Inertial sensors for smartphones navigation

The advent of smartphones and tablets, means that we can constantly get informa- tion on our current geographical location. These devices include not only GPS/GNSS chipsets but also mass-market inertial platforms that can be used to plan activities, share locations on social networks, and also to perform positioning in indoor and outdoor scenarios. This paper shows the performance of smartphones and their inertial sensors in terms of gaining information about the user’s current geographical loca- tion considering an indoor navigation scenario. Tests were carried out to determine the accuracy and precision obtainable with internal and external sensors. In terms of the attitude and drift estimation with an updating interval equal to 1 s, 2D accuracies of about 15 cm were obtained with the images. Residual benefits were also obtained, however, for large intervals, e.g. 2 and 5 s, where the accuracies decreased to 50 cm and 2.2 m, respectively

Cellular network capacity and coverage enhancement with MDT data and Deep Reinforcement Learning

Recent years witnessed a remarkable increase in the availability of data and computing resources in comm-unication networks. This contributed to the rise of data-driven over model-driven algorithms for network automation. This paper investigates a Minimization of Drive Tests (MDT)-driven Deep Reinforcement Learning (DRL) algorithm to optimize coverage and capacity by tuning antennas tilts on a cluster of cells from TIM's cellular network. We jointly utilize MDT data, electromagnetic simulations, and network Key Performance indicators (KPIs) to define a simulated network environment for the training of a Deep Q-Network (DQN) agent. Some tweaks have been introduced to the classical DQN formulation to improve the agent's sample efficiency, stability and performance. In particular, a custom exploration policy is designed to introduce soft constraints at training time. Results show that the proposed algorithm outperforms baseline approaches like DQN and best-first search in terms of long-term reward and sample efficiency. Our results indicate that MDT -driven approaches constitute a valuable tool for autonomous coverage and capacity optimization of mobile radio networks

Data-driven Predictive Latency for 5G: A Theoretical and Experimental Analysis Using Network Measurements

The advent of novel 5G services and applications with binding latency requirements and guaranteed Quality of Service (QoS) hastened the need to incorporate autonomous and proactive decision-making in network management procedures. The objective of our study is to provide a thorough analysis of predictive latency within 5G networks by utilizing real-world network data that is accessible to mobile network operators (MNOs). In particular, (i) we present an analytical formulation of the user-plane latency as a Hypoexponential distribution, which is validated by means of a comparative analysis with empirical measurements, and (ii) we conduct experimental results of probabilistic regression, anomaly detection, and predictive forecasting leveraging on emerging domains in Machine Learning (ML), such as Bayesian Learning (BL) and Machine Learning on Graphs (GML). We test our predictive framework using data gathered from scenarios of vehicular mobility, dense-urban traffic, and social gathering events. Our results provide valuable insights into the efficacy of predictive algorithms in practical applications

Test of an Assisted GPS Technique for High-Sensitivity Acquisition of Indoor Signals

A major issue of indoor GPS signals is the extremely low signal-to-noise ratio (e.g. C/N0 = 5 dB Hz ÷ 30 dB Hz ) and the consequent difficulty, for the acquisition stage, of identifying "reliable" autocorrelation peaks. Acquisition sensitivity can be increased by extending the coherent in-tegration time, but the maximum achievable performance is bounded, for instance, by the presence of navigation bits that introduce sign reversals within the coherent integration window. This may result in a partial or even total cancella-tion of "true" peaks. The "sensitivity assistance" approach enables High-Sensitivity (HS) acquisition by providing the acquisition engine with approximate code-phase/Doppler-frequency estimates together with fragments of the data bit-stream, to allow for wiping off the bit transitions and extending the coherent integratio

Test of an Assisted GPS Technique for High-Sensitivity Acquisition of Indoor Signals

A major issue of indoor GPS signals is the extremely low signal-to-noise ratio (e.g. C/N0 = 5 dB Hz ÷ 30 dB Hz ) and the consequent difficulty, for the acquisition stage, of identifying “reliable” autocorrelation peaks. Acquisition sensitivity can be increased by extending the coherent in-tegration time, but the maximum achievable performance is bounded, for instance, by the presence of navigation bits that introduce sign reversals within the coherent integration window. This may result in a partial or even total cancella-tion of “true” peaks. The “sensitivity assistance” approach enables High-Sensitivity (HS) acquisition by providing the acquisition engine with approximate code-phase/Doppler-frequency estimates together with fragments of the data bit-stream, to allow for wiping off the bit transitions and extending the coherent integratio

