Generic Multisensor Integration Strategy and Innovative Error Analysis for Integrated Navigation

A modern multisensor integrated navigation system applied in most of civilian applications typically consists of GNSS (Global Navigation Satellite System) receivers, IMUs (Inertial Measurement Unit), and/or other sensors, e.g., odometers and cameras. With the increasing availabilities of low-cost sensors, more research and development activities aim to build a cost-effective system without sacrificing navigational performance. Three principal contributions of this dissertation are as follows: i) A multisensor kinematic positioning and navigation system built on Linux Operating System (OS) with Real Time Application Interface (RTAI), York University Multisensor Integrated System (YUMIS), was designed and realized to integrate GNSS receivers, IMUs, and cameras. YUMIS sets a good example of a low-cost yet high-performance multisensor inertial navigation system and lays the ground work in a practical and economic way for the personnel training in following academic researches. ii) A generic multisensor integration strategy (GMIS) was proposed, which features a) the core system model is developed upon the kinematics of a rigid body; b) all sensor measurements are taken as raw measurement in Kalman filter without differentiation. The essential competitive advantages of GMIS over the conventional error-state based strategies are: 1) the influences of the IMU measurement noises on the final navigation solutions are effectively mitigated because of the increased measurement redundancy upon the angular rate and acceleration of a rigid body; 2) The state and measurement vectors in the estimator with GMIS can be easily expanded to fuse multiple inertial sensors and all other types of measurements, e.g., delta positions; 3) one can directly perform error analysis upon both raw sensor data (measurement noise analysis) and virtual zero-mean process noise measurements (process noise analysis) through the corresponding measurement residuals of the individual measurements and the process noise measurements. iii) The a posteriori variance component estimation (VCE) was innovatively accomplished as an advanced analytical tool in the extended Kalman Filter employed by the GMIS, which makes possible the error analysis of the raw IMU measurements for the very first time, together with the individual independent components in the process noise vector

Optimized Filter Design for Non-Differential GPS/IMU Integrated Navigation

The endeavours in improving the performance of a conventional non-differential GPS/MEMS IMU tightly-coupled navigation system through filter design, involving nonlinear filtering methods, inertial sensors' stochastic error modelling and the carrier phase implementation, are described and introduced in this thesis. The main work is summarised as follows. Firstly, the performance evaluation of a recently developed nonlinear filtering method, the Cubature Kalman filter (CKF), is analysed based on the Taylor expansion. The theoretical analysis indicates that the nonlinear filtering method CKF shows its benefits only when implemented in a nonlinear system. Accordingly, a nonlinear attitude expression with direction cosine matrix (DCM) is introduced to tightly-coupled navigation system in order to describe the misalignment between the true and the estimated navigation frames. The simulation and experiment results show that the CKF performs better than the extended Kalman filter (EKF) in the unobservable, large misalignment and GPS outage cases when attitude errors accumulate quickly, rendering the psi-angle expression invalid and subsequently showing certain nonlinearity. Secondly, the use of shaping filter theory to model the inertial sensors' stochastic errors in a navigation Kalman filter is also introduced. The coefficients of the inertial sensors' noises are determined from the Allan variance plot. The shaping filter transfer function is deduced from the power spectral density (PSD) of the noises for both stationary and non-stationary processes. All the coloured noises are modelled together in the navigation Kalman filter according to equivalence theory. The coasting performance shows that the shaping filter based modelling method has a similar and even smaller maximum position drift than the conventional 1st-order Markovian process modelling method during GPS outages, thus indicating its effectiveness. Thirdly, according to the methods of dealing with carrier phase ambiguities, tightly-coupled navigation systems with time differenced carrier phase (TDCP) and total carrier phase (TCP) as Kalman filter measurements are deduced. The simulation and experiment results show that the TDCP can improve the velocity estimation accuracy and smooth trajectories, but position accuracy can only achieve the single point positioning (SPP) level if the TDCP is augmented with the pseudo-range, while the TCP based method's position accuracy can reach the sub-meter level. In order to further improve the position accuracy of the TDCP based method, a particle filter (PF) with modified TDCP observation is implemented in the TDCP/IMU tightly-coupled navigation system. The modified TDCP is defined as the carrier phase difference between the reference and observation epochs. The absolute position accuracy is determined by the reference position accuracy. If the reference position is taken from DGPS, the absolute position accuracy can reach the sub-meter level. For TCP/IMU tightly-coupled navigation systems, because the implementation of TCP in the navigation Kalman filter introduces additional states to the state vector, a hybrid CKF+EKF filtering method with the CKF estimating nonlinear states and the EKF estimating linear states, is proposed to maintain the CKF's benefits while reducing the computational load. The navigation results indicate the effectiveness of the method. After applying the improvements, the performance of a non-differential GPS/MEMS IMU tightly-coupled navigation system can be greatly improved

