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.

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

Radar signal deinterleaving is an important content of electronic reconnaissance. In this paper, a new radar signal deinterleaving method based on semantic segmentation thought is proposed. We select representative sequence modeling neural network architectures, and input the difference of time of arrival (DTOA) of pulse stream to them. According to the semantics contained in different categories of radar signals, each pulse in the pulse stream is marked according to the category of semantics contained, and radar signals are deinterleaved. Compared with the traditional einterleaving method, this method can adapt to any pulse repetition interval (PRI) modulation mode and does not require PRI periodicity. Compared with other deinterleaving methods using neural network, this method does not need to digitize the data and train a network for each type of target. This method also eliminates the need to iterate the input and output of data. The proposed method has high robustness under the condition of pulse loss and noise pulses. The research also shows that recurrent neural network (RNN) still has more advantages than convolutional neural network (CNN) in this sequence modeling problem.