Space/time/frequency methods in adaptive radar - New Jersey ...

107also presented. The Sample Matrix Inversion (SMI), Generalized sidelobe canceler(GSC), Eigencanceler, and fixed transforms techniques are all introduced. Targetcancellation and closed form expressions for the SMI and eigencanceler are presented.An expression for fixed transforms is also shown.Reduced-rank methods for STAP based on the signal model were presented inChapter 4. Reduced-rank methods with known and unknown covariance matriceswere analyzed. The analysis is carried out in the framework of the minimum variancebeamformer and the generalized sidelobe canceller. A method was developed toevaluate the theoretical performance of reduced-rank techniques when the rankreductionis carried out by a fixed transform. The PC-SMI technique was recommendedwhen the steering vector lies mostly in the interference subspace, andconversely, when the steering vector is mainly in the noise subspace, the eigencancelermethod should be applied.The application of several reduced-rank methods to the STAP problemwas studied by analysis, simulations, and analysis of real data (in Chapter 5).The motivation for the application of reduced-rank methods is that the STAPproblem is inherently low-rank. Restriction of the number of degrees of freedomthrough the application of reduced-rank methods has the advantage of providingrobust covariance matrix estimates resulting in improved performance over SMI.A taxonomy of reduced-rank methods was presented and specific methods wereanalyzed. The CSNR was defined as a figure of merit for the array performancewhen the interference covariance matrix is estimated from a training set. Variousadaptive methods are compared according to their CSNR performance. A generalexpression for the CSNR density function was developed for reduced-rank methodsutilizing fixed transforms. When compared to the CSNR of SMI, reduced-rankmethods exhibit fewer degrees of freedom and a bias term. While best performanceis obtained using transforms based on the eigendecomposition (data dependent),