BOMP‐based angle estimation with polarimetric MIMO radar with spatially spread crossed‐dipole

Abstract

For polarimetric multi-input multi-output (MIMO) radar with spatially spread crossed-dipole, this article studies the problem of joint direction of arrival (DOA) and polarisation parameter estimation based on block-orthogonal matching pursuit (BOMP) algorithm. First, the signal model of polarimetric MIMO radar with spatially spread crossed-dipole is established, and then the covariance matrix of the received data is calculated. Using the relationship between polarisation parameter and DOA in the crossed-dipole, sparse dictionary matrix is constructed within only DOA parameter and it will be translated into a block sparse problem. Then, fast BOMP algorithm is used to estimate their support positions and their amplitudes. Last, DOA estimation is calculated by support positions and polarisation parameter is estimated by the amplitudes of the support positions. The proposed algorithm has three advantages. One is that overcomplete dictionary is constructed within only the DOA, which has a small computational complexity. Another one is that the problem of strong mutual coupling among collocated crosseddipole is solved by using the spatially spread crossed-dipole. The last one is that the DOA and polarisation estimations can pair automatically without any additional processing. Computer simulation results demonstrate the effectiveness of the proposed algorithm. 2 Signal model of polarimetric MIMO radar with spatially spread crossed-dipole A single spatially spread crossed-dipole is shown in Fig. 1, which electric dipole Ex is placed along with the x axis and electric dipole

Unimodular multiple‐input‐multiple‐output radar wave‐form design with desired correlation properties

“…Different from traditional radar systems, MIMO radar allows each antenna element to transmit independent waveform, enabling enhanced waveform diversity. This property also provides extra degrees of freedom to improve the performance of MIMO radar, including better target detection, improved parameter estimation, and increased parameter identifiability [2][3][4][5].…”

ESPRIT-Based DOA Estimation with Spatially Spread Long Dipoles and/or Large Loops

“…Modification of the vector cross-product algorithm is developed for spatially spread electromagnetic vector sensors [8,16]. The polarization estimation is obtained by comparing polarization components for spatially spread electromagnetic vector sensors [23]. The DOA estimation is obtained using super-resolution for coprime L-shaped spatially spread electromagnetic vector sensors [19].…”

“…The DOA estimation is obtained using super-resolution for coprime L-shaped spatially spread electromagnetic vector sensors [19]. Third, under the condition of collocated or spatially spread vector sensors, various estimation algorithms based on sparse representation are proposed, such as OMP and BOMP [22,23]. The above studies are based on the conditions of a short electric dipole and/or small magnetic loop, the radiation efficiency is very low.…”