Search Results (271)

WiFi sensing has gained widespread attention as a promising technology, owing to its non-intrusiveness, strong privacy-preserving characteristics, and cost-effective deployment, enabling diverse application scenarios. In addition, the stable spatial characteristics and symmetry-related patterns exhibited by human body postures in WiFi signal propagation provide new possibilities for robust person identification. In traditional WiFi-based person identification technologies, although gait recognition has achieved certain success, it is complex to operate and limited in application scenarios, increasing the constraints on recognition. This issue becomes more pronounced in large-scale user scenarios, where the system performance tends to degrade and exhibit instability. To overcome these challenges, we introduce a new person identification system called WiPID. The WiFi signals extracted from the static postures of users are treated as a “biometric fingerprint” for identity verification. An end-to-end deep learning framework is utilized by WiPID to process WiFi signals, and a convolutional autoencoder is adopted to preprocess the signals directly, effectively reducing redundant information and greatly simplifying the WiFi data processing. Furthermore, the integration of a multi-scale feature extraction module improves the system’s ability to capture discriminative features. The proposed system not only reduces operational complexity but also extends its applicability to a wider range of scenarios, thereby enhancing recognition performance. In an experiment involving 50 volunteers, WiPID achieved an average recognition accuracy of up to 98%, demonstrating the method’s suitability for large-scale person identification scenarios. In addition, a real-time identification experiment has been conducted on PCs and commercial WiFi devices. Experiments have proven that WiPID can achieve real-time person identification on Internet of Things devices, further validating its feasibility and stability in practical applications. Full article

Indoor localization in GNSS-denied environments remains a significant challenge due to the low sampling frequency and high variability of wireless signal measurements. This paper presents a wireless communication-based indoor localization method that integrates Wi-Fi received signal strength indication (RSSI) measurements with optical initialization and inertial sensor fusion. The proposed approach eliminates the need for labor-intensive fingerprinting and specialized infrastructure by leveraging existing Wi-Fi networks. Optical pose estimation using ArUco markers provides accurate initial position and orientation, enabling alignment between sensor coordinate systems and reducing inertial drift. During tracking, inertial measurements compensate for motion between sparse Wi-Fi observations by virtually translating historical RSSI samples, allowing statistically consistent averaging and improved distance estimation. A simplified factor graph framework is employed to fuse heterogeneous measurements while maintaining computational efficiency suitable for real-time operation on mobile devices. Experimental validation using a robot-based ground-truth reference system demonstrates sub-meter localization accuracy with an average positioning error of approximately 0.40 m. The proposed method provides a low-cost and scalable solution for indoor positioning and navigation applications such as access-controlled environments, exhibitions, and large public venues. Full article

WiFi fingerprinting remains attractive for indoor localization because it reuses existing wireless infrastructure, yet RSSI fingerprints are high-dimensional, sparse, and often ambiguous across adjacent floors and building regions. This study develops a hybrid blended localization framework that combines multi-task learning with feature-space weighted k-nearest-neighbor refinement. A shared neural encoder predicts building labels, floor labels, and normalized coordinates from 520-dimensional WiFi fingerprints, and the learned embedding space is then used for semantically constrained WKNN correction. The final model is trained with AdamW, a learning rate of $8\times {10}^{-4}$, batch size 512, and a joint loss over building classification, floor classification, and coordinate regression, without a learning-rate scheduler. Experiments on a public WiFi fingerprint dataset show that the hybrid model achieves the strongest overall localization robustness among the evaluated non-ensemble methods. On the official validation split, it obtains a mean localization error of 9.01, a median error of 6.25, and an RMSE of 12.95 in the dataset coordinate units. On the internal semantic validation split, it reaches 94.81% floor classification accuracy and 97.62% building classification accuracy. Floor-wise and building–floor analyses further show that the largest errors are concentrated in a small number of difficult semantic regions, especially the highest floor and sparsely constrained partitions. Full article

