Based on the simplified model of the tunable fiber laser system, the tuning performance of the laser was analyzed. Two kinds of tunable setups were established, which are the configurations with an external cavity and the configuration of the Littrow cavity. The tuning output characteristics experimentally were analyzed by means of setups. The simulation gives the output efficiency of two tunable lasers as 40% and 30%. In the experiment, the measured slope efficiency of the two lasers was 24% and 18.3%, and the tunable range of the two lasers was 32 nm and 40 nm, respectively. Both lasers could achieve laser output with good beam quality.

Wideband signal detection and recognition (WSDR) is considered an effective technical means for monitoring and analyzing spectra. The mainstream technical route involves constructing time–frequency representations for wideband sampled signals and then achieving signal detection and recognition through deep learning-based object detection models. However, existing methods exhibit insufficient attention on the prior information contained in the time–frequency domain and the structural features of signals, leaving ample room for further exploration and optimization. In this paper, we propose a novel model called TFDP-SANet for the WSDR task, which is based on time–frequency distribution priors and structure-aware adaptivity. Initially, considering the horizontal directionality and banded structure characteristics of the signal in the time–frequency representation, we introduce both the Strip Pooling Module (SPM) and Coordinate Attention (CA) mechanism during the feature extraction and fusion stages. These components enable the model to aggregate long-distance dependencies along horizontal and vertical directions, mitigate noise interference outside local windows, and enhance focus on the spatial distributions and shape characteristics of signals. Furthermore, we adopt an adaptive elliptical Gaussian encoding strategy to generate heatmaps, which enhances the adaptability of the effective guidance region for center-point localization to the target shape. During inference, we design a Time–Frequency Clustering Optimizer (TFCO) that leverages prior information to adjust the class of predicted bounding boxes, further improving accuracy. We conduct a series of ablation experiments and comparative experiments on the WidebandSig53 (WBSig53) dataset, and the results demonstrate that our proposed method outperforms existing approaches on most metrics.

In open, noncooperative electromagnetic environments, prior information about target signals, such as their modulation types and symbol rates, is often difficult to obtain. Consequently, both conventional methods and explicitly label-dependent jamming waveform generation methods face substantial limitations in practical applications. To address this issue, we propose a Multiscale Aggregation Generative Adversarial Network with Masked Pre-training (MA-GAN), a communication jamming waveform generation method based on masked self-supervised learning and multiscale feature fusion. We construct a masked waveform modeling pre-training framework for In-phase/Quadrature (I/Q) time-series signals, through which the Masked Waveform Modeling encoder (MWM-encoder) learns hierarchical and multiscale representations from unlabeled data. The generator is guided by multiscale statistical features and combined with a multi-head multiscale discrimination mechanism and physical constraints to improve the structural consistency between the generated jamming waveforms and the target signals. Experimental results show that, under Additive White Gaussian Noise (AWGN), Rayleigh fading and Rician fading channels and across different Signal-to-Noise Ratio (SNR) and Jamming-to-Signal power Ratio (JSR) conditions, MA-GAN achieves effective jamming performance for five modulation types observed during training and five unseen modulation types, with performance approaching that of the Signal-Like method, which assumes complete prior information. These results indicate that MA-GAN can generate communication jamming waveforms with a certain degree of cross-modulation and robustness to the tested channel conditions without relying on explicit prior information.