OCP Based Online Multisensor Data Fusion for Autonomous Ground Vehicle

In this paper, online multisensor data fusion algorithm using CORBA event channel is proposed, in order to deal with simplifying problem in sensor registration and fusion for vehicle’s state estimation. The networked based navigation concept for Autonomous Ground Vehicle (AGV) using several sensors is presented. A simulation of various application scenarios are considered by choosing several parameters of UKF, i.e. weighting constant for sigma points and square root matrix. Normalized mean-square error (MSE) of Monte Carlo simulations are computed and reported in the simulation results. Furthermore, the middleware infrastructure based on Open Control Platform (OCP) to support the interconnection between the whole filter structures also reported

Survey on Recent Advances in Integrated GNSSs Towards Seamless Navigation Using Multi-Sensor Fusion Technology

During the past few decades, the presence of global navigation satellite systems (GNSSs) such as GPS, GLONASS, Beidou and Galileo has facilitated positioning, navigation and timing (PNT) for various outdoor applications. With the rapid increase in the number of orbiting satellites per GNSS, enhancements in the satellite-based augmentation systems (SBASs) such as EGNOS and WAAS, as well as commissioning new GNSS constellations, the PNT capabilities are maximized to reach new frontiers. Additionally, the recent developments in precise point positioning (PPP) and real time kinematic (RTK) algorithms have provided more feasibility to carrier-phase precision positioning solutions up to the third-dimensional localization. With the rapid growth of internet of things (IoT) applications, seamless navigation becomes very crucial for numerous PNT dependent applications especially in sensitive fields such as safety and industrial applications. Throughout the years, GNSSs have maintained sufficiently acceptable performance in PNT, in RTK and PPP applications however GNSS experienced major challenges in some complicated signal environments. In many scenarios, GNSS signal suffers deterioration due to multipath fading and attenuation in densely obscured environments that comprise stout obstructions. Recently, there has been a growing demand e.g. in the autonomous-things domain in adopting reliable systems that accurately estimate position, velocity and time (PVT) observables. Such demand in many applications also facilitates the retrieval of information about the six degrees of freedom (6-DOF - x, y, z, roll, pitch, and heading) movements of the target anchors. Numerous modern applications are regarded as beneficiaries of precise PNT solutions such as the unmanned aerial vehicles (UAV), the automatic guided vehicles (AGV) and the intelligent transportation system (ITS). Hence, multi-sensor fusion technology has become very vital in seamless navigation systems owing to its complementary capabilities to GNSSs. Fusion-based positioning in multi-sensor technology comprises the use of multiple sensors measurements for further refinement in addition to the primary GNSS, which results in high precision and less erroneous localization. Inertial navigation systems (INSs) and their inertial measurement units (IMUs) are the most commonly used technologies for augmenting GNSS in multi-sensor integrated systems. In this article, we survey the most recent literature on multi-sensor GNSS technology for seamless navigation. We provide an overall perspective for the advantages, the challenges and the recent developments of the fusion-based GNSS navigation realm as well as analyze the gap between scientific advances and commercial offerings. INS/GNSS and IMU/GNSS systems have proven to be very reliable in GNSS-denied environments where satellite signal degradation is at its peak, that is why both integrated systems are very abundant in the relevant literature. In addition, the light detection and ranging (LiDAR) systems are widely adopted in the literature for its capability to provide 6-DOF to mobile vehicles and autonomous robots. LiDARs are very accurate systems however they are not suitable for low-cost positioning due to the expensive initial costs. Moreover, several other techniques from the radio frequency (RF) spectrum are utilized as multi-sensor systems such as cellular networks, WiFi, ultra-wideband (UWB) and Bluetooth. The cellular-based systems are very suitable for outdoor navigation applications while WiFi-based, UWB-based and Bluetooth-based systems are efficient in indoor positioning systems (IPS). However, to achieve reliable PVT estimations in multi-sensor GNSS navigation, optimal algorithms should be developed to mitigate the estimation errors resulting from non-line-of-sight (NLOS) GNSS situations. Examples of the most commonly used algorithms for trilateration-based positioning are Kalman filters, weighted least square (WLS), particle filters (PF) and many other hybrid algorithms by mixing one or more algorithms together. In this paper, the reviewed articles under study and comparison are presented by highlighting their motivation, the methodology of implementation, the modelling utilized and the performed experiments. Then they are assessed with respect to the published results focusing on achieved accuracy, robustness and overall implementation cost-benefits as performance metrics. Our summarizing survey assesses the most promising, highly ranked and recent articles that comprise insights into the future of GNSS technology with multi-sensor fusion technique.©2021 The Authors. Published by ION.fi=vertaisarvioimaton|en=nonPeerReviewed

