The advancement in portable and lightweight sensor technology made the inertial data from daily activities widely accessible, contributing to the interest of tracking these movements or detecting characteristic patterns in these movements. This study focuses on detecting the crosswalk-crossing behavior of visually impaired individuals, which are observed to differ from those of normal sighted people. We hypothesize that a tactile search of the crosswalk endpoint using a cane, a hesitation at the endpoints, or a particular steady walking while crossing are reflected as a unique series of readings in the inertial data. Crosswalk-crossings generate rare and relatively long-duration patterns that regular walking actions, making it challenging to determine which features of the sensory data to focus on. We propose a specially tuned learning framework for detecting crosswalk-crossings using Deep Convolutional Neural Networks, which are known to be effective at data analysis involving temporal hierarchies. Considering also the use cases of the detected crosswalk-crossings, such as correction in the pedestrian positioning systems, we emphasize a precision-focused training with weighted F-scores and a related loss function that minimize false detections. Our findings show that our approach successfully detects real-world crosswalk-crossings with high precision and acceptable recall values. We also show that the models are capable of performing similarly on the data from different devices they are not trained with.

In inertial-only navigation systems, long-term position drift is inevitable without periodic resetting or anchoring. While numerous position improvement techniques have been proposed, the majority of the correction approaches rely on external data sources other than inertial measurements, introducing additional complexity and installation requirements. Furthermore, when attempting to anchor positions using pedestrian map information, it is often observed that pedestrians, including those who are visually impaired, often deviate from predefined walking paths, further complicating accurate positioning. To address these challenges, we introduce inertial-based correction technique for Pedestrian Dead Reckoning (PDR), with the aim of keeping all the processing in the inertial domain. We couple a personalized PDR solution for visually impaired pedestrians with a crosswalk crossing information detected in the inertial data. The correction technique relies on generating parameterized trajectory proposals that match the temporal information from detected crosswalk crossings with known crosswalk locations on maps. We demonstrate that this method reduces the positioning error of the underlying PDR by 50% on real-world data collected from visually impaired individuals, while also meeting real-time processing constraints.

Achieving accurate acoustic-based indoor positioning without synchronized clocks presents a significant challenge, particularly in collaborative systems where multiple devices must estimate pairwise distances simultaneously. In this paper, we propose a novel synchronization-free collaborative acoustic positioning system using standard smartphone speakers and microphones. Our system leverages a convolutional neural network (CNN) trained on Mel-frequency cepstral coefficient (MFCC) features for accurate pairwise distance estimation, followed by a lateration algorithm for position calculation. We demonstrate the feasibility of our approach through real-world experiments, achieving sub-meter positioning accuracy in the best-performing configuration (RMSE = 0.50 m, 90th percentile = 0.80 m). Other configurations, tested under more challenging conditions (e.g., long distances and less optimal geometric placements), exhibit higher errors. Our results highlight that low-frequency tones (440 Hz and 1000 Hz) provide more robust performance, while higher frequencies excel in mid-range distances but at near and far distances are more susceptible to environmental factors decreasing their reliability. Overall, the results indicate that our approach offers a promising solution for practical, synchronization-free acoustic positioning in collaborative systems.

The popularity of lightweight portable sensors has made inertial data from everyday activities widely available, sparkling interest in tracking these activities. This study focuses on detecting the motion patterns of visually impaired people while crossing a crosswalk, which is observed to be different from sighted people. Visually impaired individuals exhibit different behaviors, such as using a cane to find a crosswalk terminal, resulting in unique patterns in inertial data. This work proposes a crosswalk detection method based on inertial signals captured from naturally walking visually impaired pedestrians. Crosswalk crossings are long-lasting actions and rare events in a walking trajectory, introducing questions on which features to use and which parts of the data to investigate. We handle this problem by efficiently tuning Deep Convolutional Neural Networks with their multi-resolution feature extraction capability. Considering also the detected crosswalks in the correction of dead-reckoning based positioning applications, we state that the false detections are intolerable. Thus, we also perform precision-driven training processes, in which the weighted F-score and related surrogate loss functions are employed. The results demonstrate that the proposed approaches detect crosswalks effectively and show potential in classifying other similar long-duration actions.

