Since the case represented by (29) is probably not a particularly important case, this complicated P D expression, and a similar one for the NCGG-NCG case, may not be worth the effort of evaluating. This is especially so in light of the above mentioned approximations that result for cases where X P dominates C P and C P can be ignored, since these approximations are relatively simple.We can dismiss the NCGG-NCGG case as it does not have a practical solution except for the case when X P and C P are fully correlated, and that would contradict the assumption that the target and clutter are independent.
ACKNOWLEDGMENTThe author gratefully acknowledges that the many discussions he had with G. Robert Crane and Fredrick G. Dworshak both of the Raytheon Electronic Systems, contributed significantly to his understanding of radar and radar clutter issues. A hidden Markov model (HMM)-based method for recognizing aerial targets according to the sequential high-range-resolution (HRR) radar signature is presented. Its recognition features are the location information of scattering centers extracted from the HRR radar echoes by the Relax algorithm. The HMM is used to characterize the spatio-temporal information of a target. Several HMMs are cascaded in a chain to model the variation in the target orientation and used as classifiers. Computer simulations with the inverse synthetic aperture radar (ISAR) data are given to demonstrate that for an open-set recognition, average class-recognition rates of 84.50% and 89.88% are achieved, respectively, under two given conditions.
Waveform optimization for multi-input multi-output radar usually depends on the initial parameter estimates (i.e., some prior information on the target of interest and scenario). However, it is sensitive to estimate errors and uncertainty in the parameters. Robust waveform design attempts to systematically alleviate the sensitivity by explicitly incorporating a parameter uncertainty model into the optimization problem. In this paper, we consider the robust waveform optimization to improve the worstcase performance of parameter estimation over a convex uncertainty model, which is based on the Cramer-Rao bound. An iterative algorithm is proposed to optimize the waveform covariance matrix such that the worst-case performance can be improved. Each iteration step in the proposed algorithm is solved by resorting to convex relaxation that belongs to the semidefinite programming class. Numerical results show that the worst-case performance can be improved considerably by the proposed method compared to that of uncorrelated waveforms and the non-robust method.
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