Development and Validation of an IMU/GPS/Galileo Integration Navigation System for UAV

Several and distinct Unmanned Aircraft Vehicle (UAV) applications are emerging, demanding steps to be taken in order to allow those platforms to operate in an un-segregated airspace. The key risk component, hindering the widespread integration of UAV in an un-segregated airspace, is the autonomous component: the need for a high level of autonomy in the UAV that guarantees a safe and secure integration in an un-segregated airspace. At this point, the UAV accurate state estimation plays a fundamental role for autonomous UAV, being one of the main responsibilities of the onboard autopilot. Given the 21st century global economic paradigm, academic projects based on inexpensive UAV platforms but on expensive commercial autopilots start to become a non-economic solution. Consequently, there is a pressing need to overcome this problem through, on one hand, the development of navigation systems using the high availability of low cost, low power consumption, and small size navigation sensors offered in the market, and, on the other hand, using Global Navigation Satellite Systems Software Receivers (GNSS SR). Since the performance that is required for several applications in order to allow UAV to fly in an un-segregated airspace is not yet defined, for most UAV academic applications, the navigation system accuracy required should be at least the same as the one provided by the available commercial autopilots. This research focuses on the investigation of the performance of an integrated navigation system composed by a low performance inertial measurement unit (IMU) and a GNSS SR. A strapdown mechanization algorithm, to transform raw inertial data into navigation solution, was developed, implemented and evaluated. To fuse the data provided by the strapdown algorithm with the one provided by the GNSS SR, an Extended Kalman Filter (EKF) was implemented in loose coupled closed-loop architecture, and then evaluated. Moreover, in order to improve the performance of the IMU raw data, the Allan variance and denoise techniques were considered for both studying the IMU error model and improving inertial sensors raw measurements. In order to carry out the study, a starting question was made and then, based on it, eight questions were derived. These eight secondary questions led to five hypotheses, which have been successfully tested along the thesis. This research provides a deliverable to the Project of Research and Technologies on Unmanned Air Vehicles (PITVANT) Group, consisting of a well-documented UAV Development and Validation of an IMU/GPS/Galileo Integration Navigation System for UAV II navigation algorithm, an implemented and evaluated navigation algorithm in the MatLab environment, and Allan variance and denoising algorithms to improve inertial raw data, enabling its full implementation in the existent Portuguese Air Force Academy (PAFA) UAV. The derivable provided by this thesis is the answer to the main research question, in such a way that it implements a step by step procedure on how the Strapdown IMU (SIMU)/GNSS SR should be developed and implemented in order to replace the commercial autopilot. The developed integrated SIMU/GNSS SR solution evaluated, in post-processing mode, through van-test scenario, using real data signals, at the Galileo Test and Development Environment (GATE) test area in Berchtesgaden, Germany, when confronted with the solution provided by the commercial autopilot, proved to be of better quality. Although no centimetre-level of accuracy was obtained for the position and velocity, the results confirm that the integration strategy outperforms the Piccolo system performance, being this the ultimate goal of this research work

A design methodology for the implementation of embedded vehicle navigation systems

