arXiv subjects

Huang Zhitao

Publications and source records attributed to Huang Zhitao.

2 recordsLinked to original sources

arXiv2022

A New Radar Signal Multiparameter-Based Deinterleaving Method

Radar signal deinterleaving has been extensively and thoroughly investigated in the electronic reconnaissance field. In this work, a new radar signal multiparameter-based deinterleaving method is proposed. In this method, semantic information composed of the pulse repetition interval (PRI), pulse width (PW), radio frequency (RF), and pulse amplitude (PA) of a radar signal is used to deinterleave radar signals. A bidirectional gated recurrent unit (BGRU) is employed, and the difference of time of arrival (DTOA)/RF, DTOA/PW, and DTOA/PA of the pulse stream are input into the BGRU. Based on the semantic information contained in different radar signal types, each pulse in the obtained pulse stream is classified according to the semantic information category, and the radar signals are deinterleaved. Compared to the PRI-based deinterleaving methods, the proposed method utilizes the multidimensional information of radar signals. As a result, higher deinterleaving accuracy is achieved. Compared to other existing radar signal multiparameter-based deinterleaving methods, the proposed method can adapt to radar signals with complex parameter features as well as to complex signal environments, and can complete the use of multiparameter in one step.

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arXiv2022

A Radar Signal Deinterleaving Method Based on Semantic Segmentation with Neural Network

Radar signal deinterleaving is an important part of electronic reconnaissance. This study proposes a new radar signal deinterleaving method based on semantic segmentation, which we call "semantic segmentation deinterleaving" (SSD). We select representative sequence modeling neural network (NN) architectures and input the difference of time of arrival of the pulse stream into them. According to semantics contained in different radar signal types, each pulse in the pulse stream is marked according to the category of semantics contained, and radar signals are deinterleaved. Compared to the traditional deinterleaving method, the SSD method can adapt to complex pulse repetition interval (PRI) modulation environments without searching the PRI or PRI period. Multiple rounds of search and merging operation are not required for radar signals with multiple pulses in a period. Compared to other deinterleaving methods based on NNs, the SSD method does not need to digitize the data and train a network for each target type. The SSD method also does not need to iterate input and output data. The proposed method has high robustness to pulse loss and noise pulses. This research also shows that recurrent NNs still have more advantages than convolutional NNs in this sequence modeling task.

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