Situation-aware technologies enabled by multitarget tracking algorithms will create new services and applications in emerging fields such as autonomous navigation and maritime surveillance. The system models underlying multitarget tracking algorithms often involve unknown parameters that are potentially time-varying.

Standard interpolation techniques are implicitly based on the assumption that the signal lies on a single homogeneous domain. In contrast, many naturally occurring signals lie on an inhomogeneous domain, such as brain activity associated to different brain tissue. We propose an interpolation method that instead exploits prior information about domain inhomogeneity, characterized by different, potentially overlapping, subdomains. 

We obtain a characterization of all wavelets leading to analytic wavelet transforms (WT). The characterization is obtained as a byproduct of the theoretical foundations of a new method for wavelet phase reconstruction from magnitude-only coefficients. The cornerstone of our analysis is an expression of the partial derivatives of the continuous WT, which results in phase-magnitude relationships similar to the short-time Fourier transform setting and valid for the generalized family of Cauchy wavelets. 

The problem of locating signals transmitted in the proximity of an antenna array has been studied extensively in the signal processing literature. In this paper, we consider the standard array manifold models used in these works and show that they differ, sometimes significantly, from the model based on electromagnetic theory.