Toward a unified PNT, Part 1: Complexity and context: Key challenges of multisensor positioning

The next generation of navigation and positioning systems must provide greater accuracy and reliability in a range of challenging environments to meet the needs of a variety of mission-critical applications. No single navigation technology is robust enough to meet these requirements on its own, so a multisensor solution is required. Known environmental features, such as signs, buildings, terrain height variation, and magnetic anomalies, may or may not be available for positioning. The system could be stationary, carried by a pedestrian, or on any type of land, sea, or air vehicle. Furthermore, for many applications, the environment and host behavior are subject to change. A multi-sensor solution is thus required. The expert knowledge problem is compounded by the fact that different modules in an integrated navigation system are often supplied by different organizations, who may be reluctant to share necessary design information if this is considered to be intellectual property that must be protected

The Importance of Human Motion for Simulation Testing of GNSS

Human motion is generally considered benign to the performance of global navigation satellite system (GNSS) and other positioning sensors. This study proves that this is not the case, even for typical human behaviour involving GNSS user equipment, e.g. in smartphones. Using recorded human motion, it is shown that phase-lock loops (PLLs) in GNSS receivers are sensitive to jerk dynamics induced by user motion, resulting in carrier cycle slips. To test the effects of human dynamics on GNSS carrier tracking, real human motion profiles were captured. These profiles comprised typical types of movements using a mobile phone, e.g. holding, answering and texting, different types of activities, e.g. walking or jogging, as well as different phone locations on the human body, e.g. in a hand, pocket, backpack and on an arm band. The data were captured outdoors using an Xsens MTi-G MEMS (Micro-Electronic Mechanical Systems) Inertial Measurement Unit (IMU) aided by a Global Positioning System (GPS) receiver with a 100Hz output rate. Then the captured motion (MoCap) was processed and input into a simulated PLL in Matlab with different tracking loop bandwidths (BL_CA) and carrier power-to-noise density ratios (C/N0). The results show that pedestrian gestures and type of activity, e.g. walking or jogging, affect the performance of the simulated PLL more adversely than the location of the phone on the human body. Also, to track pedestrian motion encompassing these gestures, activities and receiver locations, a minimum of 15Hz tracking bandwidth is required. Consequently, receiver manufacturers should exercise caution before reducing tracking bandwidths to compensate for the reduction in C/N0 resulting from GNSS antenna design, human body masking and the effects of buildings, trees and other environmental features. This paper also proposes and describes a pedestrian motion model (PMM) that simulates the GNSS antenna trajectory in 3D, when it is held by or attached to a pedestrian. The PMM will be validated using real MoCap scenarios and will enable Spirent to increase their product offering in the area of simulation-based testing of positioning sensors for pedestrian applications by generating human motion profiles which affect realistically the performance of GNSS user equipment

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

Vulnerability analysis of GPS receiver software

Satellite navigation systems such as the Global Positioning System (GPS)makes it possible for users to find their relative or absolute position. Thanks to its mobility and reliability, the GPS is used in many civil and military applications. However, the GPS does not provide an advanced level of security. Therefore, it could be potentially a target of attacks. With the development of new GPS attacks, the security knowledge has to grow at the same rate, so existing attacks can be detected by updated versions of receiver software or hardware. In this paper, a comparative analysis of GPS receiver resilience to software attacks is performed with the help of GNSS simulator from Spirent. The main objective of this work is to perform a sensitivity analysis of variables involved in calculation of position of the GPS receivers from different price bands that might be targeted by existing or future GPS attack. Variables making the biggest impact on calculated position are determined using the model. Experimentation validation of their influence is performed using selected receivers and corrupted signals generated by GNSS simulator. The testing is based on tuning the selected variables in order to simulate the theoretical error obtained from the sensitivity analysis. The results obtained from testing are discussed in order to analyse the behaviour of the considered GNSS receivers (including the premium class ones)and establish whether they provide a protection from existing or potential GPS attacks

The four key challenges of advanced multisensor navigation and positioning

The next generation of navigation and positioning systems must provide greater accuracy and reliability in a range of challenging environments to meet the needs of a variety of mission-critical applications. No single navigation technology is robust enough to meet these requirements on its own, so a multisensor solution is required. Although many new navigation and positioning methods have been developed in recent years, little has been done to bring them together into a robust, reliable, and cost-effective integrated system. To achieve this, four key challenges must be met: complexity, context, ambiguity, and environmental data handling. This paper addresses each of these challenges. It describes the problems, discusses possible approaches, and proposes a program of research and standardization activities to solve them. The discussion is illustrated with results from research into urban GNSS positioning, GNSS shadow matching, environmental feature matching, and context detection