indoor navigation system developers

Abstract: Reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) systems provide directional measurements of received power for indoor localization, but device orientation changes the fingerprints. This work proposes an orientation-aware dual-stream gated (O-DSG) deep learning framework for received signal strength indicator (RSSI)-based indoor localization. An orientation classifier estimates device orientation from RSSI, so inference needs only received power. An embedding of the predicted angle and posterior entropy controls feature modulation and soft expert gating. Parallel convolutional and statistical streams are fused through bidirectional long short-term memory (BiLSTM) processing and temporal attention. Single-user measurements in an indoor laboratory at 28 GHz under non-line-of-sight (NLoS) conditions yield a median localization error of 0.141 m, a 25.8% improvement over the closest evaluated baseline (supplied with the measured orientation), and 92.3% orientation classification accuracy across four discrete classes. In spatial regions withheld from training, O-DSG has an average median error of 0.247 m, against 0.392 m for a validation-selected sequence model. A separate offline evaluation constructs non-orthogonal multiple access (NOMA)-style power mixtures from the single-user measurements and measures localization accuracy and computational scaling with up to eight emulated users whose power contributions overlap, under the specified mixture model.

Abstract: Near-field localization is a promising technique for high-resolution multi-user positioning in future wireless systems, but its performance is often degraded by scattering-induced coherent propagation. Existing near-field localization methods, which require separate parameter estimation and path/source association, suffer from high computation overhead and accumulated errors, and usually do not provide any guarantee on reliability. In this paper, we propose \emph{MUSIC-Net}, an end-to-end near-field positioning deep learning (DL) framework informed by two-stage MUltiple SIgnal Classification (MUSIC) in mixed line-of-sight (LoS) and non-LoS (NLoS) multi-path scenarios, which embeds the two-stage MUSIC objects into training to isolate the LoS-related signal subspace and to identify a surrogate distance. The proposed framework directly recovers multi-user positions without the need for involved NLoS parameter estimation or path/source association. Furthermore, we introduce split conformal prediction (SCP) to move beyond point-estimation-based positioning towards statistically guaranteed (confidence) set estimation for all users. Numerical results show that the proposed MUSIC-Net achieves lower mean positioning error (MPER) than existing benchmarks and yields tighter SCP-calibrated prediction regions, demonstrating both accurate LoS localization and efficient uncertainty quantification (UQ) in coherent multi-path environments.

Abstract: Floorplans are compact, appearance-invariant maps ideal for indoor localization, yet existing methods rely on depth networks that are brittle in cluttered scenes. We propose GALoc, a geometry-first framework that replaces depth prediction with gravity-aligned wireframes that satisfy verticality and coplanarity by construction. Given monocular RGB, camera intrinsics, relative poses, and IMU orientation, GALoc constructs a linear constraint matrix encoding verticality and coplanarity, and finds the camera gauge minimizing its smallest singular value via global search. The rectified wireframes are projected into bird's-eye-view layouts through a closed-form, FOV-consistent transformation and matched against the floorplan via metric-free SE(2) search. We evaluate end-to-end on Structured3D, with calibrated noise on Gibson, and on real-world author-collected sequences. When sufficient wall geometry is visible, GALoc matches or outperforms depth-based baselines -- achieving 88% sequential localization success at 0.1m over 100-step sequences on Gibson vs the baseline's 68% -- while abstaining in structure-blind scenes.