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Research and Development of Signal Processing for Radar Sensors

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Radar Sensors".

Deadline for manuscript submissions: 31 March 2027 | Viewed by 213

Editors

School of Automation, China University of Geosciences, Wuhan 430074, China
Interests: radar and communication signal processing; sensor signal processing; deep learning techniques
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Automation, China University of Geosciences, Wuhan, China
Interests: machine vision and its applications in industry; image recognition, and processing
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Automation, China University of Geosciences, Wuhan, China
Interests: computer vision; machine learning; intelligent transportation systems

Special Issue Information

Dear Colleagues,

As one of the core foundations of information science, signal processing has been widely applied in fields such as communication, automation, radar, sonar, biomedical, geophysical, and fault diagnosis, and the integration of signal processing and sustainable development is becoming a key force in promoting the green transformation of digital society. In recent years, new signal processing technologies have emerged continuously, especially the application of deep learning algorithms in signal processing, which has led to significant breakthroughs in the integration of computing and artificial intelligence, supporting the development of frontier technologies such as 6G communication, intelligent perception, and speech and image processing. Signal processing technologies provide solid support for achieving an environmentally friendly information society by improving energy efficiency, optimizing algorithms, and building green network architectures. The goal of this Special Issue is to provide a comprehensive perspective on the research status and future development of signal processing, including the latest research on traditional signal processing algorithms and deep learning algorithms. Topics of interest in this Special Issue include, but are not limited to, the following:

  1. Time domain signal processing methods;
  2. Frequency domain signal processing methods;
  3. Non-stationary signal processing methods;
  4. Nonlinear signal processing methods;
  5. Signal processing methods based on deep learning;
  6. Application of signal processing methods;
  7. Implementation of signal processing methods on embedded platforms.

Dr. Wei Xue
Dr. Yue Yang
Dr. Wei Liu
Guest Editors

Manuscript Submission Information

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Keywords

  • signal processing theory
  • application of signal processing
  • implementation of signal processing
  • signal processing combined with deep learning algorithms

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Published Papers (1 paper)

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Research

30 pages, 5587 KB  
Article
Robust Polarization-Domain Adaptive Anti-Jamming via Forgetting-Factor Covariance Estimation and Adaptive Diagonal Loading
by Yuancong Xiong, Huafeng He, Buma Xiao, Liyuan Wang and Zhen Li
Sensors 2026, 26(13), 4110; https://doi.org/10.3390/s26134110 (registering DOI) - 29 Jun 2026
Abstract
To address robust polarization-domain adaptive anti-jamming for dual-polarized radars with limited secondary data and time-varying interference, this paper proposes a covariance-reliability-driven MVDR framework based on forgetting-factor covariance estimation and adaptive diagonal loading. The forgetting-factor recursion assigns larger weights to recent jammer-plus-noise snapshots to [...] Read more.
To address robust polarization-domain adaptive anti-jamming for dual-polarized radars with limited secondary data and time-varying interference, this paper proposes a covariance-reliability-driven MVDR framework based on forgetting-factor covariance estimation and adaptive diagonal loading. The forgetting-factor recursion assigns larger weights to recent jammer-plus-noise snapshots to track nonstationary interference, while the adaptive loading coefficient is jointly controlled by sample deficiency and covariance condition-number degradation to improve inversion stability. Unlike many robust adaptive beamforming methods that require steering-vector uncertainty sets, mismatch distributions, or subspace information, the proposed method relies only on secondary data and a small set of scalar design parameters. Simulation results based on a synthetic dual-polarized array model show that the proposed method achieves competitive output SINR, effective jammer suppression, and improved robustness to moderate DOA and polarization mismatch under limited-snapshot and time-varying interference conditions. Complexity analysis indicates that the proposed method has the same dominant computational order as standard covariance-based MVDR beamforming, apart from condition-number evaluation. The present validation is simulation-based, and further verification using measured polarimetric radar data, realistic propagation models, or hardware experiments is still required. Full article
(This article belongs to the Special Issue Research and Development of Signal Processing for Radar Sensors)
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