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Computationally Efficient Direction Finding for Conformal MIMO Radar.

Haochen Wang1, Zhiyu Yu2, Fangqing Wen3

  • 1Department of Communication Technologies and System Design, Technical University of Denmark (DTU), 2800 Lyngby, Denmark.

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Summary
This summary is machine-generated.

This study introduces an efficient algorithm for joint Direction-of-Departure (DOD) and Direction-of-Arrival (DOA) estimation in conformal-array Multiple-Input-Multiple-Output (MIMO) radar. The new method significantly reduces computational cost compared to existing techniques.

Keywords:
DOADODMIMO radarconformal arrayrotationally invariant technique

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Area of Science:

  • Radar Systems Engineering
  • Signal Processing
  • Electromagnetics

Background:

  • Conformal arrays enable Multiple-Input-Multiple-Output (MIMO) radar deployment on irregular surfaces.
  • Existing joint Direction-of-Departure (DOD) and Direction-of-Arrival (DOA) estimation methods for conformal MIMO radar face computational challenges or phase ambiguity issues.

Purpose of the Study:

  • To develop a computationally efficient and accurate algorithm for joint DOD and DOA estimation in conformal-array MIMO radar.
  • To overcome the limitations of spectrum-searching and existing rotation-invariant techniques.

Main Methods:

  • Proposes an improved rotationally invariant technique for phase difference estimation between adjacent sensors.
  • Eliminates phase ambiguity using previously estimated standard phase differences.
  • Employs Least Squares (LS) fitting for closed-form DOD and DOA estimation.

Main Results:

  • The proposed algorithm achieves accurate 2D DOD and DOA estimation for targets.
  • Demonstrates significant computational efficiency, requiring only ~1% of the runtime of spectrum-searching methods.
  • Provides closed-form solutions, enhancing estimation speed.

Conclusions:

  • The improved rotationally invariant technique offers a superior approach for joint DOD and DOA estimation in conformal-array MIMO radar.
  • The method effectively addresses phase ambiguity and computational complexity, paving the way for practical applications.