A scheme on indoor tracking of ship dynamic positioning based on distributed multi-sensor data fusion

Investigating the model ship dynamic positioning system by simulating the actual sea conditions in the laboratory can not only avoid the risks caused by the directly experiments on a true ship, but also reduce the costs. With the purpose of realizing the high accuracy control of the dynamic positioning, besides a high accuracy mathematical model of the ship, an important condition is that the position information provided by the position detection system must be accurate, reliable and continuous. The global positioning system (GPS) signal is restricted when the model ship dynamic positioning system is set indoors. This paper describes a novel scheme for ship target tracking based on the multi-sensor data fusion techniques. To improve the accuracy of indoor positioning and ship target tracking, the characteristics of many sensors are systematically analyzed, such as radar, difference global positioning system (DGPS) and ultrasonic sensors. Other important factors, including the indoor temperature, position and environment, are also taken into account to further optimize the performance. Combining the Kalman filter method, the time alignment method, the coordinate transformation method and the optimal fusion criterion method, the core algorithm of our framework employs the track correlation as the performance index of the optimal fusion. The experimental results indicate that our method outperforms the methods based on a single ultrasonic sensor. The maximum error between the estimated location and the real location is only 1.32 cm, which meets the standard for engineering applications

Communication-based UAV Swarm Missions

Unmanned aerial vehicles have developed rapidly in recent years due to technological advances. UAV technology can be applied to a wide range of applications in surveillance, rescue, agriculture and transport. The problems that can exist in these areas can be mitigated by combining clusters of drones with several technologies. For example, when a swarm of drones is under attack, it may not be able to obtain the position feedback provided by the Global Positioning System (GPS). This poses a new challenge for the UAV swarm to fulfill a specific mission. This thesis intends to use as few sensors as possible on the UAVs and to design the smallest possible information transfer between the UAVs to maintain the shape of the UAV formation in flight and to follow a predetermined trajectory. This thesis presents Extended Kalman Filter methods to navigate autonomously in a GPS-denied environment. The UAV formation control and distributed communication methods are also discussed and given in detail

Information Aided Navigation: A Review

The performance of inertial navigation systems is largely dependent on the stable flow of external measurements and information to guarantee continuous filter updates and bind the inertial solution drift. Platforms in different operational environments may be prevented at some point from receiving external measurements, thus exposing their navigation solution to drift. Over the years, a wide variety of works have been proposed to overcome this shortcoming, by exploiting knowledge of the system current conditions and turning it into an applicable source of information to update the navigation filter. This paper aims to provide an extensive survey of information aided navigation, broadly classified into direct, indirect, and model aiding. Each approach is described by the notable works that implemented its concept, use cases, relevant state updates, and their corresponding measurement models. By matching the appropriate constraint to a given scenario, one will be able to improve the navigation solution accuracy, compensate for the lost information, and uncover certain internal states, that would otherwise remain unobservable.Comment: 8 figures, 3 table

Feasibility Analysis of Non-electromagnetical Signals Collected via Thingsee Sensors for Indoor Positioning

Internet of Things (IoT) has significant impacts on wireless networking and communication technologies of modern times. Recently it has gained also attention in the field of indoor positioning and localization, both in research and industrial markets. IoT technologies enables access to the real time information about indoor environment which are collected through sensors. The sensor data is processed and analysed to understand the complexity of the indoor environment so that it can be used for making applications based on positioning. This thesis deals with some modern applications, challenges, key technologies and architectural overviews of Internet of Things including some recent works which were carried out based on electromagnetical and non-electromagnetical approaches. Then. a feasibility analysis is made for indoor positioning using non-electromagnetical sensor data which includes temperature, humidity, pressure and luminance. These sensors are also known as environmental sensors. An IoT development device named ‘Thingsee One’ was used where the environmental sensors were embedded in. The device was used for capturing environmental data from different locations inside a university building in Tampere, Finland. At first, Thingsee One device was configured for capturing temperature, humidity, pressure and luminance data from an indoor environment. Measurements were taken from different locations of the building, from first and second floor. Different times and weather condition were also taken into account during data capturing. Then the captured data has been analysed for identifying those positions through histograms and power maps. The results show that, the data captured by the sensors are highly dependent on time and weather which makes them rather inconsistent over the same position in different situations and time and therefore not likely candidates for positioning estimation

Bibliographic Review on Distributed Kalman Filtering

In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area

Context Detection, Categorization and Connectivity for Advanced Adaptive Integrated Navigation

