Dennis W. Davis

Parametric adaptive matched filter for airborne radar applications

The parametric adaptive matched filter (PAMF) for space-time adaptive processing (STAP) is introduced via the matched filter (MF), multichannel linear prediction, and the multichannel LDU decomposition. Two alternative algorithmic implementations of the PAMF are discussed. Issues considered include sample training data size and constant false alarm rate (CFAR). Detection test statistics are estimated for airborne phased array radar measurements, and probability of detection is estimated using simulated phased array radar data for airborne surveillance radar scenarios. For large sample sizes, the PAMF performs close to the MF; performance degrades slightly for small sample sizes. In both sample size ranges, the PAMF is tolerant to targets present in the training set.

Two new model order selection approaches for ARMA system modeling using the two-dimensional frequency domain least square algorithm

The linear algorithm for two-dimensional least square approximation in the frequency domain (2D-FD-LS) is well-established and has been applied successfully to 2D signal representation and image noise cancellation. But it does not give any explicit model order selection method. In this paper, two approaches for model order selection for the 2D-FD-LS algorithm are proposed. One is a rank-checking method, and the other is a grouping measure based method. Analysis and simulation results demonstrate the applicability of these new approaches. The first approach is recommended for deterministic (known spectrum) applications, while the second approach is more suitable for the random case.

Modeling performance of the two dimensional frequency domain least squares algorithm in airborne surveillance radar applications

The application of the two-dimensional frequency domain least squares (2D-FD-LS) algorithm to modeling the ground clutter in airborne surveillance phased array radar scenarios is discussed herein. Model order selection and model stabilization issues are considered also. The stabilized models are used to generate filters to whiten the found clutter. Simulation-based results demonstrate that the ground clutter is modeled effectively.