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Auflistung nach Autor:in "Heute, Ulrich"

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  • Konferenzbeitrag
    Adaptive clutter density in multi-hypothesis tracking
    (INFORMATIK 2011 – Informatik schafft Communities, 2011) Wilkens, Kathrin; Nguyen, Viet Duc; Heute, Ulrich
    In underwater surveillance active sonar is an important technological asset. Compared to passive sonar it features higher detection ranges and enables the detection of silent objects. As a drawback the interaction of sound waves with the seabed and the water surface causes false alarms, named clutter. False alarms usually appear randomly and variable in time and space. To distinguish false alarms from true contacts the Multi-Hypothesis Tracking approach can be used. This approach incorporates the density of sonar contacts to extract possible target tracks. Thus, the assumed clutter density influences, amongst others, the performance of this tracking approach. This paper presents a method for determining the clutter density adaptively. It considers positions of all sonar contacts within one measurement and thereby approximates the actual clutter density precisely. The influence on the tracking results using adaptive clutter density in a multi-hypothesis tracker is shown by applying the algorithm to two multistatic sonar datasets and comparing it to results obtained by tracking using constant clutter density. Tracking performance is quantified by existing tracking performance metrics.
  • Konferenzbeitrag
    Maneuver-adaptive multi-hypothesis tracking for active sonar systems
    (INFORMATIK 2010. Service Science – Neue Perspektiven für die Informatik. Band 2, 2010) Seget, Kathrin; Schulz, Arne; Heute, Ulrich
    In undersea surveillance, active sonar systems are commonly used to detect submarines. These sonar systems allow high detection ranges, but the interaction of sound with the sea bottom may lead to a high number of false alarms as well, especially in shallow-water environments. Therefore, automatic detection and tracking procedures are needed to provide helpful assistance to sonar operators. The Multi- Hypothesis Tracking approach presented in this paper is one of these procedures. It is based on nonlinear Kalman Filtering. In Kalman Filtering the assumption on underlying target dynamics is essential and has considerable impact on the overall tracking performance. As targets usually maneuver, their dynamics are varying and hidden. To include variable target dynamics, a Multi-Hypothesis tracking algorithm is adapted to consider target maneuvers by estimating and adjusting the process-noise level in the Kalman Filter equations. The level of process noise is determined for every track hypothesis individually based on the estimated velocities of the target. The impact on the tracking result is shown by applying the presented approach to different multistatic sonar datasets and comparing it to results gained by tracking with one global level of process noise. Tracking results are quantified by several tracking-performance metrics.