| Full text | |
| Author(s): |
Fa, Rui
;
de Lamare, Rodrigo C.
;
Nascimento, Vitor H.
;
IEEE
Total Authors: 4
|
| Document type: | Journal article |
| Source: | 2010 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING; v. N/A, p. 4-pg., 2010-01-01. |
| Abstract | |
Knowledge-aided space-time adaptive processing (KA-STAP) algorithms, which incorporate a priori knowledge into radar signal processing methods, have the potential to substantially enhance detection performance while combating heterogeneous clutter effects. In this paper, we develop a KA-STAP algorithm to estimate the inverse interference covariance matrix rather than the covariance matrix itself, by combining the inverse of the covariance known a priori, R-0(-1), and the inverse sample covariance matrix estimate (R) over cap -1. The computational load is greatly reduced due to the avoidance of the matrix inversion operation. We also develop a cost-effective algorithm based on the minimum variance (MV) criterion for computing the mixing parameter that performs a convex combination of R-0(-1) and (R) over cap -1. Simulations show the potential of our proposed algorithm, which obtain substantial performance improvements over prior art. (AU) | |
| FAPESP's process: | 08/04828-5 - Adaptive filters for audio applications |
| Grantee: | Vitor Heloiz Nascimento |
| Support Opportunities: | Regular Research Grants |