RÉSUMÉ Au fil des années, en raison de l'augmentation de la densité routière et l'intensité de la circulation, un système de navigation automobile devient nécessaire. Ce système doit fournir non seulement l'emplacement du véhicule mais, surtout, augmentera le contrôle, la sécurité et la performance globale de l'automobile. La baisse du coût des récepteurs de Géo-Positionnement par Satellite (GPS) a vulgarisé leur utilisation dans la navigation automobile. Le système GPS fournit les données de positionnement ainsi que l'information qui concerne la vitesse aux conducteurs. De ce fait, la plupart des dispositifs de navigation des automobiles civiles sont actuellement basés sur la technologie GPS. Cependant, en cas de perte du signal GPS par blocage par feuillage, passages en béton, dense agglomération urbaine, grands immeubles, tunnels et dans le cas d'atténuation, ces dispositifs ne parviennent pas à fonctionner avec précision. Une solution alternative au GPS, qui peut être utilisée dans la navigation automobile, est le système de navigation inertielle (INS). LINS est un système autonome qui n'est pas affecté par des perturbations externes. Il comprend des capteurs inertiels comme trois gyroscopes et trois accéléromètres. Le coût des INS peut être faible mais leur performance se détériore à long terme car ils souffrent des erreurs accumulées. Cependant, il peut fournir des solutions précises sur de courts intervalles de temps. Un système intégré de GPS/INS à faible coût a donc le potentiel de fournir de meilleures informations de position pendant des intervalles courts et longs. L'objectif principal de cette recherche était de mettre en place une solution d'un système de navigation véhiculaire temps réel sur une plateforme embarquée à faible coût. Ceci avait pour but de pouvoir l'utiliser comme un cadre de conception, et comme référence pour d'autres applications embarquées similaires. Pour améliorer la solution de navigation même en cas d'arrêt de fonctionnement du GPS, les données du système GPS/INS ont été fusionnées par la technique de la boucle fermée du filtrage de Kalman décentralisé en utilisant 15 équations d'états d'erreurs d'ENS. En raison de l'utilisation d'accéléromètre à faible coût, ainsi que des capteurs gyroscopiques de données, une technique de prétraitement nommée algorithme de débruitage par ondelettes a été adoptée. L'algorithme a un maximum de 5 niveaux de décomposition, de reconstruction, ainsi que du seuillage non linéaire à chaque niveau. La conception est décrite par un logiciel qui comprend un microprocesseur embarqué. L'implémentation est effectuée à l'aide d'un cœur du processeur MicroBlaze qui gère le processus de contrôle et exécute l'algorithme. Afin de développer une implémentation efficace, des calculs en virgule flottante sont effectués en utilisant l'unité de virgule flottante (FPU) du cœur du processeur Microblaze. Le système est implémenté sur carte FPGA Spartan-3 de Xilinx. Elle contient 200 mille portes logiques cadencées par un oscillateur à 50 MHz, avec une mémoire externe asynchrone SRAM de 1 Mio. Le système comprend également un bus périphérique sur puce (OPB). À ce titre, la solution finale du système de navigation automobile devrait avoir des caractéristiques telles qu'une faible consommation de puissance, un poids léger, une capacité de traitement en temps réel ainsi qu'un petit espace occupé sur puce. D'un point de vue développement, l'utilisation du langage C et d'un cœur de processeur fonctionnant sur FPGA donne à l'utilisateur une plateforme flexible pour tout prototypage d'applications. Les simulations montrent qu'une implémentation purement logicielle de l'algorithme de la boucle fermée du filtrage de Kalman décentralisé sur une plateforme embarquée qui utilise les nombres virgule-flottante à simple précision, peut produire des résultats acceptables. Ceci est conforme aux résultats obtenus sur une plateforme d'un ordinateur de bureau qui utilise les nombres virgule-flottante à double précision. Dans un premier temps, le code du filtrage de Kalman est exécuté à partir d'une mémoire externe SRAM de 1 Mio, soutenue par une mémoire cache de données de 8Kio et une cache d'instructions de 4 Kio. Puis, le même code est lancé à partir du bloc RAM sur puce, à grande vitesse, de 64 Kio. Dans les deux configurations mémoire, les fréquences d'échantillonnage maximales pour lesquelles le code peut être exécuté sont de 80 Hz (période de 12,5 ms) et 119 Hz (période de 8,4 ms), respectivement, tandis que les capteurs fournissent les données à 75 Hz Les même deux configurations de mémoire sont employées dans l'exécution de l'algorithme de débruitage par ondelettes avec 5 niveaux de décomposition, de reconstruction et seuillage non linéaire à chaque niveau. Sur l'accéléromètre et le gyro, les données brutes sont fournies en temps réel en utilisant un mode de fenêtre de non-chevauchement, avec une longueur de fenêtre de 75 échantillons. Les latences d'exécution dans les deux cas sont 5,47 ms et 1,96 ms pour les deux configurations de mémoire précédemment citées, respectivement. En outre, l'analyse temporelle de !'après synthèse des deux configurations matérielles, reporte des apports de 26% et 66% respectivement. Puisque le système fonctionne à 50 MHz, il y a ainsi une marge de manœuvre disponible intéressante pour des perfectionnements algorithmiques. Ainsi, en utilisant la combinaison d'une plate-forme peu coûteuse, une approche flexible de développement et une solution