Frank Heymann

Self-consistent two-phase AGN torus models. SED library for observers

We assume that dust near active galactic nuclei (AGNs) is distributed in a torus-like geometry, which can be described as a clumpy medium or a homogeneous disk, or as a combination of the two (i.e. a two-phase medium). The dust particles considered are fluffy and have higher submillimeter emissivities than grains in the diffuse interstellar medium. The dust-photon interaction is treated in a fully self-consistent three-dimensional radiative transfer code. We provide an AGN library of spectral energy distributions (SEDs). Its purpose is to quickly obtain estimates of the basic parameters of the AGNs, such as the intrinsic luminosity of the central source, the viewing angle, the inner radius, the volume filling factor and optical depth of the clouds, and the optical depth of the disk midplane, and to predict the flux at yet unobserved wavelengths. The procedure is simple and consists of finding an element in the library that matches the observations. We discuss the general properties of the models and in particular the 10 µm silicate band. The AGN library accounts well for the observed scatter of the feature strengths and wavelengths of the peak emission. AGN extinction curves are discussed and we find that there is no direct one-to-one link between the observed extinction and the wavelength dependence of the dust cross sections. We show that objects in the library cover the observed range of mid-infrared colors of known AGNs. The validity of the approach is demonstrated by matching the SEDs of a number of representative objects: Four Seyferts and two quasars for which hyperluminous infrared galaxy. Strikingly, for the five luminous GN models fit the SED without needing to postulate starburst activity.

Improving the Maritime Traffic Situation Assessment for a Single Target in a Multisensor Environment

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.

EKF based trajectory tracking and integrity monitoring of AIS data

This work presents a novel approach for integrity monitoring of AIS data. Currently, the AIS is a valuable source for maritime traffic situation assessment but not suited for collision avoidance, as it is prone to failures and not capable of indicating the level of data integrity. To tackle this, an EKF was designed to track vessel trajectories, which allows for failure detection based on residual monitoring. For the latter, two methods for hypotheses testing were implemented, namely chi-squared and GLR tests. In addition, the IMM framework was adopted for mixing the state estimates of two different process models, the CV and CTRV. The designed filter will be validated on behalf of simulated and real-world AIS data.

Radar image processing and AIS target fusion

Collision avoidance is one of the high-level safety objectives and requires a complete and reliable description of maritime traffic situation. A combined use of data provided by independent data sources is an approach to improve the accuracy and integrity of traffic situation related information. In this paper the authors study the usage of radar images for automatic identification system (AIS) and radar fusion. Therefore they simulate synthetic radar images and evaluate the tracking performance of the particle filter algorithm as the most promising filter approach. During the filter process the algorithm estimates the target position and velocity which they finally compare with the known position of the simulation. This approach allows the performance analysis of the particle filter for vessel tracking on radar images. In a second extended simulation they add the respective AIS information of the target vessel and study the gained level of improvement for the particle filter approach. The work of this paper is integrated in the research and development activities of DLR Institute of Communications and Navigation dealing with the introduction of data and system integrity into the maritime traffic system. One of the aimed objectives is the automatic assessment of the traffic situation aboard a vessel including integrity information.

Performance analysis of a simulated distributed multi-radar system for target detection

The size and number of vessels in the maritime domain has grown significantly over the past decades. Hence, it has become more and more important to obtain reliable information about the current traffic situation. There cannot be more than one traffic situation at a certain time in a specific area. This unique traffic situation picture is seen differently and never in total by each individual vessel or monitoring station. Therefore, it is a reasonable idea to share and merge their corresponding sensor data in a cooperative traffic situation assessment scheme. The radar is considered to be the primary sensor for traffic situation awareness and collision avoidance in the maritime domain. In fact, nearly every vessel is equipped with at least one radar system composing a distributed sensor network, which is spatially sampling the unique situation picture. In this work, we characterise the performance of such a multi-radar system in terms of number of sensors and aspect angle diversity. The results of simulations of different radar configurations are analysed and compared to the performance of a single radar. First, a static case with an ideal (semi-circular) configuration for radars is investigated. It is shown that a small number of radars with the assumed characteristics in a multi-radar system can significantly improve the probability of detection and the detection accuracy compared to a single radar with the chosen fusion strategy. A high probability of detection can be achieved with at least 4 radars. Then the simulation is extended to a realistic scenario with a mobile target and realistic (static) radar locations. It is shown that the fusion of multiple radars yields significant improvement of the detection probability of more than 60% in the best case and a target can be detected about 1NM earlier. An increase of the accuracy by a factor of 3 on average is achievable as well.

Validation of Radar Image Tracking Algorithms withSimulated Data

Collision avoidance is one of the high‐level safety objectives and requires a complete and reliable description of the maritime traffic situation. The radar is specified by the IMO as the primary sensor for collision avoidance. In this paper we study the performance of multi‐target tracking based on radar imagery to refine the maritime traffic situation awareness. In order to achieve this we simulate synthetic radar images and evaluate the tracking performance of different Bayesian multi‐target trackers (MTTs), such as particle and JPDA filters. For the simulated tracks, the target state estimates in position, speed and course over ground will be compared to the reference data. The performance of the MTTs will be assessed via the OSPA metric by comparing the estimated multi‐object state vector to the reference. This approach allows a fair performance analysis of different tracking algorithms based on radar images for a simulated maritime scenario.

Motion-based consistency audit of onboard Global Navigation Satellite System reference as reported by static Automatic Identification System data

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 dynamic AIS data is shared mostly without human interaction, and is not flawless, the static AIS content edited manually is vulnerable to human error. This work introduces a simple vessel motion pattern approach that determines the probable foredeck/afterdeck location of the GNSS reference used by the AIS transponder, and compares it to the hull parameters obtained from the static AIS data, to find observable errors in the static AIS configuration of the mount point of the GNSS reference antenna.

Comparison Of AIS-Based Prediction Of The Distance At The CPA With Factual Separation Between Vessels

Abstract Since its deployment in 2004, the Automatic Identification System (AIS) has been considered a significant improvement of watchkeeping duties at sea. According to current regulations, AIS has not been recognised as an approved anticollision instrument yet. However, it would be difficult to rule out a possibility that AIS, being an essential part of the onboard SOLAS — compliant configuration, is unaidedly used for collision avoidance tasks. Recent research activities of DLRs Department of Nautical Systems have shown that AIS transmissions may contain a lot of incomplete data and the system does not have any dependable information on its data integrity. For that reason, the computation of the closest point of approach (CPA) and the time to the CPA (TCPA) are analysed based on AIS data involving multiple vessels, in order to compare the predictions with factual approaches between vessels and to evaluate the usability of AIS data, in its present form, for the appraisal of the traffic situation around each vessel.