Robust localization with wearable sensors

Measuring physical movements of humans and understanding human behaviour is useful in a variety of areas and disciplines. Human inertial tracking is a method that can be leveraged for monitoring complex actions that emerge from interactions between human actors and their environment. An accurate estimation of motion trajectories can support new approaches to pedestrian navigation, emergency rescue, athlete management, and medicine. However, tracking with wearable inertial sensors has several problems that need to be overcome, such as the low accuracy of consumer-grade inertial measurement units (IMUs), the error accumulation problem in long-term tracking, and the artefacts generated by movements that are less common. This thesis focusses on measuring human movements with wearable head-mounted sensors to accurately estimate the physical location of a person over time. The research consisted of (i) providing an overview of the current state of research for inertial tracking with wearable sensors, (ii) investigating the performance of new tracking algorithms that combine sensor fusion and data-driven machine learning, (iii) eliminating the effect of random head motion during tracking, (iv) creating robust long-term tracking systems with a Bayesian neural network and sequential Monte Carlo method, and (v) verifying that the system can be applied with changing modes of behaviour, defined as natural transitions from walking to running and vice versa. This research introduces a new system for inertial tracking with head-mounted sensors (which can be placed in, e.g. helmets, caps, or glasses). This technology can be used for long-term positional tracking to explore complex behaviours

Design and evaluation of an integrated GPS/INS system for shallow-water AUV Navigation

The major problem addressed by this research is the large and/or expensive equipment required by a conventional navigation system to accurately determine the position of an Autonomous Underwater Vehicle (AUV) during all phases of an underwater search or mapping mission. The approach taken was to prototype an integrated navigation system which combines Global Positioning System (OPS) and Inertial Measurement Unit (IMU), waterspeed and heading information using Kalman filtering techniques. Actual implementation was preceded by a computer simulation to test where the unit would fit into a larger hardware and software hierarchy of an AUV. The system was then evaluated in experiments which began with land based cart tests and progressed to open water trials where the unit was placed in a towed body behind a boat and alternately submerged and surfaced to provide periodic OPS updates to the Inertial Navigation System (INS). Test results and qualitative error estimates indicate that submerged navigation accuracy comparable to that of differential OPS may be attainable for periods of 30 seconds or more with low cost components of a small physical size.http://archive.org/details/designndevaluati1094535102NANAU.S. Navy (U.S.N.) authors

AN INTELLIGENT NAVIGATION SYSTEM FOR AN AUTONOMOUS UNDERWATER VEHICLE

The work in this thesis concerns with the development of a novel multisensor data fusion (MSDF) technique, which combines synergistically Kalman filtering, fuzzy logic and genetic algorithm approaches, aimed to enhance the accuracy of an autonomous underwater vehicle (AUV) navigation system, formed by an integration of global positioning system and inertial navigation system (GPS/INS). The Kalman filter has been a popular method for integrating the data produced by the GPS and INS to provide optimal estimates of AUVs position and attitude. In this thesis, a sequential use of a linear Kalman filter and extended Kalman filter is proposed. The former is used to fuse the data from a variety of INS sensors whose output is used as an input to the later where integration with GPS data takes place. The use of an adaptation scheme based on fuzzy logic approaches to cope with the divergence problem caused by the insufficiently known a priori filter statistics is also explored. The choice of fuzzy membership functions for the adaptation scheme is first carried out using a heuristic approach. Single objective and multiobjective genetic algorithm techniques are then used to optimize the parameters of the membership functions with respect to a certain performance criteria in order to improve the overall accuracy of the integrated navigation system. Results are presented that show that the proposed algorithms can provide a significant improvement in the overall navigation performance of an autonomous underwater vehicle navigation. The proposed technique is known to be the first method used in relation to AUV navigation technology and is thus considered as a major contribution thereof.J&S Marine Ltd., Qinetiq, Subsea 7 and South West Water PL

