4.7 Article

Range Resolution Improvement of GNSS-Based Passive Radar via Incremental Wiener Filter

Journal

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2021.3130062

Keywords

Signal resolution; Wiener filters; Passive radar; Gain; Spaceborne radar; Radar; Image resolution; Global navigation satellite system (GNSS)-based passive radar; incremental wiener filter; range resolution improvement; Tikhonov; truncated singular value decomposition (TSVD)

Funding

  1. Hong Kong Research Grants Council (RGC) Competitive Earmarked Research Grant [PolyU 152151/17E]
  2. Research Institute of Sustainable Urban Development, Hong Kong Polytechnic University
  3. Shenzhen Science and Technology Innovation Commission [JCYJ20170818104822282]
  4. National Key Research and Development Program [2016YFB0501803]

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This article proposes a joint target detection and range resolution improvement method for GNSS-based passive radar, achieving efficient range resolution enhancement through long-time integration technique and incremental Wiener filter.
Global navigation satellite system (GNSS)-based passive radar suffers from short operational range and low range resolution problems, due to the weak signal strength and relatively narrow signal bandwidths of GNSS signals. To increase range resolution, this letter proposes a joint target detection and range resolution improvement method. First, the long-time integration technique is performed to ensure adequate integration gain for the target response out of background noise. Then, the target detector is exploited to obtain the target response and its peak signal-to-noise ratio (PSNR). After that, the incremental Wiener filter is implemented to efficiently enhance range resolution by using the reciprocal of PSNR as the regularization parameter. The field trial results confirm the effectiveness of the proposed method and indicate that the proposed method can not only provide range resolution enhancement performance better than the truncated singular value decomposition (TSVD) method and the Tikhonov method but also has a higher computational efficiency.

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