Context is the environment that a navigation system operates in and the behaviour of its host vehicle or user. The type and quality of signals and environmental features available for positioning varies with the environment. For example, GNSS provides high-quality positioning in open environments, low-quality positioning in dense urban environments and no solution at all deep indoors. The behaviour of the host vehicle (or pedestrian) is also important. For example, pedestrian, car and train navigation all require different map-matching techniques, different motion constraints to limit inertial navigation error growth, and different dynamic models in a navigation filter [1]. A navigation system design should therefore be matched to its context. However, the context can change, particularly for devices, such as smartphones, which move between indoor and outdoor environments and can be stationary, on a pedestrian, or in a vehicle. For best performance, a navigation system should therefore be able to detect its operating context and adapt accordingly; this is context-adaptive positioning [1]. Previous work on context-adaptive navigation and positioning has focused on individual subsystems. For example, there has been substantial research into determining the motion type and sensor location for pedestrian dead reckoning using step detection [2-4]. Researchers have also begun to investigate context-adaptive (or cognitive) GNSS [5-7]. However, this paper considers context adaptation across an integrated navigation system as a whole. The paper addresses three aspects of context-adaptive integrated navigation: context detection, context categorization and context connectivity. It presents experimental results showing how GNSS C/N0 measurements, frequency-domain MEMS inertial sensor measurements and Wi-Fi signal availability could be used to detect both the environmental and behavioural contexts. It then looks at how context information could be shared across the different components of an integrated navigation system. Finally, the concept of context connectivity is introduced to improve the reliability of context detection. GNSS C/N0 measurement distributions, obtained using a smartphone, and Wi-Fi reception data collected over a range of indoor, urban and open environments will be compared to identify suitable features from which the environmental context may be derived. In an open environment, strong GNSS signals will be received from all directions. In an urban environment, fewer strong signals will be received and only from certain directions. Inside a building, nearly all GNSS signals will be much weaker than outside. Wi-Fi signals essentially vary with the environment in the opposite way to GNSS. Indoors, more access points (APs) can be received at higher signal strengths and there is greater variation in RSS. In urban environments, large numbers of APs can still be received, but at lower signal strengths [6]. Finally, in open environments, few APs, if any, will be received. Behavioural context is studied using an IMU. Although an Xsens MEMS IMU is used in this study, smartphone inertial sensors are also suitable. Pedestrian, car and train data has been collected under a range of different motion types and will be compared to identify context-dependent features. Early indications are that, as well as detecting motion, it is also possible to distinguish nominally-stationary IMUs that are placed in a car, on a person or on a table from the frequency spectra of the sensor measurements. The exchange of context information between subsystems in an integrated navigation system requires agreement on the definitions of those contexts. As different subsystems are often supplied by different organisations, it is desirable to standardize the context definitions across the whole navigation and positioning community. This paper therefore proposes a framework upon which a “context dictionary” could be constructed. Environmental and behavioural contexts are categorized separately and a hierarchy of attributes is proposed to enable some subsystems to work with highly specific context categories and others to work with broader categories. Finally, the concept of context connectivity is introduced. This is analogous to the road link connectivity used in map matching [8]. As context detection involves the matching of measurement data to stored context profiles, there will always be occurrences of false or ambiguous context identification. However, these may be minimized by using the fact that it is only practical to transition directly between certain pairs of contexts. For example, it is not normally possible to move directly from an airborne to an indoor environment as an aircraft must land first. Thus, the air and land contexts are connected, as are the land and indoor contexts, but the air and indoor contexts are not. Thus, by only permitting contexts that are connected to the previous context, false and ambiguous context detection is reduced. Robustness may be further enhanced by considering location-dependent connectivity. For example, people normally board and leave trains at stations and fixed-wing aircraft typically require an airstrip to take off and land. / References [1] Groves, P. D., Principles of GNSS, inertial, and multi-sensor integrated navigation systems, Second Edition, Artech House, 2013. [2] Park, C. G., et al., “Adaptive Step Length Estimation with Awareness of Sensor Equipped Location for PNS,” Proc. ION GNSS 2007. [3] Frank, K., et al., “Reliable Real-Time Recognition of Motion Related Human Activities Using MEMS Inertial Sensors,” Proc. ION GNSS 2010. [4] Pei, L., et al., “Using Motion-Awareness for the 3D Indoor Personal Navigation on a Smartphone,” Proc. ION GNSS 2011. [5] Lin, T., C. O’Driscoll, and G. Lachapelle, “Development of a Context-Aware Vector-Based High-Sensitivity GNSS Software Receiver,” Proc. ION ITM 2011. [6] Shafiee, M., K., O’Keefe, and G. Lachapelle, “Context-aware Adaptive Extended Kalman Filtering Using Wi-Fi Signals for GPS Navigation,” Proc. ION GNSS 2011. [7] Shivaramaiah, N. C., and A. G. Dempster, “Cognitive GNSS Receiver Design: Concept and Challenges,” Proc. ION GNSS 2011. [8] Quddus, M. A., High Integrity Map Matching Algorithms for Advanced Transport Telematics Applications, PhD Thesis, Imperial College London, 2006

XRLoc: Accurate UWB Localization for XR Systems

Understanding the location of ultra-wideband (UWB) tag-attached objects and people in the real world is vital to enabling a smooth cyber-physical transition. However, most UWB localization systems today require multiple anchors in the environment, which can be very cumbersome to set up. In this work, we develop XRLoc, providing an accuracy of a few centimeters in many real-world scenarios. This paper will delineate the key ideas which allow us to overcome the fundamental restrictions that plague a single anchor point from localization of a device to within an error of a few centimeters. We deploy a VR chess game using everyday objects as a demo and find that our system achieves $2.4$ cm median accuracy and $5.3$ cm $90^\mathrm{th}$ percentile accuracy in dynamic scenarios, performing at least $8\times$ better than state-of-art localization systems. Additionally, we implement a MAC protocol to furnish these locations for over $10$ tags at update rates of $100$ Hz, with a localization latency of $\sim 1$ ms