By using only accelerometers, gyroscopes, and magnetometers, inertial navigation systems can continuously track a pedestrian’s position without relying on external signals, making them a smart choice for seamless location-based services. While many AI-based pedestrian inertial navigation models already exist, most of them are generic and trained on a large amount of data. These models face accuracy limitations due to their inability to account for individual walking characteristics and various scenarios. In this paper, we propose a novel approach to pedestrian inertial navigation tailored to individual users. Our method uses gait segmentation techniques to leverage the cyclical nature of human locomotion, allowing for personalized modeling of walking patterns. A key advantage of our approach is its ability to achieve high accuracy with relatively small training sets—about 36 times smaller than the dataset used to train the state-of-the-art model, RoNIN. Through extensive real-world evaluation, covering 42.6 km with seven profiles learned from six volunteers, we show that our tailored models consistently outperform the RoNIN algorithm. Specifically, we report a stride length error of $0.07 \pm 0.07 \mathrm{~m}$ and a stride angular error of 7.72° ± 7.07°. Furthermore, the proposed method, when evaluated against a real-time implementation of another state-of-the-art generic model, IMUNet, shows superior performance in both walking distance and direction estimations.

In recent years, the frequency and sophistication of attacks on global navigation satellite systems (GNSS) have increased, posing a significant threat to both military and civilian sectors. This paper analyzes the historical evolution and current methods of these attacks and highlights critical vulnerabilities. Detailed case studies from the aviation, maritime, automotive and military sectors illustrate the severity of the impacts on navigation and communication systems. Various countermeasures are discussed, including advanced anti-jamming technologies, GNSS authentication methods and policy initiatives to enhance system resilience. Promising alternative navigation technologies, that are less dependent on GNSS, are also examined. By highlighting the critical research being conducted within the Indoor Positioning and Indoor Navigation (IPIN) community, the paper emphasizes the importance of developing robust indoor positioning and seamless navigation to complement GNSS. This work serves as a call to action for continued innovation and investment in secure and reliable navigation systems with alternative solutions.

Visually impaired people face particular challenges when it comes to positioning and navigation. This paper presents an innovative assistive technology designed to improve the independent mobility of visually impaired people by integrating multiple navigation inputs into a cohesive real-time positioning system. At the heart of our approach is the optimized use of an asynchronous particle filter — a probabilistic, recursive algorithm tailored to pedestrian navigation using Pedestrian Dead-Reckoning (PDR) and Map Matching. Unlike traditional mesh graphs, we employ pathway graphs specifically designed for visually impaired people, incorporating landmarks as critical nodes to enhance accuracy and flexibility. This design adapts to real pedestrian movement and improves the practicality of the system on walkable paths by allowing deviations from theoretical paths. Our system integrates graph edge observations, GNSS data, and landmarks to achieve high localization accuracy and robustness. We have evaluated three advanced navigation models that show significant improvement in stride trajectory estimation accuracy. Model 1, using GNSS, reduced the mean and median Euclidean distance to ground-truth to 2.56 meters and ${2. 2 6}$ meters, respectively. Model 2, which incorporates simulated landmarks, achieved mean distances of 2.17 meters and a median of 1.97 meters, while Model 3, which uses a graph edge-based approach, achieved the best results at a mean of 1.7 meters and a median of 1.47 meters. These results underline the improved accuracy of our navigation systems for visually impaired users, especially in complex environments.

In this article, we explore the impact of outfits on AI-based Pedestrian Dead Reckoning (PDR) with a pocket-worn inertial sensor. This PDR mode faces significant variability due to the countless choices of outfits available. We observe significant variations in the inertial signals captured by a pocket-worn device, which are highly influenced by the outfit being worn. To address this, we propose a 2-category classification of outfits as tight or loose, based on their impact on the inertial signals. Notably, AI models trained on tight outfits exhibit poor generalization with loose outfits and vice versa. We highlight this phenomenon by implementing a data-low-cost PDR algorithm based on Support Vector Regression (SVR) and assess its performance on two healthy volunteers and a senior and blind volunteer wearing tight and loose outfits, on real-life situation test tracks spanning approximately 200 to 400 meters.

