Unitary Multiple Signal Classification Research Articles

A MIMO-SAR Tomography Algorithm Based on Fully-Polarimetric Data.

A fully-polarimetric unitary multiple signal classification (UMUSIC) tomography algorithm is proposed, which can be used for acquiring high-resolution three-dimensional (3D) imagery, in a polarimetric multiple-input multiple-output synthetic aperture radar (MIMO-SAR) with a small number of baselines. In terms of the elevation resolution, UMUSIC provides an improvement over standard MUSIC by utilizing the conjugate of the complex sample data and converting the complex covariance matrix into a real matrix. The combination of UMUSIC and fully-polarimetric data permits a further reduction of the noise of the sample covariance matrix, which is obtained through pixel averaging of multiple two-dimensional (2D) images. Considering the consistency of four polarizations, this algorithm not only makes scattering centers have the same estimated height in four polarizations, but it also improves the estimation accuracy. Simulation results show that this algorithm outperforms the popular distributed compressed sensing (DCS). Image processing of measured data of an aircraft model using a multiple-input multiple-output synthetic aperture radar (MIMO-SAR) with six baselines is presented to validate the proposed algorithm.

Reduced-Complexity Direction of Arrival Estimation Using Real-Valued Computation with Arbitrary Array Configurations

A low-complexity algorithm is presented to dramatically reduce the complexity of the multiple signal classification (MUSIC) algorithm for direction of arrival (DOA) estimation, in which both tasks of eigenvalue decomposition (EVD) and spectral search are implemented with efficient real-valued computations, leading to about 75% complexity reduction as compared to the standard MUSIC. Furthermore, the proposed technique has no dependence on array configurations and is hence suitable for arbitrary array geometries, which shows a significant implementation advantage over most state-of-the-art unitary estimators including unitary MUSIC (U-MUSIC). Numerical simulations over a wide range of scenarios are conducted to show the performance of the new technique, which demonstrates that with a significantly reduced computational complexity, the new approach is able to provide a close accuracy to the standard MUSIC.

Reduced-Dimension DOA and DOD Estimation with Real-Valued Implementation for Bistatic MIMO Radars

In this paper, a reduced-dimension based direction of arrival (DOA) and direction of departure (DOD) estimator with real-valued implementation is presented by introducing a preprocessing transformation for bistatic multiple-input multiple-output radars is addressed. First, a two-dimensional searching estimator by exploiting unitary multiple signal classification (UMUSIC) technique with automatic pairing is presented. Second, this paper also presents a reduced-dimension (RD) UMUSIC (RD-UMUSIC) estimator by utilizing the characteristic of Kronecker product, which only requires a one-dimensional search for DOA estimation. Following this, the angle of DOD corresponding to the estimated DOA is obtained by using a shaping DOD steering vector approach. Furthermore, the DOA and DOD pairing is given automatically. The proposed estimator has low computational complexity due to the preprocessing unitary transformation and RD approaches. Finally, simulation results are presented to verify the efficiency of the proposed estimator.