Title :
Joint domain space-time adaptive processing with small training data sets
Author :
Pados, Dimatris A. ; Tsao, Tzeta ; Michels, James H. ; Wicks, Mike
Author_Institution :
State Univ. of New York, Buffalo, NY, USA
Abstract :
The classical problem of optimum detection of a complex signal of unknown amplitude in colored Gaussian noise is revisited. The focus, however, is on adaptive system designs with limited training data sets and low computational optimization complexity. In this context, the target vector is equipped with a carefully selected orthogonal auxiliary vector for disturbance suppression with one complex space-time degree of freedom. Direct generalization leads to adaptive generation of a sequence of conditionally optimized weighted auxiliary vectors that are orthogonal to each other and to the target vector of interest. This approach appears here for the first time. Adaptive disturbance suppression with any desired number of complex degrees of freedom below the data dimension is therefore possible. It is shown that processing with multiple auxiliary vectors falls under well known blocking-matrix processing principles. The proposed blocking matrix, however, is data dependent, adaptively generated, and no data eigen analysis is involved. While the issues treated refer to general adaptive detection procedures, the presentation is given in the context of joint space-time adaptive processing for array radars
Keywords :
Gaussian noise; adaptive radar; adaptive signal detection; adaptive signal processing; array signal processing; computational complexity; direction-of-arrival estimation; interference suppression; linear antenna arrays; matrix algebra; optimisation; radar antennas; radar detection; radar interference; radar signal processing; adaptive detection; adaptive disturbance suppression; adaptive generation; adaptive system design; blocking-matrix processing; colored Gaussian noise; complex signal amplitude; complex space-time degree of freedom; conditionally optimized weighted auxiliary vectors; data dimension; joint domain space-time adaptive processing; low computational optimization complexity; optimum detection; orthogonal auxiliary vector; small training data sets; target vector; uniform linear radar array; Adaptive arrays; Adaptive filters; Antenna arrays; Covariance matrix; Gaussian noise; Matched filters; Radar antennas; Radar applications; Testing; Training data;
Conference_Titel :
Radar Conference, 1998. RADARCON 98. Proceedings of the 1998 IEEE
Conference_Location :
Dallas, TX
Print_ISBN :
0-7803-4492-8
DOI :
10.1109/NRC.1998.677984