Global Navigation Satellite System (GNSS)-based navigation is usually considered as not usable indoors where the satellite visibility is degraded, and the complex propagation conditions perturb the GNSS signals with reflection and refraction. However, this paper presents a novel approach called LIGHT(Light Indoor GNSS macHine-learning-based Time difference carrier phase) that can select healthy indoor GNSS carrier phase data thanks to Machine Learning (ML) for positioning. The selected carrier phase data are fed into a Time Difference Carrier Phase (TDCP) based Extended Kalman Filter (EKF) to estimate the user’s velocity. Two indoor scenarios (shopping mall and railway station) are tested over a 2 km total walking distance. It is shown that at least half of the epochs become usable for GNSS TDCP standalone positioning, and the accuracy of the velocity estimates can improve up to 87% in terms of the 75th percentile of the absolute horizontal velocity error compared with the non-ML approach. Furthermore, a newly-designed hybridization filter LIGHT-PDR that fuses the LIGHT algorithm and Pedestrian Dead Reckoning (PDR) solution together is applied to perform seamless indoor/outdoor positioning in a more robust way.

Navigation is one of the major difficulties for Visually Impaired People (VIP). Spatial landmarks are of great importance for them to find their way and orient themselves. In this paper, the most commonly used spatial landmarks by VIP are identified and their geometric constraints are constructed to pave the way for map-matching algorithms. The representative landmarks for VIP were identified through a systematic interview with 12 VIP, whose profiles cover different levels of vision impairments using different assistive mobility aids. Various analyses are performed based on their sensory modality, frequency of use as well as the number of users. Next, the previously identified landmarks are divided into two categories: waypoints and reassurance points, depending on whether they contribute directly to map-matching algorithms. Geometric constraints are designed for each identified landmark to facilitate their integration into the map-matching or path-planning algorithms. Finally, an explicit dictionary of landmarks and their geometric constraints is proposed dedicated to the VIP’s navigation in cities. Through a user centric approach, our method translates the subjective, personal navigation experiences of the VIP into an objective, universally accessible format.

Miniaturized IMU (inertial measurement units) are widely integrated in wearable devices, promoting the versatile and low cost pedestrian inertial navigation technology, especially for indoor environment. In recent years, AI (Artificial Intelli-gence) is applied to improve the performance of this technology. AI methods work with data samples, thus it is important to select a suitable process for segmenting the inertial data sequences. This survey classifies AI methods for pedestrian inertial navigation into two categories, namely human gait driven methods and sampling frequency driven methods, according to their data segmentation process. Human gait driven methods segment the inertial measurement sequence by gait (step or stride) events and learn to infer a gait vector (step/stride length and direction) given a gait segment. Sampling frequency driven methods learn to infer the user's velocity or change in position given a fixed-length segment of inertial measurements. The survey studies the underlying assumptions and their validity of the two categories of AI methods. Two methods (SELDA and RoNIN), each from a category, are chosen for evaluation and comparison, on three testing tracks totaling 770m, covering indoor and outdoor en-vironment, including stairs. The experiments highlight the two methods' advantages and limitations, supporting the theoretical analyses. The selected methods achieve 7m and 12m positioning errors, respectively.

Robustly detecting steps with inertial sensors em- bedded in smartphones remains a challenging problem for pedestrian navigation, mainly due to the diversity of human gaits. In this paper, we propose a new step detection method for handheld devices, smartSTEP, that processes acceleration and gyroscope signals with machine learning techniques. The advantage of smartSTEP is that it does not rely on hand motion mode classifiers nor thresholds calibration. Trained on 9000 steps from 12 different participants and tested on approximately 2200 steps recorded on persons mostly not involved in the training, it achieved 99% recall and 98.9% precision in challenging scenarios such as asymmetrical walking, outdoor walking on different surfaces with different hand motion modes, and stairs climbing. A 0.097 seconds root mean square error is achieved on the predicted stride duration. This competes with the performances of present algorithms dedicated to calculating stride duration.

