1,347 research outputs found
Multi-Sensor Fusion for Underwater Vehicle Localization by Augmentation of RBF Neural Network and Error-State Kalman Filter
The Kalman filter variants extended Kalman filter (EKF) and error-state Kalman filter (ESKF) are widely used in underwater multi-sensor fusion applications for localization and navigation. Since these filters are designed by employing first-order Taylor series approximation in the error covariance matrix, they result in a decrease in estimation accuracy under high nonlinearity. In order to address this problem, we proposed a novel multi-sensor fusion algorithm for underwater vehicle localization that improves state estimation by augmentation of the radial basis function (RBF) neural network with ESKF. In the proposed algorithm, the RBF neural network is utilized to compensate the lack of ESKF performance by improving the innovation error term. The weights and centers of the RBF neural network are designed by minimizing the estimation mean square error (MSE) using the steepest descent optimization approach. To test the performance, the proposed RBF-augmented ESKF multi-sensor fusion was compared with the conventional ESKF under three different realistic scenarios using Monte Carlo simulations. We found that our proposed method provides better navigation and localization results despite high nonlinearity, modeling uncertainty, and external disturbances.This research was partially funded by the Campus de Excelencia Internacional Andalucia Tech, University of Malaga, Malaga, Spain. Partial funding for open access charge: Universidad de MĂĄlag
Vision-based localization methods under GPS-denied conditions
This paper reviews vision-based localization methods in GPS-denied
environments and classifies the mainstream methods into Relative Vision
Localization (RVL) and Absolute Vision Localization (AVL). For RVL, we discuss
the broad application of optical flow in feature extraction-based Visual
Odometry (VO) solutions and introduce advanced optical flow estimation methods.
For AVL, we review recent advances in Visual Simultaneous Localization and
Mapping (VSLAM) techniques, from optimization-based methods to Extended Kalman
Filter (EKF) based methods. We also introduce the application of offline map
registration and lane vision detection schemes to achieve Absolute Visual
Localization. This paper compares the performance and applications of
mainstream methods for visual localization and provides suggestions for future
studies.Comment: 32 pages, 15 figure
GA-SVR and pseudo-position-aided GPS/INS integration during GPS outage
The performance of Global Positioning System and Inertial Navigation System (GPS/INS) integrated navigation is reduced when GPS is blocked. This paper proposes an algorithm to overcome the condition where GPS is unavailable. Together with a parameter-optimised Genetic Algorithm (GA), a Support Vector Regression (SVR) algorithm is used to construct the mapping function between the specific force, angular rate increments of INS measurements and the increments of the GPS position. During GPS outages, the real-time pseudo-GPS position is predicted with the mapping function, and the corresponding covariance matrix is estimated by an improved adaptive filtering algorithm. A GPS/INS integration scheme is demonstrated where the vehicle travels along a straight line and around a curve, with respect to both low-speed-stable and high-speed-unstable navigation platforms. The results show that the proposed algorithm provides a better performance when GPS is unavailable
Collaborative navigation as a solution for PNT applications in GNSS challenged environments: report on field trials of a joint FIG / IAG working group
PNT stands for Positioning, Navigation, and Timing. Space-based PNT refers to the capabilities enabled by GNSS, and enhanced by Ground and Space-based Augmentation Systems (GBAS and SBAS), which provide position, velocity, and timing information to an unlimited number of users around the world, allowing every user to operate in the same reference system and timing standard. Such information has become increasingly critical to the security, safety, prosperity, and overall qualityof-life of many citizens. As a result, space-based PNT is now widely recognized as an essential element of the global information infrastructure. This paper discusses the importance of the availability and continuity of PNT information, whose application, scope and significance have exploded in the past 10â15 years. A paradigm shift in the navigation solution has been observed in recent years. It has been manifested by an evolution from traditional single sensor-based solutions, to multiple sensor-based solutions and ultimately to collaborative navigation and layered sensing, using non-traditional sensors and techniques â so called signals of opportunity. A joint working group under the auspices of the International Federation of Surveyors (FIG) and the International Association of Geodesy (IAG), entitled âUbiquitous Positioning Systemsâ investigated the use of Collaborative Positioning (CP) through several field trials over the past four years. In this paper, the concept of CP is discussed in detail and selected results of these experiments are presented. It is demonstrated here, that CP is a viable solution if a ânetworkâ or âneighbourhoodâ of users is to be positionedâ/ânavigated together, as it increases the accuracy, integrity, availability, and continuity of the PNT information for all users
