Method for operating mode identification of multi‐function radars based on predictive state representations

Ou, Jinping; Yong-guang, Chen; Zhao, Feng; Liu, Jin; Xiao, Shunping

doi:10.1049/iet-rsn.2016.0182

Cited by 20 publications

(9 citation statements)

References 17 publications

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“…According to the PSR-based framework of MFR we constructed in [31], the MFR is modelled by combining PSR with a hierarchical structure, taking the advantages of PSR in dynamic system representation and characteristic extraction. The model is pre-trained by utilizing the MFR signals intercepted by the ELINT system.…”

Section: Psr-based Framework Of Mfrmentioning

confidence: 99%

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Novel Approach for the Recognition and Prediction of Multi-Function Radar Behaviours Based on Predictive State Representations

Ou¹,

Yong-guang²,

Zhao³

et al. 2017

Sensors

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The extensive applications of multi-function radars (MFRs) have presented a great challenge to the technologies of radar countermeasures (RCMs) and electronic intelligence (ELINT). The recently proposed cognitive electronic warfare (CEW) provides a good solution, whose crux is to perceive present and future MFR behaviours, including the operating modes, waveform parameters, scheduling schemes, etc. Due to the variety and complexity of MFR waveforms, the existing approaches have the drawbacks of inefficiency and weak practicability in prediction. A novel method for MFR behaviour recognition and prediction is proposed based on predictive state representation (PSR). With the proposed approach, operating modes of MFR are recognized by accumulating the predictive states, instead of using fixed transition probabilities that are unavailable in the battlefield. It helps to reduce the dependence of MFR on prior information. And MFR signals can be quickly predicted by iteratively using the predicted observation, avoiding the very large computation brought by the uncertainty of future observations. Simulations with a hypothetical MFR signal sequence in a typical scenario are presented, showing that the proposed methods perform well and efficiently, which attests to their validity.

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Section: Psr-based Framework Of Mfrmentioning

confidence: 99%

“…In [31], we constructed the framework of the PSR-based MFR model, which significantly outperforms HMM in radar operating mode recognition. However, it is also based on a grid-filter and cannot be used for predicting MFR signals.…”

Section: Introductionmentioning

confidence: 99%

Novel Approach for the Recognition and Prediction of Multi-Function Radar Behaviours Based on Predictive State Representations

Ou¹,

Yong-guang²,

Zhao³

et al. 2017

Sensors

Self Cite

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“…However, the whole radar model relies heavily on the prior knowledge of MFRs and requires a large amount of radar signal data for parameter estimation. References [12,13] use target modelling [14] instead of radar modelling to identify the functional states of MFRs. They infer radar states only from the observed radar signals.…”

Section: Introductionmentioning

confidence: 99%

Method for functional state recognition of multifunction radars based on recurrent neural networks

Xu¹,

Bi²,

Pan³

2021

IET Radar Sonar & Navi

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Radar signal recognition plays a vital role in electronic warfare. For the multifunction radars (MFRs) with complex dynamical modes, the signal recognition needs to identify not only the emitter but also its current functional state. Existing research on MFR recognition mainly focuses on hierarchical modelling approaches. Inspired by recent progress of deep neural networks, the authors propose to further develop radar signal modelling with recurrent neural networks. Here, the authors propose a more efficient method for functional state recognition of MFRs based on a gated recurrent unit (GRU). The method makes full use of the ability of GRUs to automatically learn the characteristics of radar signal sequences, and the nonlinear modelling of GRUs can deal with the corrupted data well. Simulation results on the Mercury radar show that the proposed method achieves a great recognition accuracy with little prior information, and the designed GRU model outperforms the predictive state representation model under the same condition.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…Thus, a well‐scheduled work mode sequence performing the desired radar task can be described in a syntactic way. The hierarchical model and the syntactic modelling of MFR signals in their study have been widely referred to in various research studies related to MFR work mode [7, 12–21].…”

Section: Introductionmentioning

confidence: 99%

“…After that, many work mode recognition methods were proposed based on the hierarchical model. These methods usually performed the recognition task after a word sequence was extracted from the input pulse stream [15, 16, 18–20]. These two‐stage recognition methods worked well on early MFRs like Mercury and Pluto radars [11].…”

Section: Introductionmentioning

confidence: 99%

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

Zhu

et al. 2020

IET Radar, Sonar & Navigation

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Method for operating mode identification of multi‐function radars based on predictive state representations

Cited by 20 publications

References 17 publications

Novel Approach for the Recognition and Prediction of Multi-Function Radar Behaviours Based on Predictive State Representations

Novel Approach for the Recognition and Prediction of Multi-Function Radar Behaviours Based on Predictive State Representations

Method for functional state recognition of multifunction radars based on recurrent neural networks

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

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