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Solar extreme ultraviolet radiation is continuously absorbed in the dayside upper atmosphere and controls the creation of ionospheric plasma and its variability via complex interactions. Through these coupled processes, solar signatures such as the 27-day solar rotation period are introduced to the ionospheric state and are observed via various measurements. For example, time-delayed 27-day signatures with varying amplitudes are found for the main ion species and electrons, whose distributions in turn also vary in longitude, latitude, and altitude. Other changes are driven by space weather events such as geomagnetic storms or solar flares. Therefore, a full understanding of the chemical and physical processes is required to correctly describe the delayed ionospheric response. In a series of studies, we investigated and characterized these variations with different satellite- and ground-based measurements (e.g. recent studies with various NASA GOLD data products). Simulations with physics-based models were applied to reproduce the observed variations and to identify the driving processes. While the occurrence of the 27-day signatures is mainly controlled by photoionization and recombination, the delay in particular is significantly driven by photodissociation and transport across different ionospheric layers. In this presentation, we provide an overview of the main results of our studies and preview future work. We also highlight those results that are of interest for the improvement of ionospheric modelling., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Based on the analysis of electron density Ne profiles (Grahamstown ionosonde), a case study of the height‐dependent ionospheric response to two 27‐day solar rotation periods in 2019 is performed. A well‐defined sinusoidal response is observed for the period from 27 April 2019 to 24 May 2019 and reproduced with a Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model simulation. The occurring differences between model and observations as well as the driving physical and chemical processes are discussed based on the height‐dependent variations of Ne and major species. Further simulations with an artificial noise free sinusoidal solar flux input show that the Ne delay is defined by contributions due to accumulation of O+ at the Ne peak (positive delay) and continuous loss of O2+ in the lower ionosphere (negative delay). The neutral parts' 27‐day signatures show stronger phase shifts. The time‐dependent and height‐dependent impact of the processes responsible for the delayed ionospheric response can therefore be described by a joint analysis of the neutral and ionized parts. The return to the initial ionospheric state (and thus the loss of the accumulated O+) is driven by an increase of downward transport in the second half of the 27‐day solar rotation period. For this reason, the neutral vertical winds (upwards and downwards) and their different height‐dependent 27‐day signatures are discussed. Finally, the importance of a wavelength‐dependent analysis, statistical methods (superposed epoch analysis), and coupling with the middle atmosphere is discussed to outline steps for future analysis., Key Points: A response to solar 27‐day signatures is observed in ionosonde Ne height profiles and successfully reproduced with a Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model simulation. Height‐dependent variations of the delayed ionospheric response are driven by the respective contributions of O+ and O2+. Transport processes have a significant impact on the 27‐day signatures of neutral and ionized parts in the upper atmosphere.

Physics based models play an important role in the analysis of the complex coupling processes between the ionosphere and the thermosphere and predicting the processes happening in the upper atmosphere. The Coupled Thermosphere Ionosphere Plasmasphere Electrodynamics (CTIPe) model and the Thermosphere Ionosphere Electrodynamics General Circulation model (TIE-GCM) are two state-of-the-art numerical models commonly used for that purpose. However, the quality of the results of the physics-based models depends on the accuracy in the estimation of the external forcing of the system and the accuracy of the initial state of the thermosphere-ionosphere system. In order to investigate to what extent the assimilation of in-situ neutral mass density helps to improve the estimation of the state of the thermosphere-ionosphere (TI) system, we will assimilate accelerometer-derived neutral mass density from CHAMP and compare the model results with GRACE data to evaluate the improvement in the thermosphere. In the same way, we will analyze the differences with Total Electron Content (TEC) measurements from the International GNSS Service (IGS) to assess the changes in the ionosphere due to neutral mass density assimilation. Therefore, the method will allow us not only to quantify the improvement in the estimation of the TI system, but to expand our understanding of the coupling mechanisms between the thermosphere and the ionosphere. Since this analysis might depend on model capabilities and implementation of data assimilation schemes, we approach this task with two different models and data assimilation schemes. A Thermosphere-Ionosphere Data Assimilation (TIDA) scheme has been implemented to the CTIPe model and the Data Assimilation Research Testbed (DART) has been coupled to TIE-GCM model. By this means, differences in model capabilities can also be indicated.

