NDLI: Work modes recognition and boundary identification of MFR pulse sequences with a hierarchical seq2seq LSTM

Content Provider	IET Digital Library
Author	Li, Yunjie Zhu, Mengtao Ma, Yihao Yang, Jian
Abstract	Recognition of multi-function radar (MFR) work mode in an input pulse sequence is a fundamental task to interpret the functions and behaviour of an MFR. There are three major challenges that must be addressed: (i) The received radar pulses stream may contain an unknown number of multiple work mode class segments. (ii) The intra-mode and inter-mode knowledge of a modern MFR may be too flexible and complicated to be represented and learned through traditional hand-crafted features and learning models. (iii) The variable duration of each enclosed work mode makes the identification of the transition boundaries of adjacent modes difficult. To address these challenges and implement automatic recognition of MFR work mode sequences at a pulse-level, this study develops a novel processing framework based on a time series representation of MFR work mode sequence and sequence-to-sequence (seq2seq) long short-term memory network. The proposed method can not only automatically recognise multiple complexes modulated work mode classes in a pulse sequence. Still, it can also accurately identify the transition boundaries between each class by labelling the class information for each pulse. The experimental results showed the extended capabilities and improved performance of the proposed method over the state-of-the-art work mode classification methods.
Starting Page	1343
Ending Page	1353
Page Count	11
ISSN	17518784
Volume Number	14
e-ISSN	17518792
Issue Number	Issue 9, Sep (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/14/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2020.0060
Journal	IET Radar, Sonar & Navigation
Publisher Date	2020-04-28
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adjacent Modes Automatic Recognition Boundary Identification Computer Vision And Image Processing Technique Enclosed Work Mode Feature Extraction Hierarchical Seq2seq LSTM Input Pulse Sequence Inter-mode Knowledge Intra-mode Knowledge Engineering Technique Learning in AI MFR Pulse Sequence MFR Work Mode Sequence Modern MFR Multifunction Radar Work Mode Multiple Complexes Modulated Work Mode Classes Multiple Work Mode Class Segments Neural Nets Optical, Image And Video Signal Processing Pulse-level Radar Signal Processing Received Radar Pulses Stream Sequence-to-sequence Signal Processing And Detection State-of-the-artwork Mode Classification Method Statistics Time Series Traditional Hand-crafted Feature Transition Boundaries Work Mode Recognition
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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