Guidance and control of an autonomous underwater vehicle

Merged with duplicate record 10026.1/856 on 07.03.2017 by CS (TIS)A cooperative project between the Universities of Plymouth and Cranfield was aimed at designing and developing an autonomous underwater vehicle named Hammerhead. The work presented herein is to formulate an advance guidance and control system and to implement it in the Hammerhead. This involves the description of Hammerhead hardware from a control system perspective. In addition to the control system, an intelligent navigation scheme and a state of the art vision system is also developed. However, the development of these submodules is out of the scope of this thesis. To model an underwater vehicle, the traditional way is to acquire painstaking mathematical models based on laws of physics and then simplify and linearise the models to some operating point. One of the principal novelties of this research is the use of system identification techniques on actual vehicle data obtained from full scale in water experiments. Two new guidance mechanisms have also been formulated for cruising type vehicles. The first is a modification of the proportional navigation guidance for missiles whilst the other is a hybrid law which is a combination of several guidance strategies employed during different phases of the Right. In addition to the modelling process and guidance systems, a number of robust control methodologies have been conceived for Hammerhead. A discrete time linear quadratic Gaussian with loop transfer recovery based autopilot is formulated and integrated with the conventional and more advance guidance laws proposed. A model predictive controller (MPC) has also been devised which is constructed using artificial intelligence techniques such as genetic algorithms (GA) and fuzzy logic. A GA is employed as an online optimization routine whilst fuzzy logic has been exploited as an objective function in an MPC framework. The GA-MPC autopilot has been implemented in Hammerhead in real time and results demonstrate excellent robustness despite the presence of disturbances and ever present modelling uncertainty. To the author's knowledge, this is the first successful application of a GA in real time optimization for controller tuning in the marine sector and thus the thesis makes an extremely novel and useful contribution to control system design in general. The controllers are also integrated with the proposed guidance laws and is also considered to be an invaluable contribution to knowledge. Moreover, the autopilots are used in conjunction with a vision based altitude information sensor and simulation results demonstrate the efficacy of the controllers to cope with uncertain altitude demands.J&S MARINE LTD., QINETIQ, SUBSEA 7 AND SOUTH WEST WATER PL

Contributions to automated realtime underwater navigation

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2012This dissertation presents three separate–but related–contributions to the art of underwater navigation. These methods may be used in postprocessing with a human in the loop, but the overarching goal is to enhance vehicle autonomy, so the emphasis is on automated approaches that can be used in realtime. The three research threads are: i) in situ navigation sensor alignment, ii) dead reckoning through the water column, and iii) model-driven delayed measurement fusion. Contributions to each of these areas have been demonstrated in simulation, with laboratory data, or in the field–some have been demonstrated in all three arenas. The solution to the in situ navigation sensor alignment problem is an asymptotically stable adaptive identifier formulated using rotors in Geometric Algebra. This identifier is applied to precisely estimate the unknown alignment between a gyrocompass and Doppler velocity log, with the goal of improving realtime dead reckoning navigation. Laboratory and field results show the identifier performs comparably to previously reported methods using rotation matrices, providing an alignment estimate that reduces the position residuals between dead reckoning and an external acoustic positioning system. The Geometric Algebra formulation also encourages a straightforward interpretation of the identifier as a proportional feedback regulator on the observable output error. Future applications of the identifier may include alignment between inertial, visual, and acoustic sensors. The ability to link the Global Positioning System at the surface to precision dead reckoning near the seafloor might enable new kinds of missions for autonomous underwater vehicles. This research introduces a method for dead reckoning through the water column using water current profile data collected by an onboard acoustic Doppler current profiler. Overlapping relative current profiles provide information to simultaneously estimate the vehicle velocity and local ocean current–the vehicle velocity is then integrated to estimate position. The method is applied to field data using online bin average, weighted least squares, and recursive least squares implementations. This demonstrates an autonomous navigation link between the surface and the seafloor without any dependence on a ship or external acoustic tracking systems. Finally, in many state estimation applications, delayed measurements present an interesting challenge. Underwater navigation is a particularly compelling case because of the relatively long delays inherent in all available position measurements. This research develops a flexible, model-driven approach to delayed measurement fusion in realtime Kalman filters. Using a priori estimates of delayed measurements as augmented states minimizes the computational cost of the delay treatment. Managing the augmented states with time-varying conditional process and measurement models ensures the approach works within the proven Kalman filter framework–without altering the filter structure or requiring any ad-hoc adjustments. The end result is a mathematically principled treatment of the delay that leads to more consistent estimates with lower error and uncertainty. Field results from dead reckoning aided by acoustic positioning systems demonstrate the applicability of this approach to real-world problems in underwater navigation.I have been financially supported by: the National Defense Science and Engineering Graduate (NDSEG) Fellowship administered by the American Society for Engineering Education, the Edwin A. Link Foundation Ocean Engineering and Instrumentation Fellowship, and WHOI Academic Programs office

