Perancangan dan Aplikasi Tapis Kalman Diskrit Diperluas pada Sistem Navigasi Inersial Tipe Strapdown pada Roket RKX-200 dengan Korektor Magnetometer, Altimeter, dan GPS

Perancangan sistem navigasi menggunakan sensor- sensor inersial berupa akselerometer dan giroskop (strapdown) guna mendapatkan informasi berupa posisi, kecepatan dan orientasi wahana terbang. Kesalahan yang dapat timbul akibat proses integrasi dikoreksi menggunakan sensor altimeter untuk ketinggian, magnetometer untuk orientasi dan GPS  untuk posisi. Informasi yang diolah dari sensor inersia masih berupa raw data dan tidak menggunakan hasil algoritma dari VN- 100T sehingga data ini masih banyak mengandung bias. Oleh karena itu digunakan analisis kompensasi eror deterministik dan estimasi Kalman. Metoda Tapis Kalman diskrit diperluas tipe gandeng terbuka (loosely coupled) digunakan sebagai estimator kesalahan yang terjadi pada sensor navigasi secara umpan maju (feedforward). Pada penelitian ini, INS di rancang untuk aplikasi roket RKX-200 buatan LAPAN dengan kecepatan mencapai 200 km/jam atau ekuivalen dengan 55,6 m/s dimana parameter ini menjadi variabel disain dari sistem komputasi yang dirancang. Telah  dirancang algoritma sistem navigasi dengan kecepatan 100 data tiap detiknya, sehingga resolusi dari komputasi sebesar 0,5m. Estimasi Tapis Kalman yang dirancang telah berhasil memperbaiki kesalahan maksimal sebesar 67 meter atau mencapai 2373 kali lipat lebih baik dari proses INS tanpa Tapis Kalman. Uji dinamika yang dilakukan telah menunjukan bahwa respon Tapis Kalman sangat dipengaruhi oleh korektor orientasi yaitu magnetometer disaat terjadi gangguan acak medan magnet.Kata Kunci: Tapis Kalman, feedforward, loosely coupled, navigasi, roke

An innovative information fusion method with adaptive Kalman filter for integrated INS/GPS navigation of autonomous vehicles

Information fusion method of INS/GPS navigation system based on filtering technology is a research focus at present. In order to improve the precision of navigation information, a navigation technology based on Adaptive Kalman Filter with attenuation factor is proposed to restrain noise in this paper. The algorithm continuously updates the measurement noise variance and processes noise variance of the system by collecting the estimated and measured values, and this method can suppress white noise. Because a measured value closer to the current time would more accurately reflect the characteristics of the noise, an attenuation factor is introduced to increase the weight of the current value, in order to deal with the noise variance caused by environment disturbance. To validate the effectiveness of the proposed algorithm, a series of road tests are carried out in urban environment. The GPS and IMU data of the experiments were collected and processed by dSPACE and MATLAB/Simulink. Based on the test results, the accuracy of the proposed algorithm is 20% higher than that of a traditional Adaptive Kalman Filter. It also shows that the precision of the integrated navigation can be improved due to the reduction of the influence of environment noise

Step Characterization using Sensor Information Fusion and Machine Learning

A pedestrian inertial navigation system is typically used to suppress the Global Navigation Satellite System limitation to track persons in indoor or in dense environments. However, low- cost inertial systems provide huge location estimation errors due to sensors and pedestrian dead reckoning inherent characteristics. To suppress some of these errors we propose a system that uses two inertial measurement units spread in person’s body, which measurements are aggregated using learning algorithms that learn the gait behaviors. In this work we present our results on using different machine learning algorithms which are used to characterize the step according to its direction and length. This characterization is then used to adapt the navigation algorithm according to the performed classifications

Comparison between RGB and RGB-D cameras for supporting low-cost GNSS urban navigation

A pure GNSS navigation is often unreliable in urban areas because of the presence of obstructions, thus preventing a correct reception of the satellite signal. The bridging between GNSS outages, as well as the vehicle attitude reconstruction, can be recovered by using complementary information, such as visual data acquired by RGB-D or RGB cameras. In this work, the possibility of integrating low-cost GNSS and visual data by means of an extended Kalman filter has been investigated. The focus is on the comparison between the use of RGB-D or RGB cameras. In particular, a Microsoft Kinect device (second generation) and a mirrorless Canon EOS M RGB camera have been compared. The former is an interesting RGB-D camera because of its low-cost, easiness of use and raw data accessibility. The latter has been selected for the high-quality of the acquired images and for the possibility of mounting fixed focal length lenses with a lower weight and cost with respect to a reflex camera. The designed extended Kalman filter takes as input the GNSS-only trajectory and the relative orientation between subsequent pairs of images. Depending on the visual data acquisition system, the filter is different because RGB-D cameras acquire both RGB and depth data, allowing to solve the scale problem, which is instead typical of image-only solutions. The two systems and filtering approaches were assessed by ad-hoc experimental tests, showing that the use of a Kinect device for supporting a u-blox low-cost receiver led to a trajectory with a decimeter accuracy, that is 15% better than the one obtained when using the Canon EOS M camera

