Deep Learning for Fingerprint Localization in Indoor and Outdoor Environments

Abstract

Wi-Fi and magnetic field fingerprinting-based localization have gained increased attention owing to their satisfactory accuracy and global availability. The common signal-based fingerprint localization deteriorates due to well-known signal fluctuations. In this paper, we proposed a Wi-Fi and magnetic field-based localization system based on deep learning. Owing to the low discernibility of magnetic field strength (MFS) in large areas, the unsupervised learning density peak clustering algorithm based on the comparison distance (CDPC) algorithm is first used to pick up several center points of MFS as the geotagged features to assist localization. Considering the state-of-the-art application of deep learning in image classification, we design a location fingerprint image using Wi-Fi and magnetic field fingerprints for localization. Localization is casted in a proposed deep residual network (Resnet) that is capable of learning key features from a massive fingerprint image database. To further enhance localization accuracy, by leveraging the prior information of the pre-trained Resnet coarse localizer, an MLP-based transfer learning fine localizer is introduced to fine-tune the coarse localizer. Additionally, we dynamically adjusted the learning rate (LR) and adopted several data enhancement methods to increase the robustness of our localization system. Experimental results show that the proposed system leads to satisfactory localization performance both in indoor and outdoor environments.

Deep Learning-Based Geomagnetic Navigation Method Integrated with Dead Reckoning

“…Bhattarai et al [45] utilized deep recurrent neural networks (DRNNs) to learn the spatial/temporal geomagnetic patterns and capture long-range dependencies in variable geomagnetic input lengths. Li et al [46] aimed to achieve localization by implementing a deep residual network (ResNet) to learn the key features from a massive fingerprint image database. Ding et al [47] designed a sliding window mechanism to expand the dimension of geomagnetic data and utilized a onedimensional convolutional neural network and LSTM network to realize localization.…”

Implementing Deep Learning Techniques in 5G IoT Networks for 3D Indoor Positioning: DELTA (DeEp Learning-Based Co-operaTive Architecture)

“…In the fingerprint-based approach, deep learning techniques have been widely used to extract common patterns from a sparse radiomap database and to improve localization. In recent years, it has gained a huge popularity among the indoor localization researchers, in particular, due to its robustness and high accuracy [ 24 ]. Supervised and unsupervised deep learning algorithms have been recently implemented in 2D localization [ 25 ] and multi-floor localization [ 26 ].…”

Predicting Clinical Outcome in Acute Ischemic Stroke Using Parallel Multi-Parametric Feature Embedded Siamese Network

“…In turn, they can solve recognition, regression, semi-supervised and unsupervised problems [42][43][44]. Deep learning has proven its efficacy in medical imaging like in many other domains such as self-driving cars, natural language and image processing, predictive forecasting, eye tracking systems, object detection in space, finger print localization systems [45][46][47][48][49]. Vgg16 is one of the deep learning models [50] that is a successful feature extractor in multiple domains having lots of image data.…”