Radar reconnaissance plays a crucial role in obtaining intelligence information about enemy radar operating modes, combat intentions, and other related data. The parameter measurement, discrimination, identification, and localization techniques for traditional radar have become increasingly mature. However, with the emergence of modern multifunctional radar, the time sequence and frequency agility of radar pulse sequences have become more complex. The methods of discrimination and identification based on statistical rules and pulse descriptor waveform have become challenging to apply in the current complex electromagnetic environment. On the other hand, localization methods such as single-pulse-based angle of arrival (AOA), time difference of arrival (TDOA), and frequency difference of arrival (FDOA) suffer from poor localization accuracy due to insufficient information utilization, making it difficult to meet the requirements of accurate electromagnetic situational awareness. Artificial intelligence based on neural networks and deep learning models has the capability to analyze massive complex data. By observing radar over an extended period, it can acquire a massive amount of multidimensional data information about radar. By extracting hidden patterns within the data to be analyzed and the observed data, it has the potential to significantly improve the performance of discrimination and identification in complex electromagnetic environments. Synthetic aperture positioning (SAP) technology, which has emerged in recent years, is a high-precision passive positioning technique. It utilizes the relative motion between the platform and the radiation source to form a long virtual antenna, thereby greatly improving localization accuracy and multi-target resolution. It provides more accurate location information for individual identification of radiation sources.

Intelligent processing has further blurred the boundaries of signal discrimination and identification, simplifying the reconnaissance processing flow. However, the methods for exploring multidimensional correlated features such as radar radiation source pulse descriptors, pulse group characteristics, in-pulse features, radar operating modes, and radar RF fingerprints are not yet comprehensive enough. Synthetic aperture positioning can enhance localization accuracy and multi-target resolution. However, further analysis is needed to improve its adaptability to complex radiation source waves and its suitability for radiation source localization with squint scenarios.

This special issue invites original contributions in the field of intelligent processing and high-precision positioning in radar reconnaissance, with a particular focus on algorithm models, system design, data processing methods, and more. The topics of this special issue include, but are not limited to:

1) Intelligent Signal Discrimination Methods for Multifunctional Radar

2) Intelligent Signal Recognition Methods for Multifunctional Radar

3) Extraction of Radar Pulse RF Fingerprint Features and Individual Identification

4) Synthetic Aperture Positioning Methods for radar by Satellite/Airborne

5) Intelligent Processing Methods for Distributed Radar Reconnaissance

All submissions will be peer reviewed according to the Journal of Beijing Institute of Technology guidelines. The contributions should be original and have not been published or submitted elsewhere.

Manuscript should be submitted online viahttps://mc03.manuscriptcentral.com/jbit, and select the special issue “202402- Intelligent Processing and High- Precision Positioning of Radar Signals in Reconnaissance Systems”. Prospective authors may consult the sitehttp://journal.bit.edu.cn/jbitfor guidelines and information on paper submission.

Schedule

Submission deadline: Oct. 30, 2023

Publication date: Apr. 30, 2024

Guest Editors

Hao Huan, Beijing Institute of Technology, China(haohuan@bit.edu.cn)

Yunjie Li, Beijing Institute of Technology, China(liyunjie@bit.edu.cn)

Xiaogang Tang, Space Engineering University, China (titantxg@163.com)

Guangcai Sun, Xidian University, China(gcsun@xidian.edu.cn)

Gaogao Liu, Xidian University, China(ggliu@xidian.edu.cn)

Fuyuan Xu, Nanjing Research Institute of Electronic Equipment (xu_fuyuan@hotmail.com)