Positioning Based on Tightly Coupled Multiple Sensors: A Practical Implementation and Experimental Assessment

During the last decade, the number of applications for land transportation that depend on systems for accurate positioning has significantly increased. Unfortunately, systems based on low-cost global navigation satellite system (GNSS) components harshly suffer signal impairments due to the environment surrounding the antenna, but new designs based on deeper data fusion and on the combination of different signal processing techniques can overcome limitations without the introduction of expensive components. Supported by a complete mathematical model, this paper presents the design of a real-time positioning system that is based on the tight integration of extremely low-cost sensors and a consumer-grade global positioning system receiver. The design has been validated experimentally through a series of tests carried out in real scenarios. The performance of the new system is compared against a standalone GNSS receiver and survey-grade professional equipment. The results show that a carefully designed and constrained integration of low-cost sensors can have performance comparable to that of an expensive professional equipment

Towards self-powered sensing using nanogenerators for automotive systems

The final publication is available at Elsevier via https://dx.doi.org/10.1016/j.nanoen.2018.09.032 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/Harvesting energy from the working environment of vehicles is important for wirelessly monitoring their operation conditions and safety. This review aims at reporting different sensory and energy harvesting technologies developed for automotive and active safety systems. A few dominant sensing and power harvesting mechanisms in automotive systems are illustrated, then, triboelectric, piezoelectric and pyroelectric nanogenerators, and their potential for utilization in automotive systems are discussed considering their high power density, flexibility, different operating modes, and cost in comparison with other mechanisms. Various ground vehicles’ sensing mechanisms including position, thermal, pressure, chemical and gas composition, and pressure sensors are presented. A few novel types self-powered sensing mechanisms are presented for each of the abovementioned sensor categories using nanogenerators. The last section includes the automotive systems and subsystems, which have the potential to be used for energy harvesting, such as suspension and tires. The potential of nanogenerators for developing new self-powered sensors for automotive applications, which in the near future, will be an indispensable part of the active safety systems in production cars, is also discussed in this review article

Kernel-based fault diagnosis of inertial sensors using analytical redundancy

Kernel methods are able to exploit high-dimensional spaces for representational advantage, while only operating implicitly in such spaces, thus incurring none of the computational cost of doing so. They appear to have the potential to advance the state of the art in control and signal processing applications and are increasingly seeing adoption across these domains. Applications of kernel methods to fault detection and isolation (FDI) have been reported, but few in aerospace research, though they offer a promising way to perform or enhance fault detection. It is mostly in process monitoring, in the chemical processing industry for example, that these techniques have found broader application. This research work explores the use of kernel-based solutions in model-based fault diagnosis for aerospace systems. Specifically, it investigates the application of these techniques to the detection and isolation of IMU/INS sensor faults – a canonical open problem in the aerospace field. Kernel PCA, a kernelised non-linear extension of the well-known principal component analysis (PCA) algorithm, is implemented to tackle IMU fault monitoring. An isolation scheme is extrapolated based on the strong duality known to exist between probably the most widely practiced method of FDI in the aerospace domain – the parity space technique – and linear principal component analysis. The algorithm, termed partial kernel PCA, benefits from the isolation properties of the parity space method as well as the non-linear approximation ability of kernel PCA. Further, a number of unscented non-linear filters for FDI are implemented, equipped with data-driven transition models based on Gaussian processes - a non-parametric Bayesian kernel method. A distributed estimation architecture is proposed, which besides fault diagnosis can contemporaneously perform sensor fusion. It also allows for decoupling faulty sensors from the navigation solution

3D reconstruction and motion estimation using forward looking sonar

Autonomous Underwater Vehicles (AUVs) are increasingly used in different domains including archaeology, oil and gas industry, coral reef monitoring, harbour’s security, and mine countermeasure missions. As electromagnetic signals do not penetrate underwater environment, GPS signals cannot be used for AUV navigation, and optical cameras have very short range underwater which limits their use in most underwater environments. Motion estimation for AUVs is a critical requirement for successful vehicle recovery and meaningful data collection. Classical inertial sensors, usually used for AUV motion estimation, suffer from large drift error. On the other hand, accurate inertial sensors are very expensive which limits their deployment to costly AUVs. Furthermore, acoustic positioning systems (APS) used for AUV navigation require costly installation and calibration. Moreover, they have poor performance in terms of the inferred resolution. Underwater 3D imaging is another challenge in AUV industry as 3D information is increasingly demanded to accomplish different AUV missions. Different systems have been proposed for underwater 3D imaging, such as planar-array sonar and T-configured 3D sonar. While the former features good resolution in general, it is very expensive and requires huge computational power, the later is cheaper implementation but requires long time for full 3D scan even in short ranges. In this thesis, we aim to tackle AUV motion estimation and underwater 3D imaging by proposing relatively affordable methodologies and study different parameters affecting their performance. We introduce a new motion estimation framework for AUVs which relies on the successive acoustic images to infer AUV ego-motion. Also, we propose an Acoustic Stereo Imaging (ASI) system for underwater 3D reconstruction based on forward looking sonars; the proposed system features cheaper implementation than planar array sonars and solves the delay problem in T configured 3D sonars

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