Robust Modular Feature-Based Terrain-Aided Visual Navigation and Mapping
The visual feature-based Terrain-Aided Navigation (TAN) system presented in this thesis addresses the problem of constraining inertial drift introduced into the location estimate of Unmanned Aerial Vehicles (UAVs) in GPS-denied environment. The presented TAN system utilises salient visual features representing semantic or human-interpretable objects (roads, forest and water boundaries) from onboard aerial imagery and associates them to a database of reference features created a-priori, through application of the same feature detection algorithms to satellite imagery. Correlation of the detected features with the reference features via a series of the robust data association steps allows a localisation solution to be achieved with a finite absolute bound precision defined by the certainty of the reference dataset. The feature-based Visual Navigation System (VNS) presented in this thesis was originally developed for a navigation application using simulated multi-year satellite image datasets. The extension of the system application into the mapping domain, in turn, has been based on the real (not simulated) flight data and imagery. In the mapping study the full potential of the system, being a versatile tool for enhancing the accuracy of the information derived from the aerial imagery has been demonstrated. Not only have the visual features, such as road networks, shorelines and water bodies, been used to obtain a position âfixâ, they have also been used in reverse for accurate mapping of vehicles detected on the roads into an inertial space with improved precision. Combined correction of the geo-coding errors and improved aircraft localisation formed a robust solution to the defense mapping application. A system of the proposed design will provide a complete independent navigation solution to an autonomous UAV and additionally give it object tracking capability
Multisensor navigation systems: a remedy for GNSS vulnerabilities?
Space-based positioning, navigation, and timing (PNT) technologies, such as the global navigation satellite systems (GNSS) provide position, velocity, and timing information to an unlimited number of users around the world. In recent years, PNT information has become increasingly critical to the security, safety, and prosperity of the World's population, and is now widely recognized as an essential element of the global information infrastructure. Due to its vulnerabilities and line-of-sight requirements, GNSS alone is unable to provide PNT with the required levels of integrity, accuracy, continuity, and reliability. A multisensor navigation approach offers an effective augmentation in GNSS-challenged environments that holds a promise of delivering robust and resilient PNT. Traditionally, sensors such as inertial measurement units (IMUs), barometers, magnetometers, odometers, and digital compasses, have been used. However, recent trends have largely focused on image-based, terrain-based and collaborative navigation to recover the user location. This paper offers a review of the technological advances that have taken place in PNT over the last two decades, and discusses various hybridizations of multisensory systems, building upon the fundamental GNSS/IMU integration. The most important conclusion of this study is that in order to meet the challenging goals of delivering continuous, accurate and robust PNT to the ever-growing numbers of users, the hybridization of a suite of different PNT solutions is required
Intelligent Sensor Positioning and Orientation Through Constructive Neural Network-Embedded INS/GPS Integration Algorithms
Mobile mapping systems have been widely applied for acquiring spatial information in applications such as spatial information systems and 3D city models. Nowadays the most common technologies used for positioning and orientation of a mobile mapping system include a Global Positioning System (GPS) as the major positioning sensor and an Inertial Navigation System (INS) as the major orientation sensor. In the classical approach, the limitations of the Kalman Filter (KF) method and the overall price of multi-sensor systems have limited the popularization of most land-based mobile mapping applications. Although intelligent sensor positioning and orientation schemes consisting of Multi-layer Feed-forward Neural Networks (MFNNs), one of the most famous Artificial Neural Networks (ANNs), and KF/smoothers, have been proposed in order to enhance the performance of low cost Micro Electro Mechanical System (MEMS) INS/GPS integrated systems, the automation of the MFNN applied has not proven as easy as initially