Localization and navigation techniques have become fundamental for modern lives, while achieving accurate results indoors still remains a significant challenge. The widespread adoption of smart devices, and especially smartphones, has increased the need for accurate and robust pedestrian dead reckoning systems that operate in infrastructure-less environments. Pedestrian dead reckoning’s primary challenge is maintaining accuracy despite varying smartphone placements (attitudes) and the noisy, low-cost inertial measurements units. In this work, a comprehensive pedestrian dead reckoning framework is presented that integrates advanced step counting and heading estimation techniques. For step detection and counting, we propose a robust step counting algorithm that utilizes the optimum fusion of the raw IMU readings, i.e., accelerometer, linear accelerometer, gyroscope, and magnetometer readings, each broken down into three degrees of freedom for different body placements and walking speeds. Furthermore, to address the critical issue of heading estimation, we propose the heading estimation axis mapping (HEAT-MAP) algorithm, which dynamically adjusts the sensor axes in response to the smartphone’s orientation, ensuring a consistent coordinate frame and reducing heading drift. Moreover, to eliminate cumulative pedestrian dead reckoning errors, the system incorporates an adaptive weighted fusion mechanism with Wi-Fi fingerprinting. Experimental results demonstrate that this integrated system significantly improves the overall trajectory accuracy, providing a high-precision, attitude-unconstrained solution for real-time indoor pedestrian navigation. Full article

Radio frequency fingerprints (RFFs), arising from inherent hardware imperfections, serve as distinctive features for device identification. The location- and time-dependent nature of the wireless channel directly affects RFF-based device identification, making it challenging under different channel conditions. This is primarily because the training and test datasets containing RFFs may not overlap within the same feature-space domain. In this work, the mentioned issue is addressed as a domain adaptation problem. For this objective, we propose the use of a triplet-learning-based domain-adversarial neural network within a hybrid framework named TripletDANN. We leverage the triplet loss, enabling the network to focus exclusively on device-specific latent representations under different channel conditions, while employing an adversarial loss to prevent the network from exploiting channel-specific characteristics. With this aim, data aggregation is performed together with channel labeling. The generalization capability of TripletDANN is evaluated on previously unseen test data collected across different locations under two distinct scenarios. Raw I/Q signals of 15 Wi-Fi devices are used as a case study. The proposed TripletDANN model achieves up to $88.52\%$ average device classification accuracy across the different data collection locations. On average, TripletDANN attains up to a $5\%$ performance improvement over its counterpart model. Moreover, data augmentation is employed to improve the overall performance, and a highest accuracy of $96.71\%$ is achieved on experimentally collected test data from an unseen location. Full article

Indoor positioning in hospitals is challenging because global navigation satellite systems signals are unavailable and existing solutions struggle with complex indoor propagation and high maintenance requirements. Fingerprinting-based methods using Wi-Fi, Bluetooth Low Energy (BLE), or magnetic field depend on extensive site surveys, while time or angle-based systems such as ultra-wide band, angle of arrival, and Wi-Fi round trip time require additional infrastructure. Recent machine learning approaches improve performance but remain limited by Pedestrian Dead Reckoning (PDR) drift and unstable spatial representations. This study proposes an AI-generated spatial pattern matching framework that integrates an AI-based PDR model with BLE Received Signal Strength Indicator (RSSI) to construct a user RSSI surface. Spatial similarity between user-generated patterns and the pre-built radio map is evaluated using Surface Correlation (SC), and a bi-directional candidate generation strategy with SC-based heading correction is employed to mitigate inertial drift. Experiments in a real hospital setting show that the proposed method achieves robust and accurate localization even in complex indoor environments where conventional fingerprinting and PDR techniques often fail. The results indicate that combining AI-driven inertial modeling with SC-based spatial pattern matching offers a practical and infrastructure-friendly solution for hospital indoor positioning. Full article

