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DOA Estimation on One-Bit Quantization Observations through Noise-Boosted Multiple Signal Classification.

Yan Pan1, Li Zhang1, Liyan Xu2

  • 1College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590, China.

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|July 27, 2024
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Summary
This summary is machine-generated.

This study introduces a noise-boosted quantizer unit (NBQU) for improved direction-of-arrival (DOA) estimation using one-bit quantized data. The NBQU method enhances accuracy and resolution compared to existing one-bit techniques.

Keywords:
direction of arrivalmultiple-signal classificationnoise-boosted quantizer unitone-bit quantization

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Area of Science:

  • Signal Processing
  • Array Signal Processing
  • Information Theory

Background:

  • One-bit quantized data offers low-complexity implementation for Direction-of-Arrival (DOA) estimation.
  • Existing signal processing methods struggle to achieve sufficient estimation accuracy with one-bit data.
  • Multiple Signal Classification (MUSIC) is a common DOA estimation algorithm.

Purpose of the Study:

  • To propose an efficient one-bit MUSIC method for DOA estimation using a novel noise-boosted quantizer unit (NBQU).
  • To improve the estimation accuracy and resolution of DOA estimation with one-bit quantized data.
  • To investigate the impact of injected noise on the performance of one-bit DOA estimation.

Main Methods:

  • Injecting noise components into receiving data before a uniform linear array (ULA) of one-bit quantizers.
  • Designing a noise-boosted quantizer unit (NBQU) based on the noise-injected data.
  • Developing an efficient one-bit MUSIC algorithm utilizing the NBQU for DOA estimation.

Main Results:

  • The proposed NBQU-based MUSIC method demonstrates superior estimation accuracy and resolution compared to existing one-bit MUSIC methods.
  • Numerical results confirm the performance enhancement achieved by injecting noise.
  • Optimal root mean square (RMS) of injected noise allows the proposed method's accuracy to approach that of MUSIC using complete analog data.

Conclusions:

  • The NBQU-based MUSIC method significantly improves DOA estimation accuracy and resolution for one-bit quantized data.
  • Injected noise, when optimally managed, is crucial for enhancing the performance of one-bit DOA estimation.
  • This approach offers a promising solution for accurate DOA estimation in low-complexity, one-bit systems.