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A tensor-based subspace approach for bistatic MIMO radar in spatial colored noise.

Xianpeng Wang1, Wei Wang2, Xin Li3

  • 1College of Automation, Harbin Engineering University, No. 145 Nantong Street, Harbin 150001, China. wangxianpeng@hrbeu.edu.cn.

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This study introduces a novel tensor-based subspace method for precise direction estimation in bistatic MIMO radar systems. The approach effectively handles spatial colored noise, improving accuracy in direction of departure (DOD) and direction of arrival (DOA) calculations.

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Area of Science:

  • Radar Signal Processing
  • Array Signal Processing
  • Tensor Analysis

Background:

  • Bistatic MIMO radar systems face challenges with spatial colored noise.
  • Accurate estimation of direction of departure (DOD) and direction of arrival (DOA) is crucial for radar performance.

Purpose of the Study:

  • To propose a novel tensor-based subspace approach for DOD and DOA estimation.
  • To effectively mitigate spatial colored noise in bistatic MIMO radar.

Main Methods:

  • Received signals are formed into a third-order measurement tensor.
  • A cross-covariance tensor is formulated to eliminate spatial colored noise.
  • Higher-order singular value decomposition (HOSVD) and ESPRIT are employed for subspace construction and parameter estimation.

Main Results:

  • The proposed method demonstrates superior angle estimation performance compared to existing techniques.
  • Automatic pairing of DOD and DOA is achieved.
  • Simulation results validate the method's effectiveness and advantages.

Conclusions:

  • The tensor-based subspace approach offers improved DOD and DOA estimation in bistatic MIMO radar.
  • The cross-covariance tensor technique effectively suppresses spatial colored noise.
  • The method provides a robust solution for advanced radar applications.