Information management and target detection for multisensor airborne platforms

Jäger, Klaus; Hebel, Marcus; Armbruster, Walter; Bers, Karl-Heinz

doi:10.1117/12.666567

Cited by 4 publications

(3 citation statements)

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“…Additionally, this allows producing views of the objects from different aspects. The application of the obtained IR-signatures for automatic target recognition is described in [5].…”

Section: )mentioning

confidence: 99%

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Imaging sensor fusion and enhanced vision for helicopter landing operations

2006

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An automatic target recognition system has been assembled and tested at the Research Institute for Optronics and Pattern Recognition in Germany over the last years. Its multisensorial design comprises off-the-shelf components: an FPA infrared camera, a scanning laser radar und an inertial measurement unit. In the paper we describe several possibilities for the use of this multisensor equipment during helicopter missions. We discuss suitable data processing methods, for instance the automatic time synchronization of different imaging sensors, the pixel-based data fusion and the incorporation of collateral information. The results are visualized in an appropriate way to present them on a cockpit display. We also show how our system can act as a landing aid for pilots within brownout conditions (dust clouds caused by the landing helicopter).

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“…Additionally, this allows producing views of the objects from different aspects. The application of the obtained IR-signatures for automatic target recognition is described in [5].…”

Section: )mentioning

confidence: 99%

“…In addition, the reconnaissance results derived from the various data channels have to be processed and stored in a database that is updated continuously. Moreover, the contents of this database have to be visualized for the human observer (decision maker, pilot) in an appropriate way [5].…”

Section: Data Processingmentioning

confidence: 99%

Imaging sensor fusion and enhanced vision for helicopter landing operations

2006

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“…The concepts of combined 2D and 3D data analysis for improved target detection can be used for automatic tracking of vehicles in combination with automatic navigation. In the same way data from unmanned airborne sensor platforms operating in a sensor network can be evaluated [5].…”

Section: Introductionmentioning

confidence: 99%

Generation of efficient 2D templates from 3D multisensor data for correlation-based target tracking

Witte¹,

Armbruster²,

Jäger³

et al. 2008

Signal Processing, Sensor Fusion, and Target Recognition XVII

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The general demand for the prevention of collateral damages in military operations requires methods of robust automatic identification of target objects like vehicles especially during target approach. This requires the development of sophisticated techniques for automatic and semi-automatic interpretation of sensor data. In particular the automatic pre-analysis of reconnaissance data is important for the human observer as well as for autonomous systems. In the phase of target approach fully automatic methods are needed for the recognition of predefined objects. For this purpose appropriate sensors are used like imaging IR sensors suitable for day/night operation and laser radar supplying 3D information of the scenario. Classical methods for target recognition based on comparison with synthetic IR object models imply certain shortcomings, e.g. unknown weather conditions and the engine status of vehicles. We propose a concept of generating efficient 2D templates for IR target signatures based on the evaluation of a precise 3D model of the target generated from real multisensor data. This model is created from near-term laser range and IR data gathered by reconnaissance in advance to gain realistic and up-to-date target signatures. It consists of the visible part of the object surface textured with measured infrared values. This enables recognition from slightly differing viewing angles. Our test bed is realized by a helicopter equipped with a multisensor suite (laser radar, imaging IR, GPS, and IMU). Results are demonstrated by the analysis of a complex scenario with different vehicles

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