다중 센서 항법시스템을 위한 연합형 불변 확장칼만필터

학위논문(석사) -- 서울대학교대학원 : 공과대학 항공우주공학과, 2022.2. 박찬국.This thesis presents the federated invariant extended Kalman filter (IEKF) using multiple measurements. IEKF has superior estimation performance compared to EKF through the definition of state variables on matrix Lie group while using the framework of the EKF. The IEKF enables trajectory independent estimation when left- or right-invariant measurements are used with proper invariant error selection. As a result, the IEKF ensures the convergence and accuracy of estimation, even when the estimation error is large. Most IEKF studies assumed the use of single aiding measurement. However, navigation systems often use multiple aiding sensors to improve estimation performance in applications. When left- and right-invariant measurements are used simultaneously, implementing the LIEKF or RIEKF with a centralized filter structure causes some terms of the measurement matrix dependent on the current estimates, which results in IEKF losing its trajectory independent advantage. On the other hand, when a decentralized filter structure, especially a federated filter structure, is applied, the estimation becomes trajectory independent through separate update of each measurement in the local filters. This thesis proposes a fusion method of IEKF using the federated filter structure for simultaneous use of left- and right-invariant measurements. The performance of the proposed fusion method is validated through simulations. The error convergence and accuracy of the proposed method and the centralized IEKF are compared.본 논문에서는 다수의 보정 센서를 사용하는 항법 시스템을 위한 연합형 불변 확장 칼만필터의 구현을 제안한다. 불변 확장 칼만필터는 일반적인 확장 칼만필터의 프레임워크는 그대로 사용하면서 상태변수를 행렬 리 그룹 상에서 정의하여 확장 칼만필터 대비 우수한 추정 성능을 가진다. 좌불변 혹은 우불변 측정치를 사용할 때 이에 적합한 불변 오차 정의를 선택하여 구현한다면 궤적 독립적인 추정이 가능하다. 대부분의 불변 확장 칼만필터에 대한 연구들은 단일 보정 센서의 사용을 가정한다. 그런데 실제 적용에 있어, 항법 시스템은 추정 성능을 향상하기 위해 다수의 보정 센서를 사용하는 경우가 많다. 좌불변 측정치와 우불변 측정치가 모두 사용되는 상황이라면, 중앙집중형 좌불변 확장 칼만필터와 우불변 확장 칼만필터는 모두 추정치에 영향을 받는 측정치 행렬을 사용하게 된다. 이로 인해 불변 확장칼만필터가 갖는 가장 큰 장점인 궤적 독립 특성을 잃는다. 반면에 연합형 필터 구조를 사용하면 각 측정치에 할당된 국소 필터에서 적절한 필터로 각 측정치를 처리할 수 있다. 따라서 이 논문에서는 불변 확장 칼만필터의 연합형 구조 구현을 제안한다. 리 그룹의 성질을 고려하는 적절한 융합 방식을 사용한 구조를 제안하며, 그 성능을 시뮬레이션을 통해 확인한다. 제안한 방식과 중앙집중형 불변 확장 칼만필터를 수렴성과 추정 정확도의 관점에서 비교하였다.Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Objectives and contributions 3 Chapter 2 Related Works 5 2.1 Invariant extended Kalman filter (IEKF) 5 2.2 Federated filter 7 Chapter 3 Framework of invariant EKF 9 3.1 Mathematical preliminaries 9 3.2 States and model 10 3.2.1 Matrix Lie group states 10 3.2.2 Process model 12 3.2.3 Measurement model 15 3.2.4 Adjoint 16 3.3 IEKF for inertial navigation 17 3.3.1 IMU states and error states 17 3.3.2 Process model 20 3.3.3 Measurement model 22 3.3.4 Adjoint transformation 27 Chapter 4 IEKF Using Multiple Measurements 28 4.1 Centralized filter implementation 29 4.1.1 Centralized LIEKF 30 4.1.2 Centralized RIEKF 32 4.2 Federated filter implementation 34 4.2.1 Overall structure 34 4.2.2 Fusion process 39 4.3 Numerical simulations 40 4.3.1 Convergence test 43 4.3.2 Comparison of centralized IEKF and EKF 48 4.3.3 Comparison of IEKF and the proposed method 52 Chapter 5 Conclusion 60 5.1.1 Conclusion and summary 60 5.1.2 Future works 61 Bibliography 62 국문초록 68석

