Signal-to-noise ratio (SNR) estimation plays a crucial role in communication systems, directly impacting the quality and reliability of signal transmission. This paper proposes a novel deep learning framework aimed at enhancing the accuracy and robustness of SNR estimation. The framework converts received signals into time–frequency matrices as feature inputs, effectively capturing both temporal and spectral characteristics through time–frequency analysis. Extensive experimental results across an SNR range of −5 dB to 15 dB demonstrate that our method achieves a mean squared error (MSE) that closely approaches the theoretical Cramér–Rao bound (CRB), comparable to data-aided (DA) maximum likelihood methods. A quantitative analysis reveals that, even under challenging conditions, such as a low SNR of −5 dB, the model maintains superior accuracy with a mean absolute error (MAE) as low as 0.352, significantly outperforming traditional M2M4 and NDA estimators. The model’s performance was systematically evaluated in a wide range of scenarios, encompassing various signal modulation formats, upsampling factors, multipath fading channels, frequency offsets, phase shifts, and roll-off factors. The evaluation highlights its exceptional generalization capability and robustness, with high performance and stability maintained even in challenging and dynamic environments.

Active signal-source seeking by swarms of unmanned aerial vehicles (UAVs) requires reliable waypoint decisions under measurement corruption, misleading response peaks, and geometric constraints. We propose MI-PSO-Adaptive, a high-level waypoint planning framework based on particle swarm optimization (PSO). The framework combines repeated sampling with robust aggregation, adaptive exploration with archive-based social target selection, candidate waypoint guidance, and geometric constraint repair. We evaluated the framework in a three-dimensional simulation using a five-sample measurement budget, waypoint limits, obstacles, and reproducible random seed protocols. In the default unimodal Gaussian field, MI-PSO-Adaptive achieved a mean localization error of 35.36 m and a success rate of 94%; model-matched Gaussian estimation baselines remained more accurate. In the fixed Gaussian mixture field, it achieved a mean localization error of 54.03 m and a success rate of 66%. In contrast, the other evaluated optimization methods and unimodal-model estimation baselines failed to reach the target region. Under 5% outlier contamination, robust aggregation achieved a mean localization error of 157.80 m and a success rate of 52%, outperforming arithmetic mean MI-PSO, although the error distribution remained heavy tailed. Functional ablation showed that adaptive exploration with social target selection helped the swarm escape misleading interference peaks, whereas candidate waypoint guidance further reduced localization errors after effective exploration. The method is therefore intended for discrete high-level waypoint planning under the tested model-mismatch conditions rather than serving as a general replacement for model-based estimation or a guarantee of low-level flight feasibility.