en temps réel, l'exécution montrée dans ce mémoire démontre que la synthèse d'une solution finale de navigation véhiculaire fonctionnant en temps réel, complètement fonctionnelle, panne-résiliente, peu coûteuse est faisable. -------------------ABSTRACT Over the years, due to the increasing road density and intensive road traffic, the need for automobile navigation has increased not just for providing location awareness but also for enhancing vehicular control, safety and overall performance. The declining cost of Global Positioning System (GPS) receivers has rendered them attractive for automobile navigation applications. GPS provides position and velocity information to automobile users. As a result, most of the present civilian automobile navigation devices are based on GPS technology. However, in the event of GPS signal loss, blockage by foliage, concrete overpasses, dense urban developments viz. tall buildings or tunnels and attenuation, these devices fail to perform accurately. An alternative to GPS that can be used in automobile navigation is an Inertial Navigation System (INS). INS is a self-contained system that is not affected by external disturbances. It comprises inertial sensors such as three gyroscopes and three accelerometers. Although low-grade, low-cost INS performance deteriorates in the long run as they suffer from accumulated errors, they can provide adequate navigational solution for short periods of time. An integrated GPS/INS system therefore has the potential to provide better positional information over short and long intervals. The main objective of this research was to implement a real-time navigation system solution on a low cost embedded platform so that it can be used as a design framework and reference for similar embedded applications. An integrated GPS/INS system with closed loop decentralized Kalman filtering technique is designed using trajectory data from low-cost GPS, accelerometer and gyroscope sensors. A data pre-processing technique based on a wavelet de-noising algorithm is implemented. It uses up to five levels of de-composition and reconstruction with non-linear thresholding on each level. The design is described in software which consists of an embedded microprocessor namely MicroBlaze that manages the control process and executes the algorithm. In order to develop an efficient implementation, floating-point computations are carried out using the floating point unit (FPU) of MicroBlaze soft core processor. The system is implemented on a Xilinx Spartan-3 Field Programmable Gate Array (FPGA) containing 200 thousand gates clocked by an onboard oscillator operating at 50 MHz, with an external asynchronous SRAM memory of 1 MiB. The system also includes the IBM CoreConnect On-Chip Peripheral Bus (OPB). As such the final solution for vehicle navigation system is expected to have features like low power consumption, light weight, real-time processing capability and small chip area. From a development point of view, the combination of the standard C programming language and a soft processor running on an FPGA gives the user a powerful yet flexible platform for any application prototyping. Results show that a purely software implementation of the decentralized closed loop Kalman filter algorithm embedded platform that uses single precision floating point numbers can produce acceptable results relative to those obtained from a desktop PC platform that uses double precision floating point numbers. At first, the Kalman filter code is executed from a 1 MiB external SRAM supported by 8KiB of data cache and 4IUB of instruction cache. Then, the same code is run from high speed 64ICiB on-chip Block RAM. In the two memory configurations, the maximum sampling frequencies at which the code can be executed are 80 Hz (period of 12.5 ms) and 119 Hz (period of 8.4 ms) respectively, while accelerometer and gyroscope sensors provide data at 75 Hz. The same two memory configurations are employed in executing a wavelet de-noising algorithm with 5 levels of de-composition, reconstruction and non linear thresholding on each level. Accelerometer and gyroscope raw data are processed in real-time using non-overlapping windows of 75 samples. The execution latencies in the two cases are found to be 5.47 ms and 1.96 ms respectively. Additionally, from the post synthesis timing analyses, the critical frequencies for the two hardware configurations were 63.3 MHz and 83.2 MHz. an enhancement of 26% and 66% respectively. Since the system operates at 50 MHz, there is thus an interesting processing margin available for further algorithmic enhancements. Thus, by employing the combination of a low cost embedded platform, a flexible development approach and a real-time solution, the implementation shown in this thesis demonstrates that synthesizing a completely functional low-cost, outage-resilient, real-time navigation solution for automotive applications is feasible. -------------CONTENT Automobile Navigation -- Global Positioning System -- Navigation Frame -- Earth Models -- Attitude Representations -- Inertial Navigation System -- IMU Sensor Errors -- 2D INS Mechanization Equations -- 3D INS Mechanization Equations -- INS Error Equations -- GPS/INS data fusion using KF -- IMU data prepocessing using Wavelet De-noising -- Hardware/Equipment Setup -- Embedded Platform -- Hardware Platform Development -- Software Coding -- Software Design Issues -- Navigation Solution using MicroBlaze -- Timing Measurements

