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A unified framework for space-time adaptive processing

Ninth IEEE Signal Processing Workshop on Statistical Signal and Array Processing (Cat. No.98TH8381), 2002
This work provides a common framework for three recently proposed space-time adaptive processing (STAP) methods. A common goal of these methods is to reduce computational complexity and sample support requirements. It is shown that the canonical correlations model provides a mechanism for treating the STAP methods in a unified framework.
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An overview of space-time adaptive processing for radar

2003 Proceedings of the International Conference on Radar (IEEE Cat. No.03EX695), 2004
The paper provides a survey of space-time adaptive processing for radar target detection. Specifically, early work on adaptive array processing from the point of view of maximum signal-to-noise-ratio and minimum mean squared error perspectives are briefly reviewed for motivation. The sample matrix inversion method of I.S. Reed et al.
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Robust techniques in space-time adaptive processing

2004
This chapter is a brief review of several traditional STAP schemes as well as an explanation of the complex detection environments in which many radar signal processors must operate. A review of the deterioration of the performance of algorithms, as well as the benefits and drawbacks of newly proposed adaptive processing approaches, has been provided ...
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Airborne radar space time adaptive processing based on atomic norm minimization

Signal Processing, 2018
Weike Feng, Weike Feng, Yiduo Guo
exaly  

On Clutter Sparsity Analysis in Space–Time Adaptive Processing Airborne Radar

IEEE Geoscience and Remote Sensing Letters, 2013
Hongqiang Wang, Zhaocheng Yang
exaly  

Clutter Rank Estimation for Reduce-Dimension Space-Time Adaptive Processing MIMO Radar

IEEE Sensors Journal, 2017
Weike Feng, Xingyu He, Yongshun Zhang
exaly  

Space-time adaptive monopulse processing for airborne radar in non-homogeneous environments

AEU - International Journal of Electronics and Communications, 2011
Zhigang Su
exaly  

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