Miguel Ortiz

About Non-Line-Of-Sight Satellite Detection and Exclusion in a 3D Map-Aided Localization Algorithm

Reliable GPS positioning in city environment is a key issue actually, signals are prone to multipath, with poor satellite geometry in many streets. Using a 3D urban model to forecast satellite visibility in urban contexts in order to improve GPS localization is the main topic of the present article. A virtual image processing that detects and eliminates possible faulty measurements is the core of this method. This image is generated using the position estimated a priori by the navigation process itself, under road constraints. This position is then updated by measurements to line-of-sight satellites only. This closed-loop real-time processing has shown very first promising full-scale test results.

GNSS Autonomous Localization: NLOS Satellite Detection Based on 3-D Maps

One of the main drawbacks of global navigation satellite systems (GNSSs) in urban environments is that signals may arrive at the receiver antenna only in nonline-of-sight (NLOS) conditions, leading to biased pseudorange estimates when they are taken for granted by the receiver and, eventually, wrong positioning. This article presents a study on the benefits of using three-dimensional (3-D) maps of cities to decide whether the GNSS signal coming from each tracked satellite is reliable. Based on this principle, two different 3-D maps and two methodologies are presented and compared. The results show the benefits of this approach.

Adaptative pedestrian displacement estimation with a smartphone

Pedestrian dead reckoning is one of the most promising processing strategies of inertial signals collected with a smartphone for autonomous indoor personal navigation. When the sensors are held in hand, step length models are usually used to estimate the walking distance. They combine stride frequency with a finite number of physiological and descriptive parameters that are calibrated with training data for each person. But even under steady conditions, several physiological conditions are impacting the walking gait and consequently these parameters. Frequent calibration is needed to tune these models prior to relying on free inertial navigation solutions in indoor locations. Two hybridization filters are proposed for calibrating the step length model and estimating the navigation solution. They integrate either GNSS standalone positions or GNSS Doppler depending on the coupling level. A data collection performed with four test subjects show the variations of these parameters for the same person during his journey and effectiveness of the calibration for improving the estimation of walking distances. Thanks to the new filters, the error on the travelled distance gets reduced to 7% with the loosely coupled filter and 2% with the tightly coupled filter.

Smartphone based gait analysis using STFT and wavelet transform for indoor navigation

In this paper, we propose a frequency domain analysis for characterizing the walking gait in the context of indoor navigation, without assuming that the sensors are rigidly attached to the body. Firstly, frequency analysis is performed using Short Time Fourier Transform (STFT) since the statistical properties of the signal are changing over time but are assumed contant over a hort window. Globally STFT can extract step/stride frequency, but STFT is found non optimal for fast motion transitions. Wavelet Transform (WT) analysis is then introduced. Contrary to STFT, WT uses a size-adjustable window, which offers more advantages for human gait features extraction. When the time period of interest comprises a high frequency, the window is short, while when the local area comprises a low frequency, the window size is enlarged. This WT propriety is found to be critical our smartphone based gait analysis. Experimental assessment is performed with a smartphone Nokia Lumia 920 and a foot mounted MEMS grade inertial used as reference. These results are encouraging for designing a robust and adaptable real-time motion detection solution for smartphone in the context of indoor navigation.

A New PDR Navigation Device for Challenging Urban Environments

The motivations, the design, and some applications of the new Pedestrian Dead Reckoning (PDR) navigation device, ULISS (Ubiquitous Localization with Inertial Sensors and Satellites), are presented in this paper. It is an original device conceived to follow the European recommendation of privacy by design to protect location data which opens new research toward self-contained pedestrian navigation approaches. Its application is presented with an enhanced PDR algorithm to estimate pedestrian’s footpaths in an autonomous manner irrespective of the handheld device carrying mode: texting or swinging. An analysis of real-time coding issues toward a demonstrator is also conducted. Indoor experiments, conducted with 3 persons, give a 5.8% mean positioning error over the 3 km travelled distances.

Assessment of end-to-end performances of a GNSS-based road user charging system based on IFSTTAR-GEOLOC and Q-Free cooperation

The paper deals with the issues that have to be faced when researching a Road User Charging (RUC) in Norway based upon GNSS and with an innovative methodology especially developed to address these issues proposed by CEN-CENELEC TC5/WG1 and supported by the SaPPART COST Action. The paper presents a case study applying the methodology to a specific RUC algorithm developed by the Norwegian company Q-Free. A dedicated GNSS position error model has been produced by Ifsttar GEOLOC for this purpose.

Foot-mounted pedestrian navigation reference with tightly coupled GNSS carrier phases, inertial and magnetic data

Many indoor navigation systems have been developed for pedestrians and assessing their performances is a real challenge. Benefiting from a reference solution that is accurate enough to evaluate other indoor navigation systems and assist novel research is of prime interest. The design and algorithms of a foot-mounted reference navigation system titled PERSY (PEdestrian Reference SYstem) are presented in this paper. Quasi static phases of the acceleration and the magnetic field are used to mitigate inertial sensor errors in indoor spaces. Differential indoor/outdoor GNSS phase measurements are added to the strapdown EKF to improve the positioning accuracy with a correlation between low and high frequency velocity estimates. Experiments conducted with four persons over a 1.4 km walking distance show a 0.22% positioning mean error.