Inertial Sensors Strapdown Approach for Hybrid Cameras and MEMS Positioning

In this paper the performances (in terms of accuracy) of an hybrid positioning technique, that brings together an image based recognition approach and MEMS (Micro Electro-Mechanical Systems) technology, will be investigated. The image recognition based (IRB) positioning has already been well investigated in the past and represents a good technology for navigation in GNSS denied environment, like indoor or urban canyon, where GNSS accuracy is poor (tens of meters accuracy). However, for practical exploitation of IRB positioning with smartphones the following main problems must be taken into account. The first one is the optimization of the battery, that implies a proper use of the frame rate. A second main issue is represented by latencies due to image processing algorithms and visual search solutions that may require a cloud architecture to manage the size of the database. To overcome the above problems in IRB, reduction of the frame rate and latencies compensation, inertial platform built with MEMS technology may be exploited. Two different approaches are taken into account for IRB positioning: the first one is represented by a real-time solution obtainable through single images; the second one is achieved from a commercial software that locates all the frames (not suitable for real time applications). The paper presents a methodology that fuses IRB with MEMS measurements. Results have shown that with single IRB fixes fused together with inertial navigation the standard errors with the 95th percentile are about 1.66 m, 2.36 m and 3.16 m if the interval between two IRB localizations are 1, 2 and 5 sec respectively, while these errors decrease up to 0.64 m, 1.79 m and 2.05 m if the commercial hybrid absolute and relative orientation solution is considered. We can affirm that the weakness in single photograms positioning approach resides in poor orientation estimation of photograms that can grow up to some tens of degrees when poor geometry is picked in the camera scene. With this study we have demonstrate that the hybrid IRB positioning technique coupled with an INS instrument is useful for indoor navigation because errors are less than 2 m and 1 m, with intervals of about 2 s and 1 s between two images, respectively

Technique based on 3D LIDAR scanning and MPEG7 Visual Search Solution

This paper describes a location algorithm for mobile phones based on image recognition. The use of image recognition based (IRB) positioning in mobile applications is characterized by the availability of a single camera; under this constraint, to estimate the camera position and orientation a prior knowledge of 3D environment is needed, acquired for instance with a LiDAR (Light Detection And Ranging) survey, in the form of a database of images with associated spatial information The procedure to locate the camera can be divided in two steps, a first step is the selection of the (reference) image from the database as most similar to the (query) image used to locate the camera and a second one for estimation of the position and orientation of the camera based on available 3D information on the reference image. In designing those steps of the proposed location procedure, we propose to reuse as much as possible the solutions adopted in MPEG7 standard for Visual Search: for processing time optimization we have introduced also in position estimation procedure, with an approach similar to the one of retrieval procedure defined by MPEG7, the geometric check for a preliminary coarse outliers rejections in paired descriptors belonging to the couple (query and reference) of images. We present the position and orientation accuracy results of the location methodology, for indoor and outdoor environment, that reaches few decimeters precision in large percentage of case in both selected environments

The MPEG7 Visual Search Solution for image recognition based positioning using 3D models

This paper describes a location algorithm for mobile phones based on image recognition. The use of image recognition based (IRB) positioning in mobile applications is characterized by the availability of a single camera; under this constraint, to estimate the camera position and orientation, a prior knowledge of 3D environment is needed in the form of a database of images with associated spatial information; this database can be built projecting the 3D model, acquired for instance with a LiDAR (Light Detection And Ranging), on a set of synthetic images. The herein proposed procedure to locate the camera can be divided in two steps, a first step is the selection from a database of the most similar image to the query image used to locate the camera, and a second step for estimation of the position and orientation of the camera based on available 3D information on the reference image. In designing the proposed location procedure, we have reused as much as possible the MPEG standard Compact Descriptors for Visual Search. For processing load optimization, similarly to the retrieval procedure defined by MPEG, we have introduced also in the position estimation step a preliminary statistical geometric check for coarse rejection of wrong matches (where a match represents two views in the respective images of the same point). We present the position and orientation accuracy results of the location methodology, for indoor and outdoor environment, that reaches respectively few decimeters and tenth of radians of precision

The MPEG7 Visual Search Solution for image recognition based positioning using 3D models

This paper describes a location algorithm for mobile phones based on image recognition. The use of image recognition based (IRB) positioning in mobile applications is characterized by the availability of a single camera; under this constraint, to estimate the camera position and orientation, a prior knowledge of 3D environment is needed in the form of a database of images with associated spatial information; this database can be built projecting the 3D model, acquired for instance with a LiDAR (Light Detection And Ranging), on a set of synthetic images. The herein proposed procedure to locate the camera can be divided in two steps, a first step is the selection from a database of the most similar image to the query image used to locate the camera, and a second step for estimation of the position and orientation of the camera based on available 3D information on the reference image. In designing the proposed location procedure, we have reused as much as possible the MPEG standard Compact Descriptors for Visual Search. For processing load optimization, similarly to the retrieval procedure defined by MPEG, we have introduced also in the position estimation step a preliminary statistical geometric check for coarse rejection of wrong matches (where a match represents two views in the respective images of the same point). We present the position and orientation accuracy results of the location methodology, for indoor and outdoor environment, that reaches respectively few decimeters and tenth of radians of precision