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Träfflista för sökning "L773:2151 870X OR L773:9781479914814 srt2:(2020)"

Sökning: L773:2151 870X OR L773:9781479914814 > (2020)

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1.
  • Klintberg, Jacob, 1994, et al. (författare)
  • Mitigation of Ground Clutter in Airborne Bistatic Radar Systems
  • 2020
  • Ingår i: Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop. - 2151-870X. ; 2020-June
  • Konferensbidrag (refereegranskat)abstract
    • Space-Time Adaptive Processing is a commonly used technique to mitigate ground clutter reflections from an airborne radar system. It estimates a covariance matrix based on spatial and temporal information, and the estimate is thereafter used to suppress the ground clutter. In a side-looking monostatic radar system, the estimate is rather straight forward based on radar observations. However, in this paper, we consider bistatic systems where the power of adaptivity is limited due to nonstationarity of the ground clutter reflections over the range dimension. To overcome this, scenario dependent transformations are commonly used when forming the sample covariance matrix. In this contribution we instead investigate a detector where the clutter covariance matrix is determined from the geometry of the bistatic scenario. Using a Monte-Carlo simulation, we investigate how sensitive the detector is to errors in the assumed geometry, and compare this with state-of-the-art adaptive methods. The results indicates that a good clutter rejection is obtained for errors of order 103 m for assumed transmitter position and 100km/h for assumed transmitter velocity.
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2.
  • Xia, Yuxuan, 1993, et al. (författare)
  • Extended object tracking using hierarchical truncation model with partial-view measurements
  • 2020
  • Ingår i: Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop. - 2151-870X. ; 2020 June
  • Konferensbidrag (refereegranskat)abstract
    • This paper introduces the hierarchical truncated Gaussian model in representing automotive radar measurements for extended object tracking. The model aims at a flexible spatial distribution with adaptive truncation bounds to account for partial-view measurements caused by self-occlusion. Built on a random matrix approach, we propose a new state update step together with an adaptively update of the truncation bounds. This is achieved by introducing spatial-domain pseudo measurements and by aggregating partial-view measurements over consecutive time-domain scans. The effectiveness of the proposed algorithm is verified on a synthetic dataset and an independent dataset generated using the MathWorks Automated Driving toolbox.
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  • Resultat 1-2 av 2

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