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From Matched Spatial Filtering towards the Fused Statistical Descriptive Regularization Method for Enhanced Radar Imaging

Abstract

We address a new approach to solve the ill-posed nonlinear inverse problem of high-resolution numerical reconstruction of the spatial spectrum pattern (SSP) of the backscattered wavefield sources distributed over the remotely sensed scene. An array or synthesized array radar (SAR) that employs digital data signal processing is considered. By exploiting the idea of combining the statistical minimum risk estimation paradigm with numerical descriptive regularization techniques, we address a new fused statistical descriptive regularization (SDR) strategy for enhanced radar imaging. Pursuing such an approach, we establish a family of the SDR-related SSP estimators, that encompass a manifold of existing beamforming techniques ranging from traditional matched filter to robust and adaptive spatial filtering, and minimum variance methods.

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Correspondence to Yuriy Shkvarko.

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Shkvarko, Y. From Matched Spatial Filtering towards the Fused Statistical Descriptive Regularization Method for Enhanced Radar Imaging. EURASIP J. Adv. Signal Process. 2006, 039657 (2006). https://doi.org/10.1155/ASP/2006/39657

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Keywords

  • Manifold
  • Radar
  • Minimum Variance
  • Radar Image
  • Matched Filter