The quality of positioning information given by smartphones is not always fully evident and might be inaccurate. With the possibility to access the Inertial Measurement Unit (IMU) data of the smartphone using the Android application program interface (API) functions and the Global Navigation Satellite System (GNSS) signals using the GNSS analysis App from Google, more or less unprocessed data is made available to developers and users. This enables the assessment of the quality of the smartphone chipsets and final positioning information. In this paper, we investigate the quality of available GNSS and IMU measurements and final track estimates of the smartphone. The analysis includes both static and dynamic measurements. A platform is developed to carry both, the smartphone and high accurate reference sensors for a fair comparison in the dynamic tests. The data of the different sensors are synchronized and the lever arm is removed. Open-sky and urban environments are considered for the GNSS analyses. Depending on the environment and on the dynamics, we show the different quality impairments of the data in this paper. Moreover, the Google Android indicators are investigated and their consistency and context with the data are provided.

This paper presents a wearable positioning system which is able to provide real-time positioning information for all kinds of environments in a robust way. The system is mainly based on a foot-mounted INS assisted by a GNSS receiver as well as a barometer. The scenario presented in this paper took place during the final competition of the challenge MALIN (MAîtrise de la Localisation INdoor) organized by the DGA (Direction Générale de l’Armement) and the French National Research Agency (ANR). The objective of this challenge is to create a positioning system to track emergency response agents in non-collaborative environments, where GNSS signals are usually defeated. The proposed INS-based foot-mounted system is able to provide highly accurate positioning for various motion types (walking, running, stairs, ladder) thanks to a machine learning-based Zero Velocity Detector (ZVD). The external GNSS receiver is used to capture the GNSS positions in favorable conditions and further to provide absolute position and orientation corrections via a least square-based point pattern matching algorithm. The proposed system is tested over a 2.5 km trajectory in a soldier scenario including complex outdoor and deep indoor environments. The proposed system was able to provide real-time positioning information with an accuracy around 0.3% of error over the total traveled distance. The 75% HPE remains below 8 m and the 75% VPE is under 3 m.

Over the last decade, the number of accidents involving Vulnerable Road Users (VRU), i.e. pedestrians, cyclists and motorbike drivers, has not decreased in the same way as accidents between passenger cars have. Cooperative systems based on Vehicle-to-X (V2X) communication make it possible to directly exchange information between VRUs and vehicles and to increase the overall situational awareness beyond the capabilities of on-board ranging sensors. To detect and avoid collisions, vehicles require up-to-date and precise information on the location and trajectory of VRUs. In this paper, we propose a VRU localization system based on Global Navigation Satellite System (GNSS), inertial sensors and ultra-wideband (UWB) round-trip-delay ranging technology. We present an exhaustive measurement campaign comprising pedestrians, cyclists and vehicles performed in an urban setting and show first results on the localization performance for a pedestrian crossing an intersection. In the experiments, the pedestrian inertial system supported by GNSS and UWB ranges is able to achieve 0.65 m 1σ-position accuracy.

With the highly development of navigation techniques during the past decades, the demand for seamless indoor-outdoor navigation is growing from different application fields especially for the military or the first response emergency services. For military applications, one of the key performance requirements is the availability of the positioning solutions for all kinds of dynamics in different environments. Furthermore, due to the stealth requirement in some military actions, it is impossible for military vehicles or personnel to emit signals which enable to be detected by their opponents. This limitation prevents the use of infrastructure-based cooperative localization techniques.The research work of this paper aims at facing the following challenging issues: firstly, to design a positioning filter which is adaptive to the dynamic changes between walking and driving; secondly, to find an approach that correctly identifies the transition between outdoor and indoor with reduced latency; finally, to construct a loosely coupling GNSS/IMU scheme which takes into account the GNSS signal distortion in indoor and urban spaces.Under this context, we propose a complete indoor-outdoor infrastructure-free positioning prototype including a foot-mounted reference navigation system named Pedestrian Reference System (PERSY) and a Ublox High Sensitivity GNSS (HS-GNSS) receiver (M8P). A loosely-coupled architecture between GNSS receiver and the PERSY is employed by using an indicator of horizontal position accuracy PACCH provided by the GNSS Ublox M8P receiver. This indicator allows qualifying the position solutions delivered by the GNSS receiver as well as detecting the transition of indoor/outdoor, which helps the PERSY to update with absolute positions from GNSS. This positioning prototype can take advantage of both GNSS and PERSY so as to realize a seamless indoor-outdoor positioning for pedestrians and vehicles. The proposed system is evaluated in two scenarios over respectively 2.17 km and 2.68 km including indoor , outdoor and in-vehicle phases. The median horizontal position errors for the two scenarios are respectively 2.23 m and 1.93 m.