Indoor localization finds wide-ranging applications in user navigation and intelligent building systems. Nevertheless, signal interference within complex indoor environments and challenges regarding localization generalization in multi-building and multi-floor scenarios have restricted the performance of traditional localization methods based on Wi-Fi fingerprinting. To tackle these issues, this paper presents the SE-MTCAELoc model, a multi-task convolutional autoencoder approach that integrates a squeeze-excitation (SE) attention mechanism for indoor positioning. Firstly, the method preprocesses Wi-Fi Received Signal Strength (RSSI) data. In the UJIIndoorLoc dataset, the 520-dimensional RSSI features are extended to 576 dimensions and reshaped into a 24 × 24 matrix. Meanwhile, Gaussian noise is introduced to enhance the robustness of the data. Subsequently, an integrated SE module combined with a convolutional autoencoder (CAE) is constructed. This module aggregates channel spatial information through squeezing operations and learns channel weights via excitation operations. It dynamically enhances key positioning features and suppresses noise. Finally, a multi-task learning architecture based on the SE-CAE encoder is established to jointly optimize building classification, floor classification, and coordinate regression tasks. Priority balancing is achieved using weighted losses (0.1 for building classification, 0.2 for floor classification, and 0.7 for coordinate regression). Experimental results on the UJIIndoorLoc dataset indicate that the accuracy of building classification reaches 99.57%, the accuracy of floor classification is 98.57%, and the mean absolute error (MAE) for coordinate regression is 5.23 m. Furthermore, the model demonstrates exceptional time efficiency. The cumulative training duration (including SE-CAE pre-training) is merely 9.83 min, with single-sample inference taking only 0.347 milliseconds, fully meeting the requirements of real-time indoor localization applications. On the TUT2018 dataset, the floor classification accuracy attains 98.13%, with an MAE of 6.16 m. These results suggest that the SE-MTCAELoc model can effectively enhance the localization accuracy and generalization ability in complex indoor scenarios and meet the localization requirements of multiple scenarios. Full article

Indoor WiFi fingerprint localization often suffers from abnormal fluctuations in received signal strength indicator (RSSI) measurements and from the sensitivity of least-squares support vector machine (LSSVM) hyperparameters to local optima. To address these issues, this paper presents an improved end-to-end localization method that integrates fingerprint reconstruction with adaptive model optimization. First, a knowledge-enhanced anomaly detection and spatial fingerprint repair (KADSFR) model is used to enhance fingerprint database consistency by combining robust Mahalanobis distance, median absolute deviation, and local outlier factor for anomaly detection, followed by weighted k-nearest neighbors interpolation based on composite signal–physical distances. Then, an adaptive particle swarm optimization (APSO) scheme with stagnation detection and spatial exclusion mechanisms is employed to tune the LSSVM regularization coefficient and RBF kernel width under five-fold cross-validation. Experiments show that KADSFR improves fingerprint quality by approximately 10 percent, and the proposed method achieves an average error of 0.74 m, outperforming KNN, WKNN, LSSVM, and APSO-LSSVM by 63.5 percent, 62.8 percent, 34.5 percent, and 16.9 percent, respectively. Sensitivity analysis further confirms strong robustness and stability. Full article

The use of Artificial Intelligence (AI) algorithms has enhanced WiFi fingerprinting-based indoor localization. However, most existing approaches are limited to 2D coordinate estimation, which leads to significant performance declines in multi-floor environments due to vertical ambiguity and inadequate spatial modeling. This limitation reduces reliability in real-world applications where accurate indoor localization is essential. This study proposes RELoc, a new 3D indoor localization framework that integrates Recursive Feature Elimination with Cross-Validation (RFECV) for optimal Access Point (AP) selection and Extremely Randomized Trees (ERT) for precise 2D and 3D coordinate regression. The ERT hyperparameters are optimized using Bayesian optimization with Optuna’s Tree-structured Parzen Estimator (TPE) to ensure robust, stable, and accurate localization. Extensive evaluation on the SODIndoorLoc and UTSIndoorLoc datasets demonstrates that RELoc delivers superior performance in both 2D and 3D indoor localization. Specifically, RELoc achieves Mean Absolute Errors (MAEs) of 1.84 m and 4.39 m for 2D coordinate prediction on SODIndoorLoc and UTSIndoorLoc, respectively. When floor information is incorporated, RELoc improves by 33.15% and 26.88% over the 2D version on these datasets. Furthermore, RELoc outperforms state-of-the-art methods by 7.52% over Graph Neural Network (GNN) and 12.77% over Deep Neural Network (DNN) on SODIndoorLoc and 40.22% over Extra Tree (ET) on UTSIndoorLoc, showing consistent improvements across various indoor environments. This enhancement emphasizes the critical role of 3D modeling in achieving robust and spatially discriminative indoor localization. Full article

