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Robust waveform design for multistatic cognitive radars

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posted on 2018-01-09, 10:51 authored by Gaia Rossetti, Sangarapillai LambotharanSangarapillai Lambotharan
In this paper we propose robust waveform techniques for multistatic cognitive radars in a signal-dependent clutter environment. In cognitive radar design, certain second order statistics such as the covariance matrix of the clutter, are assumed to be known. However, exact knowledge of the clutter parameters is difficult to obtain in practical scenarios. Hence we consider the case of waveform design in the presence of uncertainty on the knowledge of the clutter environment and propose both worst-case and probabilistic robust waveform design techniques. Initially, we tested our multistatic, signaldependent model against existing worst-case and probabilistic methods. These methods appeared to be over conservative and generic for the considered scenario. We therefore derived a new approach where we assume uncertainty directly on the radar cross-section and Doppler parameters of the clutters. Accordingly, we propose a clutter-specific stochastic optimization that, by using Taylor series approximations, is able to determine robust waveforms with specific Signal to Interference and Noise Ratio (SINR) outage constraints.

Funding

This work has been supported by the Engineering and Physical Science Research Council (EPSRC) grant EP/K014307/1 and the MOD University Defence Research Collaboration (UDRC) in Signal Processing.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

IEEE Access

Citation

ROSSETTI, G. and LAMBOTHARAN, S., 2018. Robust Waveform Design for Multistatic Cognitive Radars. IEEE Access, 6, pp. 7464-7475.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Version

  • NA (Not Applicable or Unknown)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution 3.0 International (CC BY 3.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by/3.0/

Acceptance date

2017-12-09

Publication date

2017-12-13

Notes

This work was published as Open Access by IEEE and is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/

ISSN

2169-3536

Language

  • en

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