Wi-Fi and magnetic field fingerprinting have been a hot topic in indoor positioning researches because of their ubiquity and location-related features. Wi-Fi signals can provide rough initial positions, and magnetic fields can further improve the positioning accuracies, therefore many researchers have tried to combine the two signals for high accuracy indoor localization. Currently, state-of-the-art solutions design separate algorithms to process different indoor signals. Outputs of these algorithms are generally used as inputs of data fusion strategies. These methods rely on computationally expensive particle filters, laborintensive feature analysis, and time-consuming parameter tuning to achieve better accuracies. Besides, particle filters need to estimate the moving directions of particles, limiting smartphone orientation to be stable and aligned with the user’s moving directions. In this work, we adopted a convolutional neural network (CNN) to implement an accurate and orientation-free positioning system. Inspired by the state-of-the-art image classification methods, we design a novel hybrid location image using Wi-Fi and magnetic field fingerprints, then a CNN is employed to classify the locations of the fingerprint images. In order to prevent the overfitting problem of the positioning CNN on limited training datasets, we also propose to divide the learning process into two steps to adopt proper learning strategies for different network branches. We show that the CNN solution is able to automatically learn location patterns, thus significantly lower the work-force burden of designing a localization system. Our experimental results convincingly reveal that the proposed positioning method achieves an accuracy of about 1 meter under different smartphone orientations, users, and use patterns.

Smartphone based indoor positioning has greatly helped people in finding their positions in complex and unfamiliar buildings. One popular positioning method is by utilizing indoor magnetic field, because this feature is stable and infrastructure-free. In this method, the magnetometer embedded on the smartphone measures indoor magnetic field and queries its position. However, the environments of the magnetometer are rather harsh. This harshness mainly consists of coarse-grained hard/soft-iron calibrations and sensor electronic noise. The two kinds of interferences decrease the position distinguishability of the magnetic field. Therefore, it is important to extract location features from magnetic fields to reduce these interferences. This paper analyzes the main interference sources of the magnetometer embedded on the smartphone. In addition, we present a feature distinguishability measurement technique to evaluate the performance of different feature extraction methods. Experiments revealed that selected fingerprints will improve position distinguishability.

With the increasing demand for location-based services, Wi-Fi-based indoor positioning technology has attracted much attention in recent years because of its ubiquitous deployment and low cost. Considering that Wi-Fi signals fluctuate greatly with time, extracting robust features of Wi-Fi signals is the key point to maintaining good positioning accuracy. To handle the dynamic fluctuation with time and sparsity of Wi-Fi signals, we propose an SDAE (Stacked Denoising Autoencoder)-based feature extraction method, which can obtain a robust and time-independent Wi-Fi fingerprint by learning the reconstruction distribution from a raw Wi-Fi signal and an artificial-noise-added Wi-Fi signal. We also leverage the strong representation ability of MLP (Multi-Layer Perceptron) to build a regression model, which maps the extracted features to the corresponding location. To fully evaluate the performance of our proposed algorithm, three datasets are applied, which represent three different scenarios, namely, spacious area with time interval, no time interval, and complex area with large time interval. The experimental results confirm the validity of our proposed SDAE-based feature extraction method, which can accurately reflect Wi-Fi signals in corresponding locations. Compared with other regression models, our proposed regression model can better map the extracted features to the target position. The average positioning error of our proposed algorithm is 4.24 m when there is a 52-day interval between training dataset and testing dataset. That confirms that the proposed algorithm outperforms other state-of-the-art positioning algorithms when there is a large time interval between training dataset and testing dataset.

The IEEE 802.11mc-2016 protocol enables certified devices to obtain precise ranging information using time-of-flight based techniques. The ranging error increases in indoor environments due to the multipath effect. Traditional methods utilize only the ranging measurements of the current location, thus limiting the abilities to reduce the influence of multi-path problems. This paper introduces a robust positioning method that leverages the constraints of multiple positioning nodes at different positions. We transfer a sequence of temporal ranging measurements into multiple virtual positioning clients in the spatial domain by considering their spatial constraints. Defining an objective function and the spatial constraints of the virtual positioning clients as Karush-Kuhn-Tucker conditions, we solve the positioning estimation with non-convex optimization. We propose an iterative weight estimation method for the time of flight ranging and the virtual positioning client to optimize the positioning model. An extensive experimental campaign demonstrates that our proposal is able to remarkably improve the positioning accuracy in complex indoor environments.

The traditional wireless signals used for positioning, such as Wi-Fi and Bluetooth, are not stable enough for accurate indoor positioning due to the wireless signal multipath and time-varying effect. Compared with the wireless signals, the geomagnetic field signals inside buildings are influenced by ferromagnetic materials, which are significantly more stable for accurate indoor positioning. However, due to the location ambiguity problem, different positions may have similar geomagnetic readings, leading to significant positioning errors. Existing indoor geomagnetic positioning methods generally rely on single or short-sequence geomagnetic observations, which makes it difficult to discriminate between positions with similar geomagnetic values. Besides, geomagnetic anomalies can provide accurate position estimation within small-scale areas, but they cannot be utilized for large-scale localization due to the extensive existence of contour points and their rarity of remarkable geomagnetic anomalies. Therefore, we presents a geomagnetic indoor positioning algorithm based on two-level hierarchical LSTM (HLSTM) neural networks, which significantly increases the positioning accuracy by incorporating more historical geomagnetic observations. Based on the finding that geomagnetic signals are much stable, we adopt a sequence augmentation approach to generate a large number of geomagnetic trajectories for model training and testing instead of labor-extensive human collection. The HLSTM model is trained by the Pytorch framework with Cuda and cuDNN for parallel. Our proposed algorithm can obtain around 0.8m error of 67% probability on the self-collected ICT dataset and public MagPIE dataset. The results of the comparison experiment demonstrate that our proposed algorithm performs better accuracy and robustness when compared with other algorithms.

With the widespread development of location-based services, the demand for accurate indoor positioning is getting more and more urgent. Floor positioning, as a prerequisite for indoor positioning in multi-story buildings, is particularly important. Though lots of work has been done on floor positioning, the existing studies on floor positioning in complex multi-story buildings with large hollow areas through multiple floors still cannot meet the application requirements because of low accuracy and robustness. To obtain accurate and robust floor estimation in complex multi-story buildings, we propose a novel floor positioning method, which combines the Wi-Fi based floor positioning (BWFP), the barometric pressure-based floor positioning (BPFP) with HMM and the XGBoost based user motion detection. Extensive experiments show that using our proposed method can achieve 99.2% accuracy, which outperforms other state-of-the-art floor estimation methods.