The following topics are dealt with: indoor radio; mobile robots; pedestrians; smart phones; position control; radionavigation; wireless LAN; indoor communication; indoor navigation; Kalman filters.

To support pedestrian navigation in urban and indoor spaces, an accurate pose estimate (i.e. 3D position and 3D orientation) of an equipment held in hand constitutes an essential point in the development of mobility assistance tools (e.g. Augmented Reality applications). On the assumption that the pedestrian is only equipped with general public devices, the pose estimation is restricted to the use of low-cost sensors embedded in the latter (i.e, an Inertial and Magnetic Measurement Unit and a monocular camera). In addition, urban and indoor spaces, comprising closely-spaced buildings and ferromagnetic elements, constitute challenging areas for sensor pose estimation during large pedestrian displacements. However, the recent development and provision of 3D Geographical Information System content by cities constitutes a wealth of data usable for pose estimation. To address these challenges, we propose an autonomous sensor fusion framework for pedestrian hand-held device pose estimation in urban and indoor spaces. The proposed solution integrates inertial and magnetic-based attitude estimation, monocular Visual Odometry with pedestrian motion estimation for scale estimation and known 3D geospatial object recognition-based absolute pose estimation. Firstly, this allows to continuously estimate a qualified pose of the device held in hand. Secondly, an absolute pose estimate enables to update and to improve the positioning accuracy. To assess the proposed solution, experimental data has been collected, for four different people, on a 0.5 km pedestrian walk in an urban space with sparse known objects and indoors passages. According to the performance evaluation, the sensors fusion process enhanced the pedestrian localization in areas where conventional hand-held systems were not accurate or available.

Inertial and magnetic sensors based PDR approaches are particularly interesting for pedestrian location since they don't require any specific infrastructure. Estimating the walking direction, which is essential for PDR strategy, remains difficult with handheld sensors. WAISS is a new method that integrates hand movement and is independent of the misalignment between the walking direction and the pointing direction that estimates the walking direction. It uses statistical models of the hand accelerations in the horizontal plane. The paper studies how to create the best possible models. Among the features under study are the number of strides used to learn the models, different acquisition contexts and walking directions. Finally, the complexity of models needed for a given person is discussed. 100 strides over curved and straight line walks combined with a bi-modal Gaussian Mixture Model gives the best walking direction estimate with a 15° mean error over a 325 m indoor/outdoor walk performed by four subjects.

Complementary data are necessary to bind the positioning error growth of Pedestrian Dead Reckoning (PDR). In this paper, absolute position updates are made possible with the online detection of different types of points of interest (POIs) located on the map. The POIs are selected depending on specific motion patterns which are associated to absolute locations on the map. To create the POIs database, the correlation between pedestrian motion and different map locations is first studied and the outcome is a typology of POIs. A K-NN (K nearest neighbors) algorithm is used to train different motion modes, which are further exploited for the detection of POIs in order to update the PDR algorithm with position data. Experimental assessment of the POI-based PDR calibration is conducted in both outdoor and indoor spaces with a focus on the transition between both environments. 90% of the time, motion is correctly classified and the PDR position is corrected with an accuracy that depends on POIs features (width of corridor/door, staircase size...). Therefore, the approach is found to be promising for enhancing PDR positioning using only map data.

