Secondly, by using these observers, adaptive, robust and robust-adaptive control schemes are developed and analyzed.
We then estimated the variogram parameters by method-of-moments (non-robust and robust estimators) and residual maximum likelihood.
In Fig. 4, the worst-case SINR behavior considering the non-robust and robust design of waveform covariance matrix is plotted versus uncertain parameter ε.
Next, we illustrate the gain pattern variation of the beamforming vectors designed from non-robust and robust designs for different uncertainty regions (i.e., different values of ξ).
Fig. 4 The worst-case SINR (dB) behavior versus uncertain parameter ε in uniform clutter environment considering the non-robust and robust design of waveform covariance matrix.
Fig. 1 The worst-case SINR (dB) behavior versus uncertain parameter ε in non-uniform clutter environment considering the non-robust and robust design of waveform covariance matrix.
In addition, we vary the p-robust parameter between [0 1] and calculate the mean and standard deviation for p-robust and robust models.
To evaluate both the p-robust and robust models, two performance measures are used: the mean and standard deviation of objective function under random realizations.
Models 3 and 4, the non-robust and robust versions of the AMG estimator, do not pass the Pesaran (2004) test of cross-sectional dependence for the 81 countries sample, so the results presented should be interpreted with caution.
Table 1 The angles and corresponding powers of seven interfering sources Angle (deg) −20 5 35 60 70 −30 −15 Power (dB) 20 20 30 25 28 30 10. Figure 1 shows the worst-case SINR behavior versus uncertain parameter ε considering the non-robust and robust design of waveform covariance matrix, respectively.
This is due either to the fact that they are located in the flanking regions of the robust TR, or to the similarity between the patterns of non-robust and robust TR.
