace-Based Method for Bearing and Range of Buried Objects Estimation in the Presence of Colored Noise

In this paper, a new method for simultaneous range and bearing estimation for buried objects in the presence of an unknown colored noise is proposed. We propose a method based on multiple signal classification (MUSIC) with a new source steering vector which includes both the reflection and the refraction of wave at water-sediment interface at each sensor. The bearing and the range objects at each sensor are expressed as a function of those at the first sensor. To reduce the computational load we propose the fixed-Point algorithm, which it is faster than singular value decomposition (SVD) for MUSIC, to compute only the leading eigenvectors. A novel iterative denoising algorithm based on the noise subspace spanned by the eigenvectors associated with the smallest eigenvalues is developed when the noise spectral matrix is one unknown band matrix. The bilinear focusing operator is used to decorrelate the received wideband signals and to estimate the coherent signal subspace. The performance of the proposed algorithms is evaluated by computer simulations. Finally,we test the proposed algorithms on experimental data recorded during underwater acoustic experiments.