Angle Estimation With Electromagnetic Vector-Sensor Array And Its Application In MIMO Radar

Polarization of electromagnetic wave is another important signal information besides amplitude, phase, frequency and waveform. The performance of radar and communication systems can be significantly improved by fully utilizing the polarization. Therefore, the polarization sensitive array consisted of electromagnetic vector sensor has broad applications in radar, communication, sonar and biomedicine. On the other hand, MIMO radar is a hot research topic in recent years with its advantages in target detection and parameter estimation. Parameter (such as DOA and polarization state angle) estimation is one of the main tasks of radar and communication systems. Therefore, this paper studies on DOA and polarization state estimation with electromagnetic vector sensor array and its applications in MIMO radar. Specific contents can be summarized as the following four parts.The first part studies the problem of DOA estimation of coherent incident signals for electromagnetic vector sensor. First, signal model is built with the scenario that completely polarized signal impinges upon traditional spatially collocating electromagnetic vector sensor, followed by reviewing of several DOA estimation algorithms based on electromagnetic vector sensor. Furthermore, no utilization of the cross-correlation information among the smoothed subarrays leads to low resolution of polarization smoothing algorithm. An improved polarization smoothing algorithm of direction-of-arrival estimation for coherent sources is proposed, which is called weighted polarization smoothing algorithm. Full use of auto-correlation and cross-correlation of the subarrays composed of six components of electromagnetic vector-sensor array is performed in weighted polarization smoothing algorithm. An equivalent covariance matrix is obtained by a weighted sum of36sub-matrixes. The derivation of theoretical formula of optimal weighting coefficients and analysis of the rank of equivalent signal covariance matrix constrained by its diagonalization are accomplished. Simulation results are presented to illustrate higher resolution and accuracy of weighted polarization smoothing against polarization smoothing.The second part proposes four structures of spatially noncollocating electromagnetic vector sensor (EMVS) array and studies its DOA and/or polarization estimation. Traditional spatially collocating EMVS array has strong mutual coupling, resulting the difficulty of the implementation of EMVS hardware, and the decline of the DOA and polarization estimation performance seriously. Spatially noncollocating EMVS (SNC-EMVS) can reduce greatly the mutual coupling and the hardware cost compared with the spatially collocating EMVS (SC-EMVS). Therefore, we propose SNC-EMVS array to solve the problem of strong mutual coupling in SC-EMVS. For two dimensional high accuracy DOA estimation, we firstly propose a new SNC-EMVS array with triangular configuration, which is composed of a single SNC-EMVS and a single dipole. Without adding sensors, we then propose a double-triangular configuration array to achieve two dimensional (2-D) high accuracy DOA estimation. The double-triangular configuration array is obtained by a two-step design. The first step aims to make the configurations of SNC-EMVS satisfy the "vector cross-product" Poynting-vector estimator. The second step focuses on extending the2-D array apertures of SNC-EMVS. Detection probability and estimation accuracy of an actual array radar often cannot be satisfied by only using six or seven sensors of the above two array structures. Therefore, we thirdly propose a sparse uniform rectangular SNC-EMVS array and a novel2-D DOA and polarization parameters estimation algorithm. But in practical applications, electric-field response and magnetic-field response in EMVS are often inconsistent, which leads to estimation performance degradation. Therefore, we fourth propose a sparse uniform rectangular SNC-EMVS array consisted of only electric-dipoles. The above four array structures not only reduce mutual coupling but also extend the2-D apertures to improve DOA estimation accuracy by using sparse structures.The third part studies the problem of DOD and DOA estimation for bistatic MIMO radar. Since the degree of freedom of MIMO radar is equal to Kronecker product between the number of transmitter and receiver, MIMO radar greatly increases computational complexity besides providing high accuracy parameter estimates. Furthermore, the pairing of DODs and DOAs in bistatic MIMO radar is also an important issue. Therefore, we propose unitary ESPRIT and beamspace root MUSIC method to reduce the computational complexity. Unitary ESPRIT uses real-valued operations throughout ESPRIT algorithm to reduce the computational complexity of the complex-valued ESPRIT algorithm. Beamspace root MUSIC uses beamspace transformation and root-like method to reduce the computational complexity of conventional MUSIC algorithm. In addition, from the perspective of improving the estimation accuracy, we propose distributed array bistatic MIMO radar and unitary two-size ESPRIT to estimate the DOD and DOA. Compared to the uniform linear array with the half-wavelength element spacing, distributed array can extend the physical aperture without increasing the hardware complexity to greatly improve the angle estimation performance. Moreover, the proposed three methods are able to achieve automatic pairing between DODs and DOAs.The fourth part studies the problem of DOA estimation for the EMVS MIMO radar. Considering the advantages of parameter estimation using MIMO radar, the EMVS is applied to MIMO radar and propose an interferometric EMVS MIMO radar. A short baseline and a long baseline of the transmitting array are utilized to obtain high accuracy DOD estimation via the two-size ESPRIT. Similarly, the high accuracy DOA estimation can be obtained by utilizing the EMVS receive array. The proposed system can obtain the waveform diversity offered by MIMO radar and the polarization diversity offered by EMVS simultaneously. Also, it is capable of extending array aperture without increasing sensors and hardware costs, which can improve the angle estimation accuracy greatly. Moreover, for the problem of the bad direction of arrival (DOA) estimation accuracy because of no utilization of the transmitted polarization information in EMVS MIMO radar, a transmitted polarization optimization algorithm based on minimizing the Cramer-Rao bound is proposed. The proposed algorithm can provide better estimation accuracy than the fixed polarization DOA estimation algorithm, and remain the advantages of the automatic pairing between the2-D DOA estimation and arbitrary placement of the transmitted electromagnetic vector sensor antennas.