On Fault Detection and Exclusion in Snapshot and Recursive Positioning Algorithms for Maritime Applications

Resilient provision of Position, Navigation and Timing (PNT) data can be considered as a key element of the e-Navigation strategy developed by the International Maritime Organization (IMO). An indication of reliability has been identified as a high level user need with respect to PNT data to be supplied by electronic navigation means. The paper concentrates on the Fault Detection and Exclusion (FDE) component of the Integrity Monitoring (IM) for navigation systems based both on pure GNSS (Global Navigation Satellite Systems) as well as on hybrid GNSS/inertial measurements. Here a PNT-data processing Unit will be responsible for both the integration of data provided by all available on-board sensors as well as for the IM functionality. The IM mechanism can be seen as an instantaneous decision criterion for using or not using the system and, therefore, constitutes a key component within a process of provision of reliable navigational data in future navigation systems. The performance of the FDE functionality is demonstrated for a pure GNSS-based snapshot weighted iterative least-square (WLS) solution, a GNSS-based Extended Kalman Filter (EKF) as well as for a classical error-state tightly-coupled EKF for the hybrid GNSS/inertial system. Pure GNSS approaches are evaluated by combining true measurement data collected in port operation scenario with artificially induced measurement faults, while for the hybrid navigation system the measurement data in an open sea scenario with native GNSS measurement faults have been employed. The work confirms the general superiority of the recursive Bayesian scheme with FDE over the snapshot algorithms in terms of fault detection performance even for the case of GNSS-only navigation. Finally, the work demonstrates a clear improvement of the FDE schemes over non-FDE approaches when the FDE functionality is implemented within a hybrid integrated navigation system

Entwicklung einer Low-Cost-PNT Unit für maritime Anwendungen, basierend auf MEMS-Inertialsensoren

Although the GPS/GNSS had become the primary source for Position, Navigation and Timing (PNT) information in maritime applications, the ultimate performance of the system can strongly degrade due to space weather events, deliberate interference and overall system failures. Within the presented work the development of an affordable integrated PNT unit for future on-board integrated system is presented. The system serves the task to collect and integrate the data from individual sensors in order to deliver the PNT information with a specified performance according to the requirements of the e-Navigation initiative proposed by the International Maritime Organization (IMO). The paper discusses an ongoing activity of replacing an expensive FOG inertial measurement unit with an affordable MEMS sensor system. Preliminary results of the system performance are presented for both static and dynamic scenarios using an Unscented Kalman filter with unit quaternions for the attitude parametrization

The Use of an Orientation Kalman Filter for the Static Postural Sway Analysis

AbstractThe paper presents a quaternion-based extended Kalman filter for postural instability evaluation during stance. It uses low-cost MEMS inertial sensors attached on the lower back of the person at a known height in order to instrumenting the static balancing test. Generally, patients with Parkinson's disease or vestibular-loss are at greater risk for having this problem. The objective of this study was to assess the feasibility of using Kalman filter to characterize the postural steadiness. The Kalman filter is used here as a data fusion algorithm to estimate the orientation of the body based on acceleration and angular rate signals. In order to get the coordinate of the body's centre of mass (CoM), the height of the sensor is projected on the horizontal plane by using the estimated orientation. Many parameters such as the mean velocity of sway, lateral/anterior-posterior range and others are then obtained from the sway path, which help the clinicians to assess the postural instability. The method was tested on 9 healthy individuals (21-31 years). Three different test conditions, namely feet comfortable stance with eyes-open, feet together stance with closed eyes and one-leg stance with eyes-open were evaluated here. The proposed algorithm showed successful estimation of the time-domain parameter for the postural sway analysis

Application of fractional sensor fusion algorithms for inertial mems sensing

The work presents an extension of the conventional Kalman filtering concept for systems of fractional order (FOS). Modifications are introduced using the Grünwald‐Letnikov (GL) definition of the fractional derivative (FD) and corresponding truncation of the history length. Two versions of the fractional Kalman filter (FKF) are shown, where the FD is calculated directly or by augmenting the state vector with the estimate of the FD. The filters are compared to conventional integer order (IO) Position (P‐KF) and Position‐Velocity (PV‐KF) Kalman filters as well as to an adaptive Interacting Multiple‐Model Kalman Filter (IMM‐KF). The performance of the filters is assessed based on a hand and a head motion data set. The feasibility of the given approach is shown. First published online: 14 Oct 201

Robust Position and Velocity Estimation Methods in Integrated Navigation Systems for Inland Water Applications

As the Global Navigation Satellite Systems (GNSS) are intensively used as main source of Position, Navigation and Timing (PNT) information for maritime and inland water navigation, it becomes increasingly important to ensure the reliability of GNSS-based navigation solutions for challenging environments. Although an intensive work has been done in developing GNSS Receiver Autonomous Integrity Monitoring (RAIM) algorithms, a reliable procedure to mitigate multiple simultaneous outliers is still lacking. The presented work evaluates the performance of several methods for multiple outlier mitigation based on robust estimation framework and compares them to the performance of state-of-the-art RAIM methods. The relevant methods include M-estimation, S-estimation, LMS and RANSAC-based approaches as well as corresponding modifications for C/N0-based weighting schemes. The snapshot positioning methods are also tested within the quaternion-based Cubature Quadrature Kalman filter for integrated inertial/GNSS solution. The presented schemes are evaluated using real measurement data from challenging inland water scenarios with multiple bridges and a waterway lock. The initial results are encouraging and clearly indicate the potential of the discussed methods both for classical snapshot solutions as well for the methods with complementary sensors