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 Málag

Adaptive filtering applications to satellite navigation

PhDDifferential Global Navigation Satellite Systems employ the extended Kalman filter to estimate the reference position error. High accuracy integrated navigation systems have the ability to mix traditional inertial sensor outputs with navigation satellite based position information and can be used to develop high accuracy landing systems for aircraft. This thesis considers a host of estimation problems associated with aircraft navigation systems that currently rely on the extended Kalman filter and proposes to use a nonlinear estimation algorithm, the unscented Kalman filter (UKF) that does not rely on Jacobian linearisation. The objective is to develop high accuracy positioning algorithms to facilitate the use of GNSS or DGNSS for aircraft landing. Firstly, the position error in a typical satellite navigation problem depends on the accuracy of the orbital ephemeris. The thesis presents results for the prediction of the orbital ephemeris from a customised navigation satellite receiver's data message. The SDP4/SDP8 algorithms and suitable noise models are used to establish the measured data. Secondly, the differential station common mode position error not including the contribution due to errors in the ephemeris is usually estimated by employing an EKF. The thesis then considers the application of the UKF to the mixing problem, so as to facilitate the mixing of measurements made by either a GNSS or a DGNSS and a variety of low cost or high-precision INS sensors. Precise, adaptive UKFs and a suitable nonlinear propagation method are used to estimate the orbit ephemeris and the differential position and the navigation filter mixing errors. The results indicate the method is particularly suitable for estimating the orbit ephemeris of navigation satellites and the differential position and navigation filter mixing errors, thus facilitating interoperable DGNSS operation for aircraft landing

Entwicklung und Implementierung eines Peer-to-Peer Kalman Filters für Fußgänger- und Indoor-Navigation

Smartphones are an integral part of our society by now. They are used for messaging, searching the Internet, working on documents, and of course for navigation. Although smartphones are also used for car navigation their main area of application is pedestrian navigation. Almost all smartphones sold today comprise a GPS L1 receiver which provides position computation with accuracy between 1 and 10 m as long as the environment in beneficial, i.e. the line-of-sight to satellites is not obstructed by trees or high buildings. But this is often the case in areas where smartphones are used primarily for navigation. Users walk in narrow streets with high density, in city centers, enter, and leave buildings and the smartphone is not able to follow their movement because it loses satellite signals. The approach presented in this thesis addresses the problem to enable seamless navigation for the user independently of the current environment and based on cooperative positioning and inertial navigation. It is intended to realize location-based services in areas and buildings with limited or no access to satellite data and a large amount of users like e.g. shopping malls, city centers, airports, railway stations and similar environments. The idea of this concept was for a start based on cooperative positioning between users’ devices denoted here as peers moving within an area with only limited access to satellite signals at certain places (windows, doors) or no access at all. The devices are therefore not able to provide a position by means of satellite signals. Instead of deploying solutions based on infrastructure, surveying, and centralized computations like range measurements, individual signal strength, and similar approaches a decentralized concept was developed. This concept suggests that the smartphone automatically detects if no satellite signals are available and uses its already integrated inertial sensors like magnetic field sensor, accelerometer, and gyroscope for seamless navigation. Since the quality of those sensors is very low the accuracy of the position estimation decreases with each step of the user. To avoid a continuously growing bias between real position and estimated position an update has to be performed to stabilize the position estimate. This update is either provided by the computation of a position based on satellite signals or if signals are not available by the exchange of position data with another peer in the near vicinity using peer-to-peer ad-hoc networks. The received and the own position are processed in a Kalman Filter algorithm and the result is then used as new position estimate and new start position for further navigation based on inertial sensors. The here presented concept is therefore denoted as Peer-to-Peer Kalman Filter (P2PKF)

New Approach of Indoor and Outdoor Localization Systems

Accurate determination of the mobile position constitutes the basis of many new applications. This book provides a detailed account of wireless systems for positioning, signal processing, radio localization techniques (Time Difference Of Arrival), performances evaluation, and localization applications. The first section is dedicated to Satellite systems for positioning like GPS, GNSS. The second section addresses the localization applications using the wireless sensor networks. Some techniques are introduced for localization systems, especially for indoor positioning, such as Ultra Wide Band (UWB), WIFI. The last section is dedicated to Coupled GPS and other sensors. Some results of simulations, implementation and tests are given to help readers grasp the presented techniques. This is an ideal book for students, PhD students, academics and engineers in the field of Communication, localization & Signal Processing, especially in indoor and outdoor localization domains