Automatic modulation classification (AMC) can generally be divided into knowledge-based
methods and data-driven methods. In this paper, we explore combining the knowledge …

Intelligent signal processing for wireless communications is a vital task in modern wireless
systems, but it faces new challenges because of network heterogeneity, diverse service …

Orthogonal frequency-division multiplexing (OFDM) is commonly deployed in Internet of
Things (IoT) systems to achieve high data rates with reasonable complexity, where …

To improve the accuracy of modulated signal recognition in variable environments and
reduce the impact of factors such as lack of prior knowledge on recognition results …

Multi‐passive‐sensor systems are a common means for the target tracking and their
bearings processing is a prerequisite for stable control and nonlinear filtering. This study …

Most of the existing automatic modulation recognition (AMR) studies focus on optimizing the
network structure to improve performance, without fully considering cooperation among the …

In this letter, we consider the problem of dynamic state estimation (DSE) in scenarios where
sensor measurements are corrupted with outliers. For such situations, we propose a filter …

Automatic composite-modulation classification (ACMC) is deemed to be an essential and
important cognitive mechanism adopted for the next generation intelligent telemetry …

To address the problem that model uncertainty and unknown time-varying system noise
hinder the filtering accuracy of the autonomous navigation system of satellite constellation …

Automatic modulation classification (AMC) is a method that supported different wireless
communication systems for modulation type classification. Currently, orthogonal frequency …