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Robust Parameter Estimation with Sensor Arrays in Complex Electromagnetic Environments: 2nd Edition

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

Deadline for manuscript submissions: 15 March 2025 | Viewed by 499

Special Issue Editors


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Guest Editor
College of Electronic Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Interests: array signal processing; direction-of-arrival estimation; source localization; multi-array system
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
1. College of Information System Engineering, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China
2. National Digital Switching System Engineering and Technology Research Center, Zhengzhou 450002, China
Interests: wireless localization; array signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sensor arrays have been widely applied in various fields, e.g., wireless communication, radar, sonar, and navigation. The key roles of sensor arrays include providing spatial parameter estimations, for example, for predicting the direction-of-arrival and source position, and for enhancing parameter estimation performances in other domains. However, with the present complex electromagnetic environment, general estimation methods have great performance degradations when encountering complex signal propagation, such as multipath or occlusion situations. Meanwhile, a sensor array system itself also suffers from uncertainties, such as gain-phase errors, position errors, and mutual coupling, which are classical but long-term problems. So, there are urgent requirements for robust estimation methods, including model-driven and data-driven methods, which can achieve high-precision, high-resolution, and large-capacity parameter estimation, regardless of complex influencing factors. This Special Issue invites contributions on the latest developments and advances of robust processing methods, schemes, or architectures on sensor array systems.

Topics to be covered include, but are not limited to, the following:

  • Direction-of-arrival estimation with unknown array uncertainties;
  • Direction-of-arrival estimation under multipath propagation;
  • Distributed array data fusion under complex propagation;
  • Source localization in complex electromagnetic environments;
  • Array structure design to suppress array uncertainties or complex propagation;
  • Data-driven methods for robust parameter estimation or classification;
  • Robust detection or estimation methods in array radar system;
  • Array system calibration methods;
  • Cooperative estimation methods for distributed multi-array parameters;
  • Parameter estimation methods of sensor arrays based on artificial intelligence technology.

Dr. Jianfeng Li
Dr. Ding Wang
Guest Editors

Manuscript Submission Information

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Keywords

  • sensor array
  • parameter estimation
  • robust estimation methods
  • direction-of-arrival estimation
  • multi-array system
  • source localization
  • digital array radar
  • array structure design
  • data-driven methods

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Published Papers (1 paper)

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Research

19 pages, 5613 KiB  
Article
A New Method for Joint Sparse DOA Estimation
by Jinyong Hou, Changlong Wang, Zixuan Zhao, Feng Zhou and Huaji Zhou
Sensors 2024, 24(22), 7216; https://doi.org/10.3390/s24227216 - 12 Nov 2024
Viewed by 374
Abstract
To tackle the issue of poor accuracy in single-snapshot data processing for Direction of Arrival (DOA) estimation in passive radar systems, this paper introduces a method for judiciously leveraging multi-snapshot data. This approach effectively enhances the accuracy of DOA estimation and spatial angle [...] Read more.
To tackle the issue of poor accuracy in single-snapshot data processing for Direction of Arrival (DOA) estimation in passive radar systems, this paper introduces a method for judiciously leveraging multi-snapshot data. This approach effectively enhances the accuracy of DOA estimation and spatial angle resolution in passive radar systems. Additionally, in response to the non-convex nature of the mixed norm, we propose a hyperbolic tangent model as a replacement, transforming the problem into a directly solvable convex optimization problem. The rationality of this substitution is thoroughly demonstrated. Lastly, through a comparative analysis with existing discrete grid DOA estimation methods, we illustrate the superiority of the proposed approach, particularly under conditions of medium signal-to-noise ratio, varying numbers of snapshots, and close target angles. This method is less affected by the number of array elements, and is more usable in practices verified in real-world scenarios. Full article
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