expected. Therefore, this study not only addresses the problems of insufficient automation in the conventional methodology that has been applied in MFNN-KF/smoother algorithms for INS/GPS integrated systems proposed in previous studies, but also exploits and analyzes the idea of developing alternative intelligent sensor positioning and orientation schemes that integrate various sensors in more automatic ways. The proposed schemes are implemented using one of the most famous constructive neural networksâthe Cascade Correlation Neural Network (CCNNs)âto overcome the limitations of conventional techniques based on KF/smoother algorithms as well as previously developed MFNN-smoother schemes. The CCNNs applied also have the advantage of a more flexible topology compared to MFNNs. Based on the experimental data utilized the preliminary results presented in this article illustrate the effectiveness of the proposed schemes compared to smoother algorithms as well as the MFNN-smoother schemes
The Estimation Methods for an Integrated INS/GPS UXO Geolocation System
This work was supported by a project funded by the US Army Corps of Engineers,
Strategic Environment Research and Development Program, contract number W912HQ-
08-C-0044.This report was also submitted to the Graduate School of the Ohio State
University in partial fulfillment of the PhD degree in Geodetic Science.Unexploded ordnance (UXO) is the explosive weapons such as mines, bombs, bullets,
shells and grenades that failed to explode when they were employed. In North America,
especially in the US, the UXO is the result of weapon system testing and troop training
by the DOD. The traditional UXO detection method employs metal detectors which
measure distorted signals of local magnetic fields. Based on detected magnetic signals,
holes are dug to remove buried UXO. However, the detection and remediation of UXO
contaminated sites using the traditional methods are extremely inefficient in that it is
difficult to distinguish the buried UXO from the noise of geologic magnetic sources or
anthropic clutter items. The reliable discrimination performance of UXO detection
system depends on the employed sensor technology as well as on the data processing
methods that invert the collected data to infer the UXO. The detection systems require
very accurate positioning (or geolocation) of the detection units to detect and discriminate
the candidate UXO from the non-hazardous clutter, greater position and orientation
precision because the inversion of magnetic or EMI data relies on their precise relative
locations, orientation, and depth. The requirements of position accuracy for MEC
geolocation and characterization using typical state-of-the-art detection instrumentation
are classified according to levels of accuracy outlined in: the screening level with position
tolerance of 0.5 m (as standard deviation), area mapping (less than 0.05 m), and
characterize and discriminate level of accuracy (less than 0.02m).
The primary geolocation system is considered as a dual-frequency GPS integrated with a
three dimensional inertial measurement unit (IMU); INS/GPS system. Selecting the
appropriate estimation method has been the key problem to obtain highly precise
geolocation of INS/GPS system for the UXO detection performance in dynamic
environments. For this purpose, the Extended Kalman Filter (EKF) has been used as the
conventional algorithm for the optimal integration of INS/GPS system. However, the
newly introduced non-linear based filters can deal with the non-linear nature of the
positioning dynamics as well as the non-Gaussian statistics for the instrument errors, and
the non-linear based estimation methods (filtering/smoothing) have been developed and
proposed. Therefore, this study focused on the optimal estimation methods for the
highly precise geolocation of INS/GPS system using simulations and analyses of two
Laboratory tests (cart-based and handheld geolocation system).
First, the non-linear based filters (UKF and UKF) have been shown to yield superior
performance than the EKF in various specific simulation tests which are designed similar
to the UXO geolocation environment (highly dynamic and small area). The UKF yields
50% improvement in the position accuracy over the EKF particularly in the curved
sections (medium-grade IMUs case). The UKF also performed significantly better than
EKF and shows comparable improvement over the UKF when the IMU noise probability
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density function is symmetric and non-symmetric. Also, since the UXO detection
survey does not require the real-time operations, each of the developed filters was
modified to accommodate the standard Rauch-Tung-Striebel (RTS) smoothing algorithms.
The smoothing methods are applied to the typical UXO detection trajectory; the position
error was reduced significantly using a minimal number of control points. Finally, these
simulation tests confirmed that tactical-grade IMUs (e.g. HG1700 or HG1900) are
required to bridge gaps of high-accuracy ranging solution systems longer than 1 second.
