EKF based trajectory tracking and integrity monitoring of AIS data

Siegert, Gregor; Banyś, Paweł; Martinez, Cristina Saez; Heymann, Frank

doi:10.1109/plans.2016.7479784

Cited by 17 publications

(19 citation statements)

References 13 publications

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“…Analysis of automatic identification system (AIS) data confirms this (Siegert et al. [ 6 ]). However, both USVs have propellers that can turn the vehicles quite fast.…”

Section: Introductionmentioning

confidence: 63%

Five-State Extended Kalman Filter for Estimation of Speed over Ground (SOG), Course over Ground (COG) and Course Rate of Unmanned Surface Vehicles (USVs): Experimental Results

Fossen

2021

Sensors

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Small USVs are usually equipped with a low-cost navigation sensor suite consisting of a global navigation satellite system (GNSS) receiver and a magnetic compass. Unfortunately, the magnetic compass is highly susceptible to electromagnetic disturbances. Hence, it should not be used in safety-critical autopilot systems. A gyrocompass, however, is highly reliable, but it is too expensive for most USV systems. It is tempting to compute the heading angle by using two GNSS antennas on the same receiver. Unfortunately, for small USV systems, the distance between the antennas is very small, requiring that an RTK GNSS receiver is used. The drawback of the RTK solution is that it suffers from dropouts due to ionospheric disturbances, multipath, interference, etc. For safety-critical applications, a more robust approach is to estimate the course angle to avoid using the heading angle during path following. The main result of this article is a five-state extended Kalman filter (EKF) aided by GNSS latitude-longitude measurements for estimation of the course over ground (COG), speed over ground (SOG), and course rate. These are the primary signals needed to implement a course autopilot system onboard a USV. The proposed algorithm is computationally efficient and easy to implement since only four EKF covariance parameters must be specified. The parameters need to be calibrated for different GNSS receivers and vehicle types, but they are not sensitive to the working conditions. Another advantage of the EKF is that the autopilot does not need to use the COG and SOG measurements from the GNSS receiver, which have varying quality and reliability. It is also straightforward to add complementary sensors such as a Doppler Velocity Log (DVL) to the EKF to improve the performance further. Finally, the performance of the five-state EKF is demonstrated by experimental testing of two commercial USV systems.

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“…Analysis of automatic identification system (AIS) data confirms this (Siegert et al. [ 6 ]). However, both USVs have propellers that can turn the vehicles quite fast.…”

Section: Introductionmentioning

confidence: 63%

Five-State Extended Kalman Filter for Estimation of Speed over Ground (SOG), Course over Ground (COG) and Course Rate of Unmanned Surface Vehicles (USVs): Experimental Results

Fossen

2021

Sensors

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“…For instance, Kalman filter approaches use dynamic information from the vessel target, and removes noise to get a prediction of the next vessel target locations [20]. Siegert G et al used EKF to track vessel trajectories [21], and allows for failure detection based on residual monitoring. In [13], an Extended Kalman Filter is proposed as an adaptive filter algorithm for the estimation of position, velocity, and acceleration that are in turn, used to predict maneuvers for ocean vessel trajectory.…”

Section: Related Workmentioning

confidence: 99%

ST-Seq2Seq: A Spatio-Temporal Feature-Optimized Seq2Seq Model for Short-Term Vessel Trajectory Prediction

et al. 2020

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Deep learning provides appropriate mechanisms to predict vessel trajectories for safer and efficient shipping, but still existing models are mainly oriented to longer-term prediction trends and do not fully support real time navigation needs. While most recent works have been largely exploiting Automatic Identification System (AIS), the complete semantics of these data haven't so far fully exploited. The research presented in this paper introduced an extended sequence-to-sequence model using AIS data. A Gated Recurrent Unit (GRU) network encodes historical spatio-temporal sequences as a context vector, which not only preserves the sequential relationships among trajectory locations, but also alleviates the gradient descent problem. The GRU network acts as a decoder, outputting target trajectory location sequences. Real AIS data from the Chongqing and Wuhan sections of the Yangzi River were selected as typical experimental areas for evaluation purposes. The proposed ST-Seq2Seq model has been tested against the LSTM-RNN and GRU-RNN baseline models for short term trajectory prediction experiments. A 10-minute historical trajectory sequence was used to predict the trajectory sequence for the next five minutes. Overall, the findings show that LSTM and GRU networks, while applying a recursive method to predict a sequence of continuous trajectory points, when the number of predicted trajectory points increases accuracy decreases. Conversely, the extended sequence-to-sequence model shows satisfactory stability on different ship channels.

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“…The detailed definitions of the process models f i (•) for CV and CTRV can be found in [17]. In many applications it can be beneficial to parameterize the different models in such a way that one acts more conservatively than the other.…”

Section: Imm-jpda Framework Using Ukfmentioning

confidence: 99%

Counteracting the Effects of GNSS Jamming in a Maritime Multi-Target Scenario by Fusing AIS with Radar Data

Siegert¹,

Banyś²,

Hoth³

et al. 2017

Proceedings of the 2017 International Technical Meeting of the Institute of Navigation

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is a research fellow at the German Aerospace Center (DLR), in the Department of Nautical Systems of the Institute of Communications and Navigation. In 2010 he received his Diploma degree in electrical engineering from the University of Technology Ilmenau in Germany. Before joining DLR in 2015, he worked in the field of SatCom-On-The-Move and Over-The-Air RF testing at the Fraunhofer Institute for Integrated Circuits. Currently, his research is focused on cooperative maritime traffic situation assessment being particularly interested in distributed sensor fusion and target tracking. Paweł Banyś holds a master's degree in finance and banking and an engineer's degree in geodesy and cartography. Between 2001 and 2010 he was employed at different IT companies as network and Linux administrator. He also cooperated with the Maritime University of Szczecin on a vessel traffic safety project. Since 2010 he has been working at the DLR Department of Nautical Systems in the field of AIS and maritime traffic systems. Julian Hoth is a research associate at the German Aerospace Center (DLR) in the Department of Nautical Systems. He received a master's degree in mechanical engineering and a doctoral degree from the University of Duisburg-Essen, Germany. Before joining DLR in 2016, Julian worked in the area of underwater navigation and underwater imagery at the Chair of Mechanics and Robotics of the University of Duisburg-Essen. His current research is focused on radar target detection and tracking. Frank Heymann received a PhD in physics from the University of Bochum in collaboration with the European Southern Observatories (ESO). From 2010 until 2012 he has worked in the field of active galactic nuclei in astrophysics as a postdoctoral researcher at the University of Kentucky. In 2012 he joined the DLR Institute of Communications and Navigation as a research associate in the field of maritime navigation. Since 2014 he is the group leader of the group Traffic Systems in the Department of Nautical Systems.

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EKF based trajectory tracking and integrity monitoring of AIS data

Cited by 17 publications

References 13 publications

Five-State Extended Kalman Filter for Estimation of Speed over Ground (SOG), Course over Ground (COG) and Course Rate of Unmanned Surface Vehicles (USVs): Experimental Results

Five-State Extended Kalman Filter for Estimation of Speed over Ground (SOG), Course over Ground (COG) and Course Rate of Unmanned Surface Vehicles (USVs): Experimental Results

ST-Seq2Seq: A Spatio-Temporal Feature-Optimized Seq2Seq Model for Short-Term Vessel Trajectory Prediction

Counteracting the Effects of GNSS Jamming in a Maritime Multi-Target Scenario by Fusing AIS with Radar Data

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