Deinterleaving of Pulse Streams With Conditional Autoregressive Kernel Mixture Network

Abstract

Deinterleaving emitters with complex patterns presents a significant challenge for electronic support measure systems. In this article, we address this issue by formulating deinterleaving as the optimization of an autoregressive likelihood function. We then propose the conditional autoregressive kernel mixture network, a conditional generative model that estimates the conditional density of pulse parameters based on previous noisy observations and source labels to estimate the solution of this optimization problem. The model, trained with a modified loss function for denoising, can extract pulses belonging to the class of a given source label. Therefore, the denoising-based deinterleaving is achieved with a single model. We evaluate our model with the proposed algorithms on a challenging synthetic dataset under various nonideal conditions and compare it against existing approaches for both conventional and open-set deinterleaving. The results indicate that our method significantly outperforms the comparative techniques, especially in open-set deinterleaving.