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

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

An intelligent navigation system for an unmanned surface vehicle

Merged with duplicate record 10026.1/2768 on 27.03.2017 by CS (TIS)A multi-disciplinary research project has been carried out at the University of Plymouth to design and develop an Unmanned Surface Vehicle (USV) named ýpringer. The work presented herein relates to formulation of a robust, reliable, accurate and adaptable navigation system to enable opringei to undertake various environmental monitoring tasks. Synergistically, sensor mathematical modelling, fuzzy logic, Multi-Sensor Data Fusion (MSDF), Multi-Model Adaptive Estimation (MMAE), fault adaptive data acquisition and an user interface system are combined to enhance the robustness and fault tolerance of the onboard navigation system. This thesis not only provides a holistic framework but also a concourse of computational techniques in the design of a fault tolerant navigation system. One of the principle novelties of this research is the use of various fuzzy logic based MSDF algorithms to provide an adaptive heading angle under various fault situations for Springer. This algorithm adapts the process noise covariance matrix ( Q) and measurement noise covariance matrix (R) in order to address one of the disadvantages of Kalman filtering. This algorithm has been implemented in Spi-inger in real time and results demonstrate excellent robustness qualities. In addition to the fuzzy logic based MSDF, a unique MMAE algorithm has been proposed in order to provide an alternative approach to enhance the fault tolerance of the heading angles for Springer. To the author's knowledge, the work presented in this thesis suggests a novel way forward in the development of autonomous navigation system design and, therefore, it is considered that the work constitutes a contribution to knowledge in this area of study. Also, there are a number of ways in which the work presented in this thesis can be extended to many other challenging domains.DEVONPORT MANAGEMENT LTD, J&S MARINE LTD AND SOUTH WEST WATER PL

Towards the development of an autonomous navigation system for unmanned vessels

This paper discusses the implementation of an intelligent navigation system for an autonomous unmanned surface vessel (USV). The focus is developing a multiple sensor data acquisition and fusion system to provide accurate and continuous information on positions, speeds and courses of the USV itself and also dynamic obstacles known as target ships (TSs). For USV’s autonomous navigation, a Global Positioning System (GPS) receiver, low-cost sensors for dead reckoning (DR) and various types of electronic compasses are employed; For TS’s localisation, the Automatic Identification System (AIS) information has been simulated to estimate and predict the positions of TSs over time. Simulations and practical trials are provided to demonstrate the effectiveness of the proposed system

AN INFORMATION THEORETIC APPROACH TO INTERACTING MULTIPLE MODEL ESTIMATION FOR AUTONOMOUS UNDERWATER VEHICLES

Accurate and robust autonomous underwater navigation (AUV) requires the fundamental task of position estimation in a variety of conditions. Additionally, the U.S. Navy would prefer to have systems that are not dependent on external beacon systems such as global positioning system (GPS), since they are subject to jamming and spoofing and can reduce operational effectiveness. Current methodologies such as Terrain-Aided Navigation (TAN) use exteroceptive imaging sensors for building a local reference position estimate and will not be useful when those sensors are out of range. What is needed are multiple navigation filters where each can be more effective depending on the mission conditions. This thesis investigates how to combine multiple navigation filters to provide a more robust AUV position estimate. The solution presented is to blend two different filtering methodologies utilizing an interacting multiple model (IMM) estimation approach based on an information theoretic framework. The first filter is a model-based Extended Kalman Filter (EKF) that is effective under dead reckoning (DR) conditions. The second is a Particle Filter approach for Active Terrain Aided Navigation (ATAN) that is appropriate when in sensor range. Using data collected at Lake Crescent, Washington, each of the navigation filters are developed with results and then we demonstrate how an IMM information theoretic approach can be used to blend approaches to improve position and orientation estimation.Lieutenant, United States NavyApproved for public release. Distribution is unlimited

The design of an autonomous maritime navigation system for unmanned surface vehicles

This paper presents the development of an autonomous maritime navigation system for unmanned surface vehicles (USVs). In the autonomous system various maritime navigational devices are connected to obtain necessary navigational information but with uncertainties. To improve signal accuracy as well as robustness, a novel multi-sensor data fusion algorithm is proposed and developed. Then, a new predictive path planning algorithm is employed to provide an advisory collision-free trajectory. Practical trials and computer based simulations are carried out to prove the effectiveness of the developed syste

Homography-Based State Estimation for Autonomous Exploration in Unknown Environments

This thesis presents the development of vision-based state estimation algorithms to enable a quadcopter UAV to navigate and explore a previously unknown GPS denied environment. These state estimation algorithms are based on tracked Speeded-Up Robust Features (SURF) points and the homography relationship that relates the camera motion to the locations of tracked planar feature points in the image plane. An extended Kalman filter implementation is developed to perform sensor fusion using measurements from an onboard inertial measurement unit (accelerometers and rate gyros) with vision-based measurements derived from the homography relationship. Therefore, the measurement update in the filter requires the processing of images from a monocular camera to detect and track planar feature points followed by the computation of homography parameters. The state estimation algorithms are designed to be independent of GPS since GPS can be unreliable or unavailable in many operational environments of interest such as urban environments. The state estimation algorithms are implemented using simulated data from a quadcopter UAV and then tested using post processed video and IMU data from flights of an autonomous quadcopter. The homography-based state estimation algorithm was effective, but accumulates drift errors over time due to the relativistic homography measurement of position