2004 IEEE Radar Conference

Innovative Radar Technologies - Expanding System Capabilities

 
 
 April 26-29, 2004 Wyndham Philadelphia at Franklin Plaza Philadelphia, Pennsylvania
 
 
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Session 5A: Space-Time Adaptive Processing I

Wed, 28 April 2004, 8:00 AM - 9:30 AM


5A.1 Application of space-time techniques in radar systems
5A.2 Classification of training data with reduced-rank generalized inner product
5A.3 Adaptive Doppler filtering applied to modern air traffic control radars
5A.4 A robust loaded reiterative median cascaded canceller

5A.1 Application of space-time techniques in radar systems
By: Abner Ephrath
Rafael, Israel
and: Branka Vucetic
The University of Sydney

? This paper presents a new approach to ROR (Range Only Radar), using S-T (Space ?Time) techniques to enhance received radar signals, in particular reducing the effects of clutter and multipath. The basic principle of S-T systems is that a complete knowledge of the propagation channel is required, at least at the receiver end. The parameters of the propagation channel are used in the process of detection and separation of data collected at the various receive antennas. The paper discusses implementation of these methods in a new approach to radar systems, and some practical problems in achieving these ideas.

5A.2 Classification of training data with reduced-rank generalized inner product
By: Michael A Tinston
Science Applications International Corporation
and: William Ogle
Science Applications International Corporation
and: Michael L Picciolo
Science Applications International Corporation
and: J. Scott Goldstein
Science Applications International Corporation
and: Michael C Wicks
Air Force Research Laboratory
and: Peter Zulch
Air Force Research Laboratory

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.

5A.3 Adaptive Doppler filtering applied to modern air traffic control radars
By: Karen J. Anderson
MIT/Lincoln Laboratory
and: James Ward
MIT/Lincoln Laboratory
and: Robert M. O'Donnell
MIT/Lincoln Laboratory

This paper presents an analysis of the Doppler processing technology currently in use in the nation's terminal airport surveillance radars, and examines possibilities for performance improvement, particularly in the presence of moving clutter. The research focuses on five- and eight-pulse waveform methodologies and their respective detection capabilities given clearly defined rain clutter scenarios. Performance with fixed coefficient filters similar to those used in the existing radars is calculated, followed by performance using an adaptive Doppler filtering technique. Performance is quantified in terms of signal-to-interference ratio at the output of the Doppler filters and resultant probability of detection given a specified probability of false alarm. The results will show that a substantial improvement in detection in the vicinity of rain clutter is realized for both the five- and eight-pulse waveforms when using the adaptive coefficient Doppler filters as compared to the performance observed with the fixed coefficient filters. For constant filter weights, the eight-pulse Doppler filters give significantly better performance in most diverse rain clutter than the five-pulse Doppler filters.

5A.4 A robust loaded reiterative median cascaded canceller
By: Michael L. Picciolo
SAIC
and: Karl Gerlach
NRL

A robust, fast-converging, reduced-rank adaptive processor is introduced, based on diagonally loading the Reiterative Median Cascaded Canceller (RMCC). The new Loaded Reiterative Median Cascaded Canceller (LRMCC) exhibits the highly desirable combination of: 1) convergence-robustness to outliers/targets/non stationary data in adaptive weight training data, like the RMCC, 2) convergence performance that is approximately independent of the interference-plus-noise covariance matrix, like the RMCC, and 3) fast convergence at a rate commensurate with reduced-rank algorithms, unlike the RMCC. Measured airborne radar data from the MCARM Space-Time Adaptive Processing (STAP) database is used to show performance enhancements. It is concluded that the LRMCC is a practical and highly robust replacement for existing reduced-rank adaptive processors, exhibiting superior performance in non ideal measured data environments.

 
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