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Demonstration of knowledge-aided space-time adaptive processing using measured airborne data

Demonstration of knowledge-aided space-time adaptive processing using measured airborne data

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© The Institution of Engineering and Technology
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The design and analysis of a knowledge-aided detector for airborne space-time adaptive processing (STAP) applications are addressed. The proposed processor is composed of a training data selector, which chooses secondary cells best representing the clutter statistics in the cell under test, and an adaptive processor for detection processing. The data selector is a hybrid algorithm, which pre-screens training data through the use of terrain information from the United States Geological Survey. Then, in the second stage, a data-driven selector attempts to eliminate residual non-homogeneities. The performance of this new approach is analysed using measured airborne radar data, obtained from the multi-channel airborne radar measurements program, and is compared with alternative STAP detectors proposed in the open literature.

Inspec keywords: radar detection; space-time adaptive processing; airborne radar; radar receivers; terrain mapping; remote sensing by radar; radar clutter

Other keywords: secondary cell; training data prescreen; knowledge-aided detector; clutter statistics; space-time adaptive processing; United States Geological Survey; terrain information; STAP; multichannel airborne radar; data-driven selector

Subjects: Signal detection; Geophysical techniques and equipment; Radar equipment, systems and applications; Radar theory

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