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Abstruct-Secondary data selection for estimation of the clutter covariance matrix, needed in space-time adaptive processing (STAP), is normally obtained from range rings nearby the cell under test. The assumption is that these range rings contain cells that are representative of the clutter statistics in the test cell. However, in a nonhomogeneous terrain environment, this may not be true. An innovative approach is presented, in the area of knowledge-aided STAP, which utilizes terrain data from the United States Geological Survey (USGS) to aid in the selection of secondary data cells. Results have been obtained and compared with the sliding (cell averaging symmetric) window method of secondary data selection. This comparison indicates that making use of the surveillance terrain knowledge improves STAP performance.

Genetic algorithms have proven to be useful tools in optimizing complex problems with large solution spaces. Radar waveform selection is a challenging problem that may benefit from the use of genetic algorithms. Furthermore, advances in the areas of waveform diversity, multistatic radars and knowledgeaided radars are making waveform selection even more challenging. As a design tool we used genetic algorithms to perform waveform selection utilizing the autocorrelation and ambiguity functions in the fitness evaluation. Monostatic, bistatic and multistatic notional examples are presented and early results indicate that genetic algorithms can provide a useful and effective tool in waveform selection for a variety of radar configurations.

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

-Future US Air Force sensor systems must beThe