In 2025, there is still no ubiquitous, accurate, infrastructure-free indoor positioning system. Among existing approaches, WiFi-based positioning is highly promising as it leverages existing infrastructure. However, its performance is severely affected by WiFi signal variability and environmental dynamics. Thus, this paper presents a novel approach that combines WiFi Round Trip Time and Received Signal Strength measurements with the Conformal Prediction (CP) framework to achieve robust uncertainty-aware indoor positioning. Our proposed method does not only accurately estimate the user position, but also provides two prediction regions: a rectangular region and a circular region. We systematically evaluate our method across three real-world testbeds, which achieves a positioning accuracy of 0.6 m, while generating prediction regions with theoretical coverage guarantees for circular regions and marginal coverage guarantees for rectangular regions. To the best of our knowledge, this is one of the first work to enable uncertainty quantification on top of state-of-the-art WiFi ranging signals. Full article

Location estimation is significant in this era of the Internet of Things (IoT). Satellite and cellular signals are often blocked indoors, prompting researchers to explore alternative wireless technologies for indoor positioning. Among these, Wi-Fi Received Signal Strength (RSS) with fingerprinting is dominant in large, multi-floor buildings due to its existing infrastructure, acceptable accuracy, low cost, easy deployment, and scalability. This study aims to systematically search and review the literature on the use of real Wi-Fi RSS fingerprints for indoor localization or positioning in large, multi-floor buildings, in accordance with PRISMA guidelines, to identify current trends, performance, and gaps. Our findings highlight three main public datasets in this fields (covering areas over 10,000 sq.m). Recent trends indicate the widespread adoption of Deep Learning (DL) techniques, particularly Convolutional Neural Networks (CNNs) and Stacked Autoencoders (SAEs). While buildings (in the same vicinity) and their respective floors are accurately identified, the maximum average error remains around 7 m. A notable gap is the lack of public datasets with detailed room or zone information. This review intends to serve as a guide for future researchers looking to improve indoor location estimation in large, multi-floor structures such as universities, hospitals, and malls. Full article

In indoor environments, fusion localization methods that combine Wi-Fi fingerprinting and Pedestrian Dead Reckoning (PDR) are constrained by the high sensitivity of traditional filters, such as the Extended Kalman Filter (EKF), to initial states and by their susceptibility to nonlinear drift. This study presents a Wi-Fi/PDR fusion localization approach based on global geometric alignment optimized via a Genetic Algorithm (GA). The proposed method models the PDR trajectory as an integrated geometric entity and performs a global search for the optimal two-dimensional similarity transformation that aligns it with discrete Wi-Fi observations, thereby eliminating dependence on precise initial conditions and mitigating multipath noise. Experiments conducted in a real office environment (14 × 9 m, eight dual-band APs) with a double-L trajectory demonstrate that the proposed GA fusion achieves the lowest mean error of 0.878 m (compared to 2.890 m, 1.277 m, and 1.193 m for Wi-Fi, PDR, and EKF fusion, respectively) and an RMSE of 0.978 m. It also attains the best trajectory fidelity (DTW = 0.390 m, improving by 71.0%, 14.7%, and 27.8%) and the smallest maximum deviation (Hausdorff = 1.904 m, 52.4% lower than Wi-Fi). The cumulative error distribution shows that 90% of GA fusion errors are within 1.5 m, outperforming EKF and PDR. Additional experiments that compare the proposed GA optimizer with Levenberg–Marquardt (LM), particle swarm optimization (PSO), and Procrustes alignment, as well as tests with 30% artificial Wi-Fi outliers, further confirm the robustness of the Huber-based cost and the effectiveness of the global optimization framework. These results indicate that the proposed GA-based fusion method achieves high robustness and accuracy in the tested office-scale scenario and demonstrate its potential as a practical multi-sensor fusion approach for indoor localization. Full article