TUG Test Instrumentation for Parkinson’s disease patients using Inertial Sensors and Dynamic Time Warping

The Timed Up and Go (TUG) test is a clinical tool widely used to evaluate balance and mobility, e.g. in Parkinson’s disease (PD). This test includes a sequence of functional activities, namely: sit-to-stand, 3-meters walk, 180° turning, walk back, and turn-to-sit. The work introduces a new method to instrument the TUG test using a wearable inertial sen-sor unit (DynaPort Hybrid, McRoberts B.V., NL) attached on the lower back of the person. It builds on Dynamic Time Warping (DTW) for detection and duration assessment of associated state transitions. An automatic assessment to sub-stitute a manual evaluation with visual observation and a stopwatch is aimed at to gain objective information about the patients. The algorithm was tested on data of 10 healthy individuals and 20 patients with Parkinson's disease (10 pa-tients for early and late disease phases respectively). The algorithm successfully extracted the time information of the sit-to-stand, turn and turn-to-sit transitions

On PNT Integrity in Snapshot And Recursive Positioning Algorithms for Maritime Applications

Resilient provision of Position Navigation and Time (PNT) data is a strategic key element of the e-Navigation strategy, developed by the International Maritime Organization (IMO). The improvement and the indication of reliability have been identified as high level user need with respect to PNT data supplied by electronics means. IMO as developed a maritime PNT system concept aiming to improve the resilience and reliability of PNT data provision during berth-to-berth navigation. The maritime PNT System comprises several structural components, where Global Navigation Satellite Systems (GNSS), have become the primary component to produce position, velocity and time information for maritime applications. For a comprehensive onboard provision of PNT data as well as to compensate the vulnerability of GNSS, further onboard sensors are needed. The PNT system is responsible for the fusion of the data provided by all the available onboard sensors and data integrity monitoring functions. A unit composed by several sensors of different classes improves the resilience of the system. DLR has developed a prototype of an onboard PNT unit and several measurement campaigns have been performed. This paper concentrates on integrity monitoring (IM) for navigation systems based on sensor fusion. IM is a mechanism that protects the user from large position and velocity errors in the presence of failures or non-scheduled events in a timely fashion. It can be seen as an instantaneous decision criterion for using or not the system and therefore constitutes a key function for the safety of navigation. The IM includes the detection and exclusion functions, they are responsible for detecting the measurements errors (faults) and exclude them from the PNT data computation algorithm. This work presents a systematic analysis of Fault Detection and Exclusion (FDE) algorithms in representative single and multi-sensor. More specifically, a pure GNSS-based snapshot weighted iterative least-square (WLS) solution is compared to a classical error-state Extended Kalman Filter (EKF) for a combined GNSS/IMU system with Euler angles for attitude parameterization. The outlier detection functionality is implemented for both pseudorange and Doppler shift observations in order to ensure the integrity of the estimated position and velocity data. The work confirms the superiority of the recursive Bayesian scheme over a snapshot algorithm in terms of the outlier detection performance. This can be explained by the recursive structure of the estimator, where the dynamical model of the system provides the additional source of information, which increases the system’s redundancy and hence improves the performance of the FDE schemes

On the Performance of Inertial/GNSS/Doppler Velocity Log Integrated Navigation Systems for Marine Applications

Although the GNSS/GPS had become the primary source for Positioning, Navigation and Timing (PNT) information in maritime applications, the ultimate performance of the system can strongly degrade due to space weather events, deliberate interference, shadowing, multipath and overall system failures. Within the presented work the development of an affordable integrated PNT unit for future on-board integrated systems is presented, where the GNSS information is fused both with inertial and Doppler Velocity Log (DVL) measurements. Here redundant and complementary information from different sensors serves to improve the system performance and reduce the position drift when the GNSS signals are not available. The nonlinearity of this advanced fusion problem is addressed by employing different forms of Sigma-Points Kalman Filter (SPKF) and further detailed analysis is presented in terms of the process and measurement models implemented. The results demonstrate that position drift can be significantly reduced by incorporating DVL measurements in IMU/GNSS system and that the proposed integrated navigation algorithm is feasible and efficient for GNSS outages of prolonged duration, where pure inertial GNSS outage bridging would be either inaccurate or would require too expensive IMUs

A Method for IMU/GNSS/Doppler Velocity Log Integration in Maritime Applications

Although the GNSS/GPS had become the primary source for Positioning, Navigation and Timing (PNT) information in maritime applications, the ultimate performance of the system can strongly degrade due to space weather events, deliberate interference, shadowing, multipath and overall system failures. Within the presented work the development of an affordable integrated PNT unit for future on-board integrated systems is presented, where the GNSS information is fused both with inertial and Doppler Velocity Log (DVL) measurements. Here redundant and complementary information from different sensors serves to improve the system performance and reduce the position drift when the GNSS signals are not available. The nonlinearity of this advanced fusion problem is addressed by employing Unscented Kalman Filter (UKF) with spherical point arrangement and further detailed analysis is presented in terms of the process and measurement models implemented. The results demonstrate that position drift can be significantly reduced by incorporating DVL measurements in IMU/GNSS system and that the proposed integrated navigation algorithm is feasible and efficient for GNSS outages of prolonged duration, where pure inertial GNSS outage bridging would be either inaccurate or would require too expensive IMUs