MIMO radars with electromagnetic vector sensor (EMVS) antennas (also known as EMVS-MIMO radars) have been investigated intensively for target direction estimation in recent years. Many existing works on this topic are based on an unrealistic assumption that each EMVS antenna transmits six identically polarized and mutually orthogonal waveforms. Furthermore, target-fluctuation induced non-coherent backscattering in polarization has been entirely overlooked. This paper aims to develop a general transmit-receive signal model for EMVS-MIMO radar direction estimation. Specifically, by taking transmit polarization agility and/or polarization non-coherent backscattering into account, the collected pulse signals are no longer of complete polarization, but of incomplete polarization. By regarding an incomplete polarized (IP) signal as two incoherent completely polarized (CP) signals with the same azimuth-elevation directions and drawing upon the idea of ESPRIT, a new azimuth-elevation direction estimation algorithm is thus derived. The advantage of the new algorithm is that it can provide closed-form, automatically paired azimuth-elevation direction estimates and require no information on the location/displacement of the transmit-receive antennas. Finally, simulations are conducted to demonstrate the effectiveness of the proposed algorithm.

In this paper, a new scheme is proposed to estimate the directions-of-arrival (DOAs) of multiple sources using a coprime electromagnetic vector sensor (EMVS) array. Each EMVS consists of three mutually orthogonal electric dipoles and three mutually orthogonal magnetic loops, all collocated at a single point in space. By exploiting the polarization diversities embedded in the vector sensors, the COarray Polarization Smoothing (COPS) is presented to increase the degrees of freedom (DOFs) from the polarization coarray domain. In contrast to the widely used spatial smoothing technique, the COPS offers the following new insights: 1) it is source polarization-dependent and is proved to offer $20$ DOFs at most, 2) it does not reduce the efficient spatial coarray aperture of the coprime array so that it enables the use of all virtual coarray sensors without resorting to the nontrivial sparse recovery or array interpolation operations, and 3) it can be synthesized with the spatial smoothing to get the DOFs multiplied. Finally, the efficacy of the COPS scheme is verified by numerical examples.

In this paper, the problem of passive direction finding is addressed using an acoustic vector sensor array (AVS), which may be deployed either in free space or near a reflecting boundary. Building upon the $4 \times 1$ vector field measured by an AVS, the particle-velocity coarray augmentation (PVCA) is proposed to admit the underdetermined direction finding using the spatial difference coarray derived from the vectorization of the array covariance matrix. Unlike the widely used spatial coarray Toeplitz recovery technique, the PVCA is applicable to arbitrary array geometries and imposes no reduction of the spatial difference coarray aperture. For the array located at or near a reflecting boundary, the PVCA allows resolving up to $13$ sources, while for the array located in free space, the PVCA can identify $9$ sources at most. By applying to the systematically designed nonuniform arrays, such as coprime arrays and nested arrays, the PVCA can be coupled with the spatial smoothing technique to get the number of resolvable sources multiplied. Finally, the efficacy of the PVCA is verified by numerical simulations.