Second, these result of the simulation tests were validated from the laboratory tests using
navigation-grade and medium-grade accuracy IMUs. To overcome inaccurate a priori
knowledge of process noise of the system, the adaptive filtering methods have been
applied to the EKF and UKF and they are called the AEKS and AUKS. The neural
network aided adaptive nonlinear filtering/smoothing methods (NN-EKS and NN-UKS)
which are augmented with RTS smoothing method were compared with the AEKS and
AUKS. Each neural network-aided, adaptive filter/smoother improved the position
accuracy in both straight and curved sections. The navigation grade IMU (H764G) can
achieve the area mapping level of accuracy when the gap of control points is about 8
seconds. The medium grade IMUs (HG1700 and HG1900) with NN-AUKS can
maintain less than 10cm under the same conditions as above. Also, the neural network
aiding can decrease the difference of position error between the straight and the curved
section. Third, in the previous simulation test, the UPF performed better than the other
filters. However since the UPF needs a large number of samples to represent the a
posteriori statistics in high-dimensional space, the RBPF can be used as an alternative to
avoid the inefficiency of particle filter. The RBPF is tailored to precise geolocation for
UXO detection using IMU/GPS system and yielded improved estimation results with a
small number of samples. The handheld geolocation system using HG1900 with a
nonlinear filter-based smoother can achieve the discrimination level of accuracy if the
update rate of control points is less than 0.5Hz and 1Hz for the sweep and swing
respectively. Also, the sweep operation is more preferred than the swing motion
because the position accuracy of the sweep test was better than that of the swing test
Innovative Solutions for Navigation and Mission Management of Unmanned Aircraft Systems
The last decades have witnessed a significant increase in Unmanned Aircraft Systems (UAS) of all shapes and sizes. UAS are finding many new applications in supporting several human activities, offering solutions to many dirty, dull, and dangerous missions, carried out by military and civilian users. However, limited access to the airspace is the principal barrier to the realization of the full potential that can be derived from UAS capabilities. The aim of this thesis is to support the safe integration of UAS operations, taking into account both the user's requirements and flight regulations. The main technical and operational issues, considered among the principal inhibitors to the integration and wide-spread acceptance of UAS, are identified and two solutions for safe UAS operations are proposed: A. Improving navigation performance of UAS by exploiting low-cost sensors. To enhance the performance of the low-cost and light-weight integrated navigation system based on Global Navigation Satellite System (GNSS) and Micro Electro-Mechanical Systems (MEMS) inertial sensors, an efficient calibration method for MEMS inertial sensors is required. Two solutions are proposed: 1) The innovative Thermal Compensated Zero Velocity Update (TCZUPT) filter, which embeds the compensation of thermal effect on bias in the filter itself and uses Back-Propagation Neural Networks to build the calibration function. Experimental results show that the TCZUPT filter is faster than the traditional ZUPT filter in mapping significant bias variations and presents better performance in the overall testing period. Moreover, no calibration pre-processing stage is required to keep measurement drift under control, improving the accuracy, reliability, and maintainability of the processing software; 2) A redundant configuration of consumer grade inertial sensors to obtain a self-calibration of typical inertial sensors biases. The result is a significant reduction of uncertainty in attitude determination. In conclusion, both methods improve dead-reckoning performance for handling intermittent GNSS coverage.
B. Proposing novel solutions for mission management to support the Unmanned Traffic Management (UTM) system in monitoring and coordinating the operations of a large number of UAS. Two solutions are proposed: 1) A trajectory prediction tool for small UAS, based on Learning Vector Quantization (LVQ) Neural Networks. By exploiting flight data collected when the UAS executes a pre-assigned flight path, the tool is able to predict the time taken to fly generic trajectory elements. Moreover, being self-adaptive in constructing a mathematical model, LVQ Neural Networks allow creating different models for the different UAS types in several environmental conditions; 2) A software tool aimed at supporting standardized procedures for decision-making process to identify UAS/payload configurations suitable for any type of mission that can be authorized standing flight regulations. The proposed methods improve the management and safe operation of large-scale UAS missions, speeding up the flight authorization process by the UTM system and supporting the increasing level of autonomy in UAS operations
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