Accurate indoor floor classification is essential for wireless positioning systems. However, the performance of conventional received signal strength indictor (RSSI)-based fingerprinting approaches is often limited by signal fluctuations and insufficient feature representation. To address these challenges, this paper introduces a heterogeneous graph neural network (GNN) framework that models WiFi signals using two types of nodes: reference points and Media Access Control (MAC) address. The edges between reference points and MAC addresses are weighted by normalized RSSI values, allowing the model to capture signal strength interactions and perform relation-aware message passing. Through this graph-based representation, the model can learn spatial and signal dependencies more effectively than conventional vector-based approaches. The proposed model was extensively evaluated under both benchmark and practical settings. On small-scale datasets, it achieved performance comparable to that of a conventional convolutional neural network trained on large-scale datasets, confirming its effectiveness with limited samples. In addition, the proposed model consistently outperformed other models under noisy conditions, achieving 93.88% accuracy on the widely used UJIIndoorLoc dataset and 97.3% accuracy in real-time experiments conducted at a test site. These values are significantly higher than those achieved using conventional machine learning (ML) baselines, highlighting the ability of the proposed model to handle real-world signal variations. These findings highlight that the heterogeneous GNN effectively captures spatial and signal-level dependencies, offering a robust and scalable solution for accurate indoor floor classification. Overall, this work presents a promising pathway for improving the performance and reliability of future wireless positioning systems. Full article

With the increasing demand for indoor location-based services, magnetic-fingerprint-based positioning has emerged as a promising complementary solution in scenarios lacking WiFi coverage. However, the dynamic nature of indoor environments, architectural complexity, and variations in pedestrian walking speeds can lead to stretching, compression, and distortion of magnetic fingerprint sequences, making it challenging for traditional sequence-matching algorithms to maintain stable positioning performance. To address these challenges, this paper proposes a magnetic-fingerprint-based positioning model that integrates residual networks (ResNet), transformer, and LSTM, referred to as Res-T-LSTM. Within the overall architecture, the ResNet module extracts deep local spatial features of magnetic fingerprints, and its residual connections effectively mitigate gradient attenuation during deep network training. The transformer module leverages self-attention mechanisms to model long-range dependencies and global contextual information, adaptively emphasizing key magnetic variations to enhance the discriminability of the feature representations. The LSTM module further captures the dynamic temporal evolution of magnetic sequences, improving robustness to variations in walking speed and sequence stretching or compression. Experimental results show that the proposed model achieves excellent performance across four smartphone-carrying postures, yielding an average positioning error of 0.21 m. Full article

Android devices employ several methods to calculate their position. This paper’s focus is the Network Location Provider (NLP), which leverages Wi-Fi and cell tower signals via the fingerprinting/database approach. Unlike GNSS-based positioning, the NLP should be able to compute positions even when the device is indoors or experiencing GNSS radio frequency interference (RFI), making it an enticing candidate for ensuring robust PNT solutions. However, the inner workings of NLP are largely undisclosed, remaining as a ‘black-box’ system. Using the Samsung S24 and Xiaomi Redmi K80 Ultra, we explored the NLP’s response to GNSS spoofing and offline operation (no network connection), as well as attempting NLP spoofing. The GNSS spoofing test confirmed that when satellite signals are spoofed, the NLP solution is maintained at the truth location. This reinforces the robustness of the NLP in RFI environments. In offline mode, NLP continued to operate without a Google server connection, indicating that it can compute positions locally using internally stored cache data. This behavior deviates from the conventional understanding of NLP and offers valuable insights into the latest NLP mechanism. These findings build upon previous work to uncover the inner workings of the NLP and ultimately contribute to robust smartphone PNT. Full article