Urban environment constitutes a challenging area for pedestrian navigation. However, with the recent increase of pedestrians owning devices (e.g. smartphones), complementary data provided by integrated low cost sensors (camera, Inertial and Magnetic Measurement Unit and GNSS receiver) may be used in a coupling process to accurately estimate the pose (i.e. 3D position and 3D orientation) of a handheld device. Additionally, the actual development and availability of 3D GIS content constitutes a mine of data usable for camera pose estimation. In the context of pedestrian navigation in urban environment, to update a Pedestrian Dead-Reckoning process and to improve the positioning accuracy, we propose to fuse the pose estimated through a vision process thanks to a precisely known 3D model with inertial and magnetic measurements. Experimental data collected in an urban environment, on a long pedestrian path with sparse known models permit to validate the benefit of sensors fusion process. This results in an improved positioning accuracy that enhances the Pedestrian Dead-Reckoning process and enables to display 3D information in Augmented Reality. Performance are presented in terms of positioning accuracy and compared to commonly used solutions.

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.

Walking is now promoted as an alternative transport mode to polluting cars and as a successful means to improve health and longevity. Intelligent transport systems navigation services are now directly targeting travelers due to smartphones and their embedded sensors. However, after a decade of research, no universal personal navigation system has been successfully introduced and adopted to improve personal mobility. An analysis of the underlying reasons is conducted, looking at the engineering, human, ethical, and legal challenges. First, contrary to adopting classical mechanization equations linked to solid state physics, location technologies must address complex personal dynamics using connected objects. Second, human factors are often not sufficiently considered while designing new technologies. The needs and abilities of travelers are not systematically addressed from a user-centered perspective. Finally, people want to benefit from location-based services without sharing personal location data to uncontrolled third bodies. Europe is a pioneer in the protection of individuals from personal identification through data processing since location data has been recognized as personal data, but the challenges to enforce the regulation are numerous. The recommendation of “privacy by design and default” is an interesting key to conceive the universal personal navigation solution. Alternative solutions are highlighted, but they definitively require a more interdisciplinary conception.

An A⁎ -based routing graph is proposed to assist PDR indoor and outdoor navigation with handheld devices. Measurements are provided by inertial and magnetic sensors together with a GNSS receiver. The novelty of this work lies in providing a realistic motion support that mitigates the absence of obstacles and enables the calibration of the PDR model even in large spaces where GNSS signal is unavailable. This motion support is exploited for both predicting positions and updating them using a particle filter. The navigation network is used to correct for the gyro drift, to adjust the step length model and to assess heading misalignment between the pedestrian’s walking direction and the pointing direction of the handheld device. Several datasets have been tested and results show that the proposed model ensures a seamless transition between outdoor and indoor environments and improves the positioning accuracy. The drift is almost cancelled thanks to heading correction in contrast with a drift of 8% for the nonaided PDR approach. The mean error of filtered positions ranges from 3 to 5 m.

A Map aided Pedestrian Dead Reckoning (PDR) algorithm is proposed to mitigate the drift errors and step detection limitations of pedestrian dead reckoning algorithm with handheld sensors in indoor and outdoor spaces. Specific to this context is the changing lever-arm between the handheld device and the pedestrian center of mass that introduces a misalignment between the inertial sensors and the walking directions. To address these challenges, an adaptive routing graph is built based on possible pedestrian's motions, which depend on personal mobility profile and surroundings. An adaptive decision process is also developed to fuse map data with GNSS positions and PDR outputs in a particle filter. The performance is assessed with 1km walk experiments. Main contributions are (1) the calibration of the PDR step length model using both GNSS and map data during straight line travels with miss/over-detected steps modeled by the particle filter; (2) the estimation of angular misalignment between the walking and the handheld unit pointing directions in geometrically constrained areas; (3) a dynamic choice of opportune periods and measurements to calibrate the PDR outputs and improve the positioning process.

More and more pedestrians own devices (as a smartphone) that integrate a wide array of low-cost sensors (camera, IMU, magnetometer and GNSS receiver). GNSS is usually used for pedestrian localization in urban environment, but signal suffers of an inaccuracy of several meters. In order to have a more accurate localization and improve pedestrian navigation and urban mobility, we present a method for city-scale localization with a handheld device. Our central idea is to estimate the 3D location and 3D orientation of the phone camera based on the knowledge of the street furnitures, which have a high repeatability and a large coverage area in the city. Firstly, the use of inertial measurements acquired with an IMU in the vision based method allows to accelerate the calculation of the position and orientation. Secondly, the weighted fusion between the rotation matrices calculated with the vision and the inertial processes allows to give the more importance in the calculation with the highest confidence. With a good points selection, this provides a localization that is in the GNSS post-processed measurement precision use for determining the position and the orientation of the street furnitures. Performances are presented in terms of accuracy of positionning. The final aim is to have with our method a precision good enough to be able to propose in future works a on site display in augmented reality.

