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Improving EFA-STAP performance using persymmetric covariance matrix estimation

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This paper deals with the estimation of the clutter covariance matrix in airborne radar space-time adaptive processing (STAP). Based on the persymmetry property, a novel STAP method referred to as persymmetric extended factored processing (Per-EFA) is derived, which can make a more intensive use
of the secondary data and improve the STAP performance in training-limited scenarios. Simulation results demonstrate the effectiveness of this method.

Tong Wang

Field of Interest

“The field of interest shall be the organization, systems engineering, design, development, integration, and operation of complex systems for space, air, ocean, or ground environments. These systems include but are not limited to navigation, avionics, mobile electric power and electronics, radar, sonar, telemetry, military, law-enforcement, automatic test, simulators, and command and control."


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