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eess.SP updates on arXiv.org

ECG-biometrics-bench: A Unified Framework for Reproducible Benchmarking of ECG Biometrics Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning Towards Improving Speaker Distance Estimation through Generative Impulse Response Augmentation Federated Learning with Hypergradient-based Online Update of Aggregation Weights Soft Graph Diffusion Transformer for MIMO Detection SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting Sequential Inference for Gaussian Processes: A Signal Processing Perspective Statistical Channel Fingerprint Construction for Massive MIMO: A Unified Tensor Learning Framework Recent Advances in mm-Wave and Sub-THz/THz Oscillators for FutureG Technologies Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods Super-resolution Multi-signal Direction-of-Arrival Estimation by Hankel-structured Sensing and Decomposition Hankel and Toeplitz Rank-1 Decomposition of Arbitrary Matrices with Applications to Signal Direction-of-Arrival Estimation Adaptive Transform Coding for Semantic Compression EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures Sparse Graph Learning from Sparse Data via Fiedler Number Maximization A Deep Learning Model for Battery State Prediction towards Intelligent Energy Management Transfer Learning for Tonal Noise Prediction in VRF Units Using Thermodynamic and Vibration Signals EVT-Based Generative AI for Tail-Aware Channel Estimation Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing BandRouteNet: An Adaptive Band Routing Neural Network for EEG Artifact Removal Phase-Separated Complex Hilbert PCA on Markerless 3D Pose Estimation Data: A Global Phase Network and Its Extension to a Continuous Field on the Body Surface Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation Deep Learning-Enabled Dissolved Oxygen Sensing in Biofouling Environments for Ocean Monitoring Speech Enhancement Based on Drifting Models Robust and Clinically Reliable EEG Biomarkers: A Cross Population Framework for Generalizable Parkinson's Disease Detection An AI-Based Supervisory Measurement Integrity Validation Layer for Cyber-Resilient AC/DC Protection in Inverter-Based Microgrids Explainable AI in Speaker Recognition -- Making Latent Representations Understandable Time-Localized Parametric Decomposition of Respiratory Airflow for Sub-Breath Analysis NAKUL-Med: Spectral-Graph State Space Models with Dynamics Kernels for Medical Signals An Algorithm for On-Sensor Agnostic Detection of Changes in Human Activity for Ultra-Low-Power Applications
Backscatter Assisted Indoor NLOS Positioning
Kalle Ruttik, Hüseyin Yiğitler, Jingyi Liao, Alexander Sheverdya · 2026-06-16 · via eess.SP updates on arXiv.org

Passive backscatter devices (BDs) can enable indoor non-line-of-sight (NLOS) positioning by serving as virtual anchors whose Doppler-separated signatures are observable in standard channel estimates. This paper studies continuous user-equipment (UE) tracking in corridor environments using a noncoherent power-domain formulation that avoids BD phase synchronization and remains robust to residual carrier offsets and strong multipath. The BD-dependent measurements are modeled by a log-distance law with unknown BD-specific offsets, which allows passive asynchronous devices to be used as anchors without transmit-power calibration. Based on this model, we develop a corridor-constrained maximum a posteriori (MAP) tracker with motion regularization and Huber-robust estimation. In ray-tracing-inspired simulations, the method achieves median positioning errors of 0.23--0.27 m with 90th-percentile errors below 0.45 m. In office-corridor measurements with four passive BDs at 866 MHz, it attains an aggregated median error of 0.505 m and outperforms a simple weighted-average baseline. The results show that passive asynchronous BDs can provide practical sub-meter indoor NLOS tracking while remaining compatible with existing channel-estimation pipelines and energy-autonomous BD deployments.