Pedestrian navigation systems based on smartphone are experiencing fast progress in indoor environment. Pedestrian dead reckoning approaches combined with improved inertial sensors' quality and the exploitation of magnetic field are used to mitigate the sensor drifts. The last remaining issue is related to the hand dynamic. It consists in estimating the angular misalignment between the smartphone pointing direction and the walking direction. Even though, some methods exist, their performances are lacking accuracy and reliability. A comparison of the three main methods to estimate this angular misalignment is performed. These methods are based on Principal Component Analysis (PCA), Forward and Lateral Accelerations Modeling (FLAM) and Frequency analysis of Inertial Signals (FIS). Despite better results for the FIS method all algorithm suffer from large outliers and a need for improved robustness is identified.

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.

As free inertial pedestrian navigation systems with foot mounted inertial mobile unit mature, it becomes feasible to conceive a sufficiently accurate derived solution for assessing other indoor navigation systems and assisting research activities. The design of this reference solution and the remaining challenges are at the heart of this paper. A new filter with a quaternion based state vector exploits signals (acceleration and magnetic field) and motions of opportunity for estimating the navigation solution. Quasi Static Field (QSF) updates, Magnetic Angular Rate Updates (MARU) and Angular Gradient Update (AGU) are frequently applied for mitigating the low-cost inertial sensors errors. Experimental tests performed using a post-processed GPS differential solution as reference show an average accuracy of 1.2 m over 500 m walking path: 0.5 %. Discussions about the data acquisition protocol for meeting a 1 meter horizontal accuracy within two standard deviations of the mean (95.45%) is conducted.

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.

Heading estimation plays an important role in pedestrian navigation applications. With the advent of smart-phones equipped with MEMS sensors, it has become possible to utilize ones orientation information along with location. This combination has allowed researchers to investigate provisioning users with orientation aware location based services as well as seamless navigation in different environments using Pedestrian Dead Reckoning (PDR) techniques. Although gyroscopes are considered to be the primary sensors for orientation estimation, the errors associated with these sensors require periodic updates from other sources. In case of small hand held devices, these other sources are accelerometers for roll and pitch estimates and magnetic field sensors for the heading. In order to utilize the magnetic field sensors for heading estimation with respect to some known reference, it is desirable to measure only the Earth's magnetic field components. Although this is achievable in the outdoors, presence of manmade infrastructure in all kinds of urban environments makes it impossible to sense only the Earth's magnetic field at all times. These manmade magnetic anomalies caused by electronic devices, ferrous materials, mechanical and electrical infrastructures among others are the main culprits contaminating the magnetic field information. Therefore it is desirable to investigate how good one can estimate the heading using magnetic field alone in different pedestrian navigation environments by isolating the perturbed regions from the clean ones. In this paper, a detector is proposed that can identify the magnetic field measurements that can be used for estimating heading with adequate accuracy. The expected errors in the heading estimates are also output based on the test statistics, which allow the proposed detector to be utilized for sensor fusion and estimation of errors associated with gyroscopes. Real world data is acquired using a custom designed consumer grade sensor platform and a high accuracy reference system. Theoretical analysis and experimental results show that the proposed detector is capable of identifying the effects of perturbations on the Earth's magnetic field, which provides users with a better estimate of magnetic heading in different pedestrian navigation environments.

In this paper, an algorithm for the detection of quasi-static instants (QS) from handheld MEMS devices is presented. In order to tune the detector according to the variety of motions that the hand can perform, a decision tree classifier, able to recognize activities typical for mobile phone users, such as phoning, texting, walking with swinging hand or carrying the device in a bag, has been designed and implemented. Performances of the proposed detector of QS epochs and of the motion mode classifier are assessed with experimental data collected with several individuals. In addition, the relationship between QS instants and human gait is investigated. Specifically, the use of QS instants for the detection of the user's step is analyzed.