Work modes recognition and boundary identification of MFR pulse sequences with a hierarchical seq2seq LSTM. Issue 9 (24th July 2020)

• Record Type: Journal Article
• Authors: Li, Yunjie; Zhu, Mengtao; Ma, Yihao; Yang, Jian
• Is Part Of: IET radar, sonar & navigation. Volume 14:Issue 9(2020)
• Page Start: 1343
• Page End: 1353
• Publication Date: 2020-07-24
• Subjects: learning (artificial intelligence) -- radar signal processing -- time series -- neural nets -- feature extraction; multifunction radar work mode -- input pulse sequence -- received radar pulses stream -- multiple work mode class segments -- intra‐mode -- inter‐mode knowledge -- modern MFR -- traditional hand‐crafted features -- enclosed work mode -- transition boundaries -- adjacent modes -- automatic recognition -- MFR work mode sequences -- pulse‐level -- sequence‐to‐sequence -- multiple complexes modulated work mode classes -- state‐of‐the‐artwork mode classification methods -- work modes recognition -- boundary identification -- MFR pulse sequences -- hierarchical seq2seq LSTM; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2020.0060
• Languages: English
• ISSNs: 1751-8784

Abstract

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.