Martin Ulmke

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The cardinalized probability hypothesis density (CPHD) filter is a recursive Bayesian algorithm for estimating multiple target states with varying target number in clutter. In particular, the Gaussian mixture variant (GMCPHD) for linear, Gaussian systems is a candidate for real time multi target tracking. The present work addresses the following three(More)
Nonlinear target tracking is a well known problem, and its Bayes optimal solution, based on particle filtering techniques, is nowadays applied in high performance surveillance systems. Nonetheless, the practical application of Particle Filters (PFs) may still be difficult, so that possibly available external knowledge can be exploited to increase the(More)
In many surveillance problems the observed objects are so closely spaced that they cannot always be resolved by the sensor(s). Typical examples for partially unresolved measurements are the surveillance of aircraft in formation, and convoy tracking for ground surveillance. Ignoring the limited sensor resolution in a tracking system may lead to degraded(More)
This paper presents results achieved by the Authors within the IPP<sup>1</sup> Multi Sensorics program. The addressed research deals with the exploitation of context information, denoted as &#x201C;Knowledge Base&#x201D; within the different steps of multi-sensor data processing for surface surveillance. An overview on the research motivations and the(More)
For the detection of targets moving on ground, airborne ground moving target indicator (GMTI) radar is well-suited. In the tracking process, complex target dynamics, particularly stop and go maneuvers, and target masking due to Doppler blindness, often lead to track losses. By means of a refined sensor model it is possible to detect and handle such diverse(More)
The cardinalized probability hypothesis density (CPHD) filter is a recursive Bayesian algorithm for estimating multiple target states with varying target number in clutter. In the present work, it is shown that a missed detection in one part of the field of view has a significant effect on the probability hypothesis density (PHD) arbitrarily far apart from(More)
Bayesian non-linear filtering is considered in this paper for the state vector estimation of manoeuvring targets at sea. Innovative schemes based on the Extended Kaiman Filter and the Particle Filter are derived by the introduction of a priori vessel route information. Such contextual input drives the selection of the manoeuvre model to be used for target(More)
This contribution presents an application of a multi hypothesis tracking (MHT) algorithm to the case of ground moving targets detected by GMTI (ground moving target indicator) sensors. We describe in some detail how the following tracking tasks are performed: track extraction, prediction, filtering, track maintenance and retrodiction (smoothing). The(More)
For analyzing dynamic scenarios with many ground moving vehicles, airborne ground moving target indicator (GMTI) radar is well-suited due to its wide-area, all-weather, day/night, and real time capabilities. The generation of GMTI tracks from these data is the backbone for producing a "recognized ground picture" as well as for analyzing traffic flows. In(More)