Abstract-We study compressive sensing in the spatial domain to achieve target localization, specifically direction of arrival (DOA), using multiple-input multiple-output (MIMO) radar. A sparse localization framework is proposed for a MIMO array in which transmit and receive elements are placed at random. This allows for a dramatic reduction in the number of elements needed, while still attaining performance comparable to that of a filled (Nyquist) array. By leveraging properties of structured random matrices, we develop a bound on the coherence of the resulting measurement matrix, and obtain conditions under which the measurement matrix satisfies the so-called isotropy property. The coherence and isotropy concepts are used to establish uniform and non-uniform recovery guarantees within the proposed spatial compressive sensing framework. In particular, we show that non-uniform recovery is guaranteed if the product of the number of transmit and receive elements, M N (which is also the number of degrees of freedom), scales with K (log G) 2 , where K is the number of targets and G is proportional to the array aperture and determines the angle resolution. In contrast with a filled virtual MIMO array where the product M N scales linearly with G, the logarithmic dependence on G in the proposed framework supports the high-resolution provided by the virtual array aperture while using a small number of MIMO radar elements. In the numerical results we show that, in the proposed framework, compressive sensing recovery algorithms are capable of better performance than classical methods, such as beamforming and MUSIC.

Abstract-A novel framework to accurately quantify the effect of stochastic variations of design parameters on the performance of textile antennas is developed and tested. First, a sensitivity analysis is applied to get a rough idea about the effect of these random variations on the textile antenna's performance. Next, a more detailed view is obtained by a Generalized Polynomial Chaos technique that accurately quantifies the statistical distribution of the textile antenna's figures of merit, for a given range over which geometry and material parameters vary statistically according to a given distribution. The method is validated both for a simple inset-fed patch textile microstrip antenna and for a dual-polarized textile antenna. For the latter, the probability density function corresponding to its most sensitive design parameter, being the width, is experimentally estimated by means of measurements performed on 100 patches. A Kolmogorov-Smirnoff test proves that, for all considered examples, the results are as accurate as those obtained via Monte Carlo analysis, while the new technique is much more efficient. Indeed, speedups up to a factor 1667 are demonstrated.

No abstract

No abstract