Benchmarking CPUs and GPUs on embedded platforms for software receiver usage

Smartphones containing multi-core central processing units (CPUs) and powerful many-core graphics processing units (GPUs) bring supercomputing technology into your pocket (or into our embedded devices). This can be exploited to produce power-efficient, customized receivers with flexible correlation schemes and more advanced positioning techniques. For example, promising techniques such as the Direct Position Estimation paradigm or usage of tracking solutions based on particle filtering, seem to be very appealing in challenging environments but are likewise computationally quite demanding. This article sheds some light onto recent embedded processor developments, benchmarks Fast Fourier Transform (FFT) and correlation algorithms on representative embedded platforms and relates the results to the use in GNSS software radios. The use of embedded CPUs for signal tracking seems to be straight forward, but more research is required to fully achieve the nominal peak performance of an embedded GPU for FFT computation. Also the electrical power consumption is measured in certain load levels.Peer ReviewedPostprint (published version

Improving Precision GNSS Positioning and Navigation Accuracy on Smartphones using Machine Learning

In this work, we developed a precision positioning algorithm for multi-constellation dual-frequency global navigation satellite systems (GNSS) receivers that predicts the latitude and longitude from smartphone GNSS data. Estimation for all epochs that have at least four valid GNSS observations is generated. Receivers (especially low-cost receivers) often have limited channels and computational resources, therefore, the complexity of the algorithm used in them needs to be kept low. The datasets and results in this paper are based on the data provided by Google under the session "High Precision GNSS Positioning on Smartphones Challenge" in the Institute of Navigation (ION GNSS+ 2021) conference. We began by exploring and analysing the raw GNSS data which includes the training dataset and its ground truth and the test dataset without the ground truth. This analysis gave insight into the nature and correlation of the dataset and helped shape the algorithm that was proposed for the accuracy improvement problem. The design of the algorithm was done using data science techniques to compute the average of the predictions of several devices data in the same collection (training dataset baseline coordinates and their ground truth) and then the data was used to train a few selected machine learning algorithms namely, Linear Regression (LR), Bayesian Ridge (BR) and Neural Network (NN) to predict the offset of the test data baseline coordinates from the expected ground-truth (which was not provided). A simple weighted average (SWA) which combines all the previous three ML technique was also implemented. The results showed improvement in the position accuracy with the simple weighted average (SWA) method having the best accuracy followed by Bayesian Ridge (BR), Linear Regression (LR), and then Neural Network (NN) respectively.©2021 The Author. Published by ION.fi=vertaisarvioimaton|en=nonPeerReviewed

Assessing the Spoofing Threat: Development of a Portable GPS Civilian Spoofer

A portable civilian GPS spoofer is implemented on a digital signal processor and used to characterize spoofing effects and develop defenses against civilian spoofing. This work is intended to equip GNSS users and receiver manufacturers with authentication methods that are effective against unsophisticated spoofing attacks. The work also serves to refine the civilian spoofing threat assessment by demonstrating the challenges involved in mounting a spoofing attack.Aerospace Engineering and Engineering Mechanic

A new multipath mitigation method for GNSS receivers based on antenna array

the potential of small antenna array for multipath mitigation in GNSS systems is considered in this paper. To discriminate the different incoming signals (Line of sight and multipaths), a new implementation of the well known SAGE algorithm is proposed. This allows a significant complexity reduction and it is fully compatible with conventional GNSS receivers. Theoretical study thanks to the Cramer Rao Bound derivation and tracking simulation results (in static and dynamic scenarios) show that the proposed method is a very promising approach for the multipath mitigation problem in GNSS receivers

A Low Cost UWB Based Solution for Direct Georeferencing UAV Photogrammetry

Thanks to their flexibility and availability at reduced costs, Unmanned Aerial Vehicles (UAVs) have been recently used on a wide range of applications and conditions. Among these, they can play an important role in monitoring critical events (e.g., disaster monitoring) when the presence of humans close to the scene shall be avoided for safety reasons, in precision farming and surveying. Despite the very large number of possible applications, their usage is mainly limited by the availability of the Global Navigation Satellite System (GNSS) in the considered environment: indeed, GNSS is of fundamental importance in order to reduce positioning error derived by the drift of (low-cost) Micro-Electro-Mechanical Systems (MEMS) internal sensors. In order to make the usage of UAVs possible even in critical environments (when GNSS is not available or not reliable, e.g., close to mountains or in city centers, close to high buildings), this paper considers the use of a low cost Ultra Wide-Band (UWB) system as the positioning method. Furthermore, assuming the use of a calibrated camera, UWB positioning is exploited to achieve metric reconstruction on a local coordinate system. Once the georeferenced position of at least three points (e.g., positions of three UWB devices) is known, then georeferencing can be obtained, as well. The proposed approach is validated on a specific case study, the reconstruction of the façade of a university building. Average error on 90 check points distributed over the building façade, obtained by georeferencing by means of the georeferenced positions of four UWB devices at fixed positions, is 0.29 m. For comparison, the average error obtained by using four ground control points is 0.18 m