Received signal strength (RSS)-based indoor localization has attracted increasing attention due to its low cost and compatibility with existing wireless infrastructures. However, RSS measurements are highly sensitive to environmental variations, making it challenging for deep learning-based localization models to generalize across different physical configurations. Driven by these challenges, this paper proposes a scenario-adaptive RSS localization framework based on backbone reuse and efficient adaptation. The proposed architecture consists of a lightweight extractor and a shared backbone, where the extractor projects heterogeneous RSS inputs into a unified feature space and the backbone captures transferable localization knowledge. During adaptation, the backbone trained from the source dataset is reused, while the extractor is optimized to adapt to the new dataset configuration. A short optimization stage is further introduced to slightly refine the backbone with a lower learning rate. Experimental results on four datasets demonstrate that the proposed training strategy enables faster convergence and improved adaptation compared with training from scratch. In addition, the proposed framework reduces training time under different dataset configurations, verifying its effectiveness and efficiency for adaptive RSS-based indoor localization.
In this work, an end-to-end generative adversarial framework for computational microwave imaging (CMI) is proposed to reconstruct the targets of interest directly from the measurements of targets obstructed by undesired objects. It integrates a conditional generative adversarial network (cGAN) with a learnable soft-threshold module (STM) to adaptively suppress non-target related information. The proposed framework is evaluated on a diverse dataset comprising MNIST digits obstructed by E-MNIST letters for training and testing, as well as on measurements acquired with an experimental CMI system. In addition, further studies involving objects with different geometries are conducted, demonstrating that the proposed approach can be adapted to other types of objects. Numerical experiments show that the proposed cGAN-STM achieves a normalized mean square error (NMSE) of 0.066 and a structural similarity index (SSIM) of 0.876 under ideal conditions. Comprehensive analyses, including benchmarking, analysis of the STM mechanism, and evaluation under different obstruction sizes, are also conducted. The performance of the model under different signal-to-noise ratio (SNR) scenarios is also evaluated, achieving reasonable reconstruction quality at 15 dB SNR with an NMSE of 0.168 and an SSIM of 0.700. Even at low SNR levels, recognizable target outlines are preserved. These results highlight the effectiveness and adaptability of the proposed method.
Jiaming Zhang, María García-Fernández, G. Álvarez-Narciandi et al.· 0 citations
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