MS'06 Proceedings of the 17th IASTED international conference on Modelling and simulation
EURASIP Journal on Applied Signal Processing
International Journal of Computational Science and Engineering
Structured covariance matrix estimation for the range-dependent problem in STAP
ARP '07 The Fourth IASTED International Conference on Antennas, Radar and Wave Propagation
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Intelligent Experiment Design-Based Virtual Remote Sensing Laboratory
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
EURASIP Journal on Advances in Signal Processing
MCPR'10 Proceedings of the 2nd Mexican conference on Pattern recognition: Advances in pattern recognition
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The problem of estimating, from one random realization of the remotely sensed signal, the spatial spectrum pattern (SSP) of the wavefield sources distributed in the environment is cast in the framework of Bayesian estimation theory. The kernel spectral estimation method that is familiar, for the classical SSP estimation problem, with the Fourier transform operator and white noise in the observations is extended to incorporate spatial correlation in the data, the system-oriented model of the signal formation operator, and the maximum entropy (ME) statistical a priori information about the SSP. To derive the estimate of the SSP, we applied the Bayesian strategy for maximization of the a posteriori probability density function of the randomized ME model of the SSP. The estimator was obtained as a nonlinear adaptive algorithm that also permits a concise robust implementation. The optimal algorithm implies formation of the second-order sufficient statistics of the data and their smoothing by applying the window operator. The new formalism of the sufficient statistics and windows, explaining their adjustment to the metrics in a solution space, a priori nonparametric model and assumed correlation properties of the desired SSP, is developed. Simulation results are included to illustrate the overall performance of the proposed method in an example of application to radar image formation.