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Waveform Diversity Array (WDA): Recent Progress in Radar Target Recognition...
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Waveform Diversity Array (WDA): Recent Progress in Radar Target Recognition and Location

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

Deadline for manuscript submissions: closed (11 August 2024) | Viewed by 2768

Special Issue Editor

Prof. Dr. Shengqi Zhu

E-Mail Website
Guest Editor
National Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China
Interests: space-time adaptive processing (STAP); multiple-input multiple-output (MIMO) radar; synthetic aperture radar (SAR) ground moving target indication (GMTI); sparse signal processing

Special Issue Information

Dear Colleagues,

Waveform diversity array (WDA) radar employs multiple transmitting antennas, each transmitting signals that differ in space, time, frequency, polarization, and modulation mode. These differences enhance the radar system's degrees of freedom, overcoming the limitations of traditional radar in terms of target positioning and identification. WDA represents a revolutionary breakthrough in radar technology.

WDA radar offers simultaneous solutions for various challenges encountered in complex electromagnetic environments. It effectively addresses issues such as anti-mainlobe interference, unambiguous parameter estimation, high-resolution wide swath imaging, multi-dimensional domain feature extraction, and target recognition. In the complex modern battlefield environments, WDA provides a new and precise approach to target location and recognition.

This Special Issue focuses on the latest advancements in WDA radar technology specifically tailored to precise target location and recognition in complex battlefield environments.

Prof. Dr. Shengqi Zhu
Guest Editor

Manuscript Submission Information

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Keywords

  • WDA radar anti-mainlobe interference
  • high-resolution imaging
  • range-ambiguous clutter suppression
  • target location and parameters estimation
  • target recognition
  • imaging and recognition integrated processing
  • weak target detection

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Published Papers (1 paper)

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Research

31 pages, 2257 KiB  
Article
Evaluation of Cluster Algorithms for Radar-Based Object Recognition in Autonomous and Assisted Driving
by Daniel Carvalho de Ramos, Lucas Reksua Ferreira, Max Mauro Dias Santos, Evandro Leonardo Silva Teixeira, Leopoldo Rideki Yoshioka, João Francisco Justo and Asad Waqar Malik
Sensors 2024, 24(22), 7219; https://doi.org/10.3390/s24227219 - 12 Nov 2024
Cited by 1 | Viewed by 2217
Abstract
Perception systems for assisted driving and autonomy enable the identification and classification of objects through a concentration of sensors installed in vehicles, including Radio Detection and Ranging (RADAR), camera, Light Detection and Ranging (LIDAR), ultrasound, and HD maps. These sensors ensure a reliable [...] Read more.
Perception systems for assisted driving and autonomy enable the identification and classification of objects through a concentration of sensors installed in vehicles, including Radio Detection and Ranging (RADAR), camera, Light Detection and Ranging (LIDAR), ultrasound, and HD maps. These sensors ensure a reliable and robust navigation system. Radar, in particular, operates with electromagnetic waves and remains effective under a variety of weather conditions. It uses point cloud technology to map the objects in front of you, making it easy to group these points to associate them with real-world objects. Numerous clustering algorithms have been developed and can be integrated into radar systems to identify, investigate, and track objects. In this study, we evaluate several clustering algorithms to determine their suitability for application in automotive radar systems. Our analysis covered a variety of current methods, the mathematical process of these methods, and presented a comparison table between these algorithms, including Hierarchical Clustering, Affinity Propagation Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Mini-Batch K-Means, K-Means Mean Shift, OPTICS, Spectral Clustering, and Gaussian Mixture. We have found that K-Means, Mean Shift, and DBSCAN are particularly suitable for these applications, based on performance indicators that assess suitability and efficiency. However, DBSCAN shows better performance compared to others. Furthermore, our findings highlight that the choice of radar significantly impacts the effectiveness of these object recognition methods. Full article
(This article belongs to the Special Issue Waveform Diversity Array (WDA): Recent Progress in Radar Target Recognition and Location)
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