Polarization parameters estimation with scalar sensor arrays

The scalar sensor array (SSA) is generally assumed insensitive to the polarization of impinging signals, and only diversely polarized arrays, such as the vector (crossed-dipole or tripole) sensor array (VSA), can be used for polarization estimation. However, as shown in this paper, with the mutual coupling effect, the SSA can become partially sensitive to polarization of the impinging signals and therefore can be used for polarization parameter estimation. The polarization sensitivity model of an SSA is first established and then as an example, a dimension-reduction method based on multiple signal classification (MUSIC) is employed to jointly estimate the direction-of-arrival and polarization parameters. Computer simulations based on a planar array of circularly polarized microstrip antennas are provided to demonstrate the performance of the proposed