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In this letter, the minimization of the power losses in time-modulated arrays is addressed by means of a suitable strategy based on Particle Swarm Optimization. By properly modifying the modulation sequence, the method is aimed at reducing the amount of wasted power, analytically computed through a very effective closed-form relationship, while constraining the radiation pattern at the carrier frequency below a fixed sidelobe level. Representative results are reported and compared with previously published solutions to assess the effectiveness of the proposed approach.

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A new approach based the contrast field (CF) formulation of the microwave imaging problem that exploits the Bayesian compressive sampling (BCS) paradigm is proposed for the reconstruction of sparse distributions of weak scatterers. Towards this end, the original inverse scattering problem is recast to a probabilistic sparseness constrained optimization by introducing suitable hierarchical priors as sparsity constraints. A fast relevance vector machine (RVM) is then employed to reconstruct the scatterers as well as to estimate the "confidence level" of the inversion. Representative numerical results are presented to illustrate the method as well as to assess its potentialities and limitations in terms of inversion accuracy, computational efficiency, and robustness. Comparisons with state-of-the-art deterministic and stochastic reconstruction methodologies still within the Born approximation (BA) are discussed, as well.Index Terms-Bayesian compressive sampling (BCS), contrast field (CF) formulation, first order Born approximation, inverse scattering, microwave imaging, relevance vector machine (RVM).

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