JMRPE‐Net: Joint modulation recognition and parameter estimation of cognitive radar signals with a deep multitask network

Abstract The newly developed cognitive radar (CR) can implement flexible work modes defined with a set of mode definition parameters. Each definition parameter can employ its modulation type and corresponding optimised modulating values. Automatic recognition and analysis of CR work mode are significant challenges for electromagnetic reconnaissance applications. In this article, a deep multitask neural network is proposed for Joint automatic Modulation Recognition and modulation Parameter Estimation (JMRPE‐Net) for the emerging task of CR signals analysis. The proposed JMRPE‐Net consists of a fork‐shaped architecture in which three cascaded convolutional layers act as a shared module for the extraction of common features followed by multiple branches of long short‐term memory layers with the attention mechanism for task‐specific features extraction. The proposed network can receive a sequence of CR pulse signals as input and parallelly perform automatic modulation recognition (AMR) and modulation parameter estimation tasks for multiple work mode definition parameters. Extensive experiments are conducted based on simulated radar intermediate frequency signals with consideration of imperfections of real‐world electromagnetic environments. The experimental results validate the superiority of the proposed JMRPE‐Net against the existing state‐of‐the‐art single task AMR methods or parameter estimation methods.

Abstract licence: CC BY-ND

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