Classification of training data with reduced-rank generalized inner product
Michael A Tinston - Science Applications International Corporation, William Ogle - Science Applications International Corporation, Michael L Picciolo - Science Applications International Corporation, J. Scott Goldstein - Science Applications International Corporation, Michael C Wicks - Air Force Research Laboratory, Peter Zulch - Air Force Research Laboratory
Wed, 28 April 2004, 8:00 AM - 9:30 AM
Selection of training data for space-time adaptive processing in
radar systems remains one of the critical problems to be solved.
The practical application of optimal detection theory relies on a
large number of i.i.d. training samples. The required homogeneity
is typically assumed to be satisfied by range cells adjacent to
the cell under test. This is typically not valid in real-world
applications. The generalized inner product has previously been
proposed to assist in training data selection. This paper
introduces two innovations: 1) the generalized inner product in
the data-adaptive reduced-rank subspace of the multistage Wiener
filter; and 2) classification of the available data into distinct,
self-homogenous sets. Injected targets in recorded data from the
MCARM program are used to assess performance. Training with data
classified within the multistage Wiener filter subspace, also
known as the Krylov subspace, is shown to outperform the
conventional technique of selecting adjacent training cells.
Michael A Tinston - Science Applications International Corporation
Michael A. Tinston received the BSEE degreee from the United States Naval Academy in 1992 and the MSECE degree from the Georgia Institute of Technology in 2001. He is currently a Senior Systems Engineer with Science Applications International Corporation in Chantilly, Virginia where he is engaged in research on reduced rank adaptive signal processing techniques. Previously Mr Tinston was an E-2C Hawkeye Mission Commander and Naval Flight Officer in the United States Navy.