An Efficient Configuration Method for RTLSs - Grimm, Robert; Edelhäußer, Thorsten; Kókai, Gabriella (Proceeding 2nd International Symposium on Logistics and Industrial Informatics (LINDI 2009), 2009)
We introduce a new method to estimate the position and direction of receiving elements of a real time locating system (RTLS). In an angle of arrival (AoA) system, we are able to reconstruct the antenna configuration using measurements at only three reference points. No further prior knowledge is required, and the computations are very lightweight. The localisation procedure is presented and the results of a simulated experiment setup are shown. We discuss possible usage scenarios and advantages of our solution as well as possible extensions.
UnivIS-Import:2015-04-16:Pub.2009.tech.IMMD.IMMD2.aneffi

Autonomous Configuration Method for Real-Time Location Systems - Edelhäußer, Thorsten; Kókai, Gabriella (Proceedings NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2009), 2009)
We introduce a new method for estimating the position and direction of receiving elements of a Real-Time Location System (RTLS). We deployed an autonomous robot and an Evolutionary Algorithm (EA) for this problem. The adopted operations and fitness calculation of the EA are presented for an Angle of Arrival (AoA) system and the results of simulation and real experiment setup were shown. Further problems of implementing in a real scenario were discussed.
UnivIS-Import:2015-04-16:Pub.2009.tech.IMMD.IMMD2.autono
Keywords: Angle of Arrival; Autonomous configuration; Evolutionary Algorithm; RTLS

Dynamische probabilistische Bewegungsmodelle mittels Verhaltensmodellierung - Edelhäußer, Thorsten (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2012)

Environment-Based Measurement Planning for Autonomous RTLS Configuration - Edelhäußer, Thorsten; Janak, Mateusz; Kókai, Gabriella (Proceedings of the Conference on Adaptive Hardware and Systems (AHS 2010), 2010)
We present a novel approach for selecting measurement points online to determine the pose (position and orientation) of an Real-Time Location System's (RTLS) receiving element. In combination with the RANdom Sample And Consensus (RANSAC) algorithm, we generate a selection of measurement points excluding bad measurements to get the right estimation of the pose. The proposed algorithm considers obstacles and boundaries, which are previously unknown. On the basis of an Angle of Arrival (AoA) system, we demonstrate the applicability and accuracy gain of our algorithm in simulation and real experiment. © 2010 IEEE.
UnivIS-Import:2015-04-16:Pub.2010.tech.IMMD.IMMD2.enviro
Keywords: AoA; Evolutionary Algorithm; Random Sample and Consensus (RANSAC); Robotics; RTLS

Online Data Stream Mining on Interactive Trajectories in Soccer Games - Mutschler, Christopher; Kókai, Gabriella; Edelhäußer, Thorsten (Proceedings of the 2nd International Conference on Positioning and Context-Awareness (PoCA 2011), 2011)
UnivIS-Import:2015-04-16:Pub.2011.tech.IMMD.IMMD2.online

Predictive Load Management in Smart Grid Environments - Mutschler, Christopher; Löffler, Christoffer; Witt, Nicolas; Edelhäußer, Thorsten; Philippsen, Michael (Proceedings of the 8th ACM International Conference on Distributed Event-Based Systems (DEBS'14), 2014)
The DEBS 2014 Grand Challenge targets the monitoring and prediction of energy loads of smart plugs installed in private households. This paper presents details of our middleware solution and efficient median calculation, shows how we address data quality issues, and provides insights into our enhanced prediction based on hidden Markov models. The evaluation on the smart grid data set shows that we process up to 244k input events per second with an average detection latency of only 13.3ms, and that our system efficiently scales across nodes to increase throughput. Our prediction model significantly outperforms the median-based prediction as it deviates much less from the real load values, and as it consumes considerably less memory.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD2.grandc
Keywords: event processing; streaming; cep

Recurrent Neural Networks on Drifting Time-of-Flight Measurements - Feigl, Tobias; Nowak, Thorsten; Philippsen, Michael; Edelhäußer, Thorsten; Mutschler, Christopher (Proceedings of the 9th International Conference on Indoor Positioning and Indoor Navigation (IPIN 2018), 2018)
Kalman filters (KFs) are popular methods to es- timate position information from a set of time-of-flight (ToF) values in radio frequency (RF)-based locating systems. Such filters are proven to be optimal under zero-mean Gaussian error distributions. In presence of multipath propagation ToF measurement errors drift due to small-scale motion. This results in changing phases of the multipath components (MPCs) which cause a drift on the ToF measurements. Thus, on a short- term scale the ToF measurements have a non-constant bias that changes while moving. KFs cannot distinguish between the drifting measurement errors and the true motion of the tracked object. Hence, very rigid motion models have to be used for the KF which commonly causes the filters to diverge. Therefore, the KF cannot resolve the short-term errors of consecutive measurements and the long-term motion of the tracked object. This paper presents a data-driven approach that uses training sequences to derive a near-optimal position estimator. A Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) learns to interpret drifting errors in ToF measurements of a tracked dynamic object directly from raw ToF data. Our evaluation shows that our approach outperforms state-of-the- art KFs on both synthetically generated and real-world dynamic motion trajectories that include drifting ToF measurement errors.

Trajectory Behavior Language - Edelhäußer, Thorsten; Philippsen, Michael; Mutschler, Christopher (Proceedings of the 2nd International Conference on Positioning and Context-Awareness (PoCA 2011), 2011)
UnivIS-Import:2015-04-16:Pub.2011.tech.IMMD.IMMD2.trajec