Parametric model‐based deinterleaving of radar signals with non‐ideal observations via maximum likelihood solution - Haiyu Wang; Mengtao Zhu; Ruozhou Fan; Yan Li (IET Radar, Sonar & Navigation, 2022)
Abstract In modern electromagnetic environments, as the number of radars continuously increasing, the intercepted interleaving signals become more complex and are easily affected by different non‐ideal factors. However, the widely used deinterleaving methods based on the pulse repetition interval (PRI) are difficult to effectively deinterleave signals with complex PRI modulation and are very sensitive to the received signal quality. To solve the deinterleaving problem in these complex situations, this paper proposes a parametric model‐based deinterleaving solution consisting of two cascaded stages. The proposed method does not require priors such as the number of sources and corresponding modulation types, which makes it more suitable for complex environments in non‐cooperative scenarios. In addition, it can adapt to complex non‐ideal situations, including parameter measurement noise, missing pulse and spurious pulse conditions. Simulation results show that the deinterleaving precision of the proposed method outperforms baseline methods in non‐ideal conditions and achieves 99.33% accuracy.