4.7 Article

PARAFAC Estimators for Coherent Targets in EMVS-MIMO Radar with Arbitrary Geometry

期刊

REMOTE SENSING
卷 14, 期 12, 页码 -

出版社

MDPI
DOI: 10.3390/rs14122905

关键词

parallel factor decomposition; estimation; MIMO radar; vector sensors; coherent target

资金

  1. China NSF [62071476]
  2. Shaanxi Provincial Key RD Plan [2020SF-166]

向作者/读者索取更多资源

In this paper, the multiple parameter estimation problem for bistatic EMVS-MIMO radar with coherent targets is investigated. Three tensor-aware spatial smoothing estimators are introduced and analyzed in detail. Numerical experiments validate the theoretical findings.
In the past few years, multiple-input multiple-output (MIMO) radar with electromagnetic vector sensor (EMVS) array, or called EMVS-MIMO radar, has attracted extensive attention in target detection. Unlike the traditional scalar sensor-based MIMO radar, an EMVS-MIMO radar can not only provides a two-dimensional (2D) direction finding of the targets but also offers 2D polarization parameter estimation, which may be important for detecting weak targets. In this paper, we investigate into multiple parameter estimations for a bistatic EMVS-MIMO radar in the presence of coherent targets, whose transmitting EMVS and receiving EMVS are placed in an arbitrary topology. Three tensor-aware spatial smoothing estimators are introduced. The core of the proposed estimators is to de-correlate the coherent targets via the spatial smoothing technique and then formulate the covariance matrix into a third-order parallel factor (PARAFAC) tensor. After the PARAFAC decomposition of the tensor, the factor matrices can be obtained. Thereafter, the 2D direction finding can be accomplished via the normalized vector cross-product technique. Finally, the 2D polarization parameter can be estimated via the least squares method. Unlike the state-of-the-art PARAFAC estimator, the proposed estimators are suitable for arbitrary sensor geometries, and they are robust to coherent targets as well as sensor position errors. In addition, they have better estimation performance than the current matrix-based estimators. Moreover, they are computationally efficient than the current subspace methods, especially in the presence of a large-scale sensor array. In addition, the proposed estimators are analyzed in detail. Numerical experiments coincide with our theoretical findings.

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