Abstract
This paper is concerned with DOA estimation using a single-electromagnetic vector sensor in the presence of mutual coupling. Firstly, we apply the temporally smoothing technique to improve the identifiability limit of a single-vector sensor. In particular, we establish sufficient conditions for constructing temporally smoothed matrices to resolve K > 2 incompletely polarized (IP) monochromatic signals with a single-vector sensor. Then, we propose an efficient ESPRIT-based method, which does not require any calibration signals or iterative operations, to jointly estimate the azimuth-elevation angles and the mutual coupling coefficients. Finally, we derive the Cramér-Rao bound (CRB) for the problem under consideration.
Highlights
Direction finding using a single-electromagnetic vector sensor (EMVS) has played an important role in applications such as radar, wireless communications and seismic exploration
An EMVS consists of six components, three identical but orthogonally oriented electrically short dipoles, and another three identical but orthogonally oriented magnetically small loops
After Li [1], and Nehorai and Paldi [2] first introduced the EMVS measurement model to the signal processing community, a variety of studies regarding signal processing with a single EMVS [2,3,4,5,6,7,8] have been extensively carried out. These methods ignore the mutual coupling across the six antenna component, which destroys the underlying model assumptions needed for their efficient implementations
Summary
Direction finding using a single-electromagnetic vector sensor (EMVS) has played an important role in applications such as radar, wireless communications and seismic exploration. After Li [1], and Nehorai and Paldi [2] first introduced the EMVS measurement model to the signal processing community, a variety of studies regarding signal processing with a single EMVS [2,3,4,5,6,7,8] have been extensively carried out These methods ignore the mutual coupling across the six antenna component, which destroys the underlying model assumptions needed for their efficient implementations. Calibration of mutual coupling for vector sensors has been studied recently in [22] and [23] These two methods can offer closed-form solutions for coupling matrix and parameter estimation. They require a coupling-free auxiliary vector sensor and design of a reference signal. The noise is zero-mean, complex Gaussian, and is statistically independent of all the signals
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