On Improving the Accuracy and Reliability of GPS/INS-Based Direct Sensor Georeferencing

Due to the complementary error characteristics of the Global Positioning System (GPS) and Inertial Navigation System (INS), their integration has become a core positioning component, providing high-accuracy direct sensor georeferencing for multi-sensor mobile mapping systems. Despite significant progress over the last decade, there is still a room for improvements of the georeferencing performance using specialized algorithmic approaches. The techniques considered in this dissertation include: (1) improved single-epoch GPS positioning method supporting network mode, as compared to the traditional real-time kinematic techniques using on-the-fly ambiguity resolution in a single-baseline mode; (2) customized random error modeling of inertial sensors; (3) wavelet-based signal denoising, specially for low-accuracy high-noise Micro-Electro-Mechanical Systems (MEMS) inertial sensors; (4) nonlinear filters, namely the Unscented Kalman Filter (UKF) and the Particle Filter (PF), proposed as alternatives to the commonly used traditional Extended Kalman Filter (EKF). The network-based single-epoch positioning technique offers a better way to calibrate the inertial sensor, and then to achieve a fast, reliable and accurate navigation solution. Such an implementation provides a centimeter-level positioning accuracy independently on the baseline length. The advanced sensor error identification using the Allan Variance and Power Spectral Density (PSD) methods, combined with a wavelet-based signal de-noising technique, assures reliable and better description of the error characteristics, customized for each inertial sensor. These, in turn, lead to a more reliable and consistent position and orientation accuracy, even for the low-cost inertial sensors. With the aid of the wavelet de-noising technique and the customized error model, around 30 percent positioning accuracy improvement can be found, as compared to the solution using raw inertial measurements with the default manufacturer’s error models. The alternative filters, UKF and PF, provide more advanced data fusion techniques and allow the tolerance of larger initial alignment errors. They handle the unknown nonlinear dynamics better, in comparison to EKF, resulting in a more reliable and accurate integrated system. For the high-end inertial sensors, they provide only a slightly better performance in terms of the tolerance to the losses of GPS lock and orientation convergence speed, whereas the performance improvements are more pronounced for the low-cost inertial sensors

Off-line evaluation of indoor positioning systems in different scenarios: the experiences from IPIN 2020 competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3. Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612. Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ” Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018. Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026. Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091. Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190. Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU). Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762. Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202. Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001