From Matched Spatial Filtering towards the Fused Statistical Descriptive Regularization Method for Enhanced Radar Imaging
- 630 Downloads
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.
KeywordsManifold Radar Minimum Variance Radar Image Matched Filter
- 1.Haykin S, Steinhardt A (Eds): Adaptive Radar Detection and Estimation. John Wiley & Sons, New York, NY, USA; 1992.Google Scholar
- 2.Henderson FM, Lewis AJ (Eds): Principles and Applications of Imaging Radar: Manual of Remote Sensing. Volume 2. 3d edition. John Wiley & Sons, New York, NY, USA; 1998.Google Scholar
- 3.Shkvarko Y, Leyva-Montiel JL: Theoretical aspects of array radar imaging via fusing the experiment design and regularization techniques. Proceedings of the 2nd IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM '02), August 2002, Rosslyn, Va, USA 115–119. CD ROMGoogle Scholar
- 17.Shkvarko Y, Villalon-Turrubiates IE: Intelligent processing of remote sensing imagery for decision support in environmental resource management: a neural computing paradigm. Proceedings of Information Resource Management Association International Conference (IRMA '05), May 2005, San Diego, Calif, USA CD ROMGoogle Scholar