Critical infrastructures are essential lifelines for a wellfunctioning society, the competitiveness of the economy, and the social prosperity. One of the basic technological infrastructures of the global trade is the maritime transportation system with its wide range of sub-systems and their components as e.g. provided services and involved actors. The coordinated actions and concerted use of system components ensure the transport of goods and the mobility of population. Automatization, digitalization, networking, as well as autonomous operation are presently considered as suitable means to improve safety and enhance the efficiency of transportation. As a result, the ever more complex transportation systems and processes become more sensitive to existing and new emerging threats e.g. cyber attacks. That’s why the resilience of transportation and used systems have been moved in the focus of investigations and developments. However, a wide variety of proactive and reactive approaches are already used or are in discussion to enhance the reliable functioning of technological and carrying on socio-technical infrastructures. This also applies for ships’ and their navigation systems, whereby carriage requirements, performance standards and guidelines of the International Maritime Organisation specify what and how resilience principles should be implemented. This paper discusses the application of resilience engineering principles by shipside navigation systems and their effectiveness taking into account their mutual dependencies. Within this context the ship navigation system is considered as technological system (without human in the loop) as well as socio-technical system (with human in the loop). Case studies will illustrate under which conditions the application of resilience principles may result into an improvement of the operational reliability. Based on simulations it will be shown that the creation of added values by implementation of various resilience principles depends strongly on the applied, optimised system layout and its adaptability. For example, physical and functional redundancy is an approach used to overcome single failures or to reduce the reliance on individual technologies. The implementation of redundancy increases the system complexity (more sensors and sources), the mutual dependencies (controlling and decision making), and ultimately the number of potential threats. It will be shown that a sub-optimal implementation and utilisation of resilience principles may decrease the robustness of the shipside navigation system (technological view) as well as the reliability and adaptability of ship’s navigating system (socio-technical view). Both approaches require the monitoring of the system behaviour and system-relevant conditions to enable a condition-based controlling and management. This illustrates once again that monitoring is one of the cornerstones of resilience and is caused by the fact that the effectiveness of most resilience principles depends on availability and trustworthiness of situational information in relation to system status and environmental conditions, irrespective of whether the generation and use of the situational information is machine-made or human-made. Therefore monitoring has to be acknowledged as essential accompanying functionality to be considered in design and operation of resilient systems. For this purpose, the monitoring process has to be portrayed as functional process for the design of resilient navigation systems as well as influencing factor on the decision management and making of the ship navigating system.

This paper discusses the application of resilience engineering principles by shipborne navigation systems. As a technological system, the ship navigation system comprises all the communication and navigation equipment required to operate a ship. If examined as a socio-technological system, one has to additionally consider the use of the ship navigation system by the bridge teams in order to perform the nautical profession in terms of safe and efficient ship navigation, taking into account environmental information received by communication. The first part of this work discusses the theoretical background of resilience engineering and situation awareness. Case studies are used to illustrate under which conditions the application of resilience principles may result in an improvement of the operational reliability. With the help of simulations, it is shown that a sub-optimal implementation and utilization of resilience principles may decrease the robustness of the technical ship navigation system, as well as the reliability and adaptability of the ship navigation system in use. The examples illustrate once again that monitoring is one of the four cornerstones of resilience: anticipating, monitoring, learning, and responding. This is due to the effectiveness of most resilience principles depending on the availability and trustworthiness of situational information in relation to system status and environmental conditions, irrespective of whether the generation and use of the situational information is machine-made or human-made. Therefore, the establishment of situation awareness is an essential accompanying functionality to be considered in design, operation, and use of resilient systems.

The marine radar remains one of the most extensively used sensor for maritime surveillance. Owing to improved technologies, it can nowadays be exploited to gain information about the extents of targets, since multiple measurements can be obtained from a single target. This paper introduces an open multitarget marine radarbased dataset subjected to a linear-time joint probabilistic data association (JPDA) filter for tracking extended targets using ellipsoidal approximations. A tailored version of the Multiplicative Error Model-Extended Kalman Filter* (MEM-EKF*) algorithm is used for estimating the orientations and kinematic properties of multiple targets recursively. Using the automatic identification system (AIS) information as ground truth, the positional errors are evaluated using the optimal sub pattern assignment (OSPA) metric and the performance of the algorithm for orientation estimation is rationally discussed with respect to the ground truth and dataset scenario. The dataset proposed is intended for comparing different algorithms for the purpose of multiple extended target tracking (METT).

The key to maritime surveillance is an accurate and real-time update of the current traffic situation. Although the Automatic Identification System (AIS) has greatly improved the traffic situation assessment over the past years, it has shown to be error-prone and vulnerable to spoofing or intentional misuse. To obtain a more reliable picture of a vessel’s motion this work proposes the fusion of AIS and Radar data in a loosely coupled architecture. For that reason, an Interacting Multiple Model (IMM) Multi Sensor Probabilistic Data Association (PDA) filter was designed not only to fuse measurements from different sources, but also to best capture the different dynamic states of a single vessel. In the IMM two Unscented Kalman Filters (UKFs) run in parallel and interact with each other, each implementing a different dynamic model, the CV and CTRV, respectively. Each UKF is conditioned on asynchronous measurements obtained from either AIS or Radar. In case of the latter, measurements of a target candidate may also originate from clutter, not from the target itself. The PDA filtering approach not only associates these measurements to the actual target state but also acts as innovation gate for faulty AIS position updates. A standard blob detection algorithm was implemented and tuned for extracting target candidates in range and bearing from Radar images. To show the benefits of the proposed scheme a dedicated measurement campaign was performed in the Baltic Sea with a stationary ship monitoring a highly manoeuvring target vessel. With the proposed approach not only short term AIS induced errors can be detected and rejected but what may also become observable is suspicious AIS behaviour including anomalous position data or muted transponders.

For over a decade now the Automatic Identification System (AIS) has been considered an important improvement of both the watchkeeping duties at sea and the vessel traffic surveillance activities worldwide. The on-board AIS equipment is used for broadcasting the dynamic data describing the vessel movement vector as well as the static parameters related to her voyage or hull dimension. The reporting intervals of the AIS transmissions depend on the data validity periods which are shorter for continuously changing dynamic AIS parameters and longer in case of less frequently altering static AIS variables. This work focuses on cases of static AIS parameters like the GNSS reference point which were detected to be changing at a high rate, despite the fact that the settings like this are only allowed to be modified during the configuration phase of an AIS transponder. The AIS data received at the DLR reference station in Rostock are analysed and provide a statistical overview of this phenomenon.

The Automatic Identification System (AIS) is widely used for reporting vessel movements and broadcasting additional information related to the current voyage or constant parameters like the IMO number or the overall dimension of the hull. Since the dynamic AIS data is shared mostly without human interaction and is not flawless, the static AIS content edited manually may also be highly exposed to errors and mistakes due to the predominant human factor. In this work a simple vessel motion pattern approach is introduced to determine the probable foredeck/afterdeck location of the GNSS reference used by the AIS transponder, and to compare it with the hull parameters obtained from the static AIS data, in order to find the observable blunders in the static AIS configuration of the mount point of the GNSS reference antenna.

Presentation of new Projects of DLR Neustrelitz and their resulting requirements for archiving of large data massive

In this paper we study the plausibility of navigation relevant AIS parameter based on time series analysis of HELCOM AIS data, obtained during September 2011. Previous analysis[1] of AIS data sets were mainly based on studies of not known or not existent (default) values in the AIS messages. The results of these studies have shown that especially true heading (THDG) and rate of turn (ROT) parameters were strongly affected by default / unknown values. We discuss algorithms to evaluate the plausibility of data contained in the AIS message for dynamic data, like time, position, speed, and course. The contained information is checked against the plausibility of their values according to other parameters in the time series. For example the speed can be calculated from the two position information from the AIS message and the time difference between these positions. The dynamic data is consistent in this case when both the calculated and the measured value are similar within an appropriate limit. For static data the determination of plausibility is based on checks against the AIS specification. The obtained results will serve as error model for the development of a maritime traffic situation assessment facility, where AIS, radar and specific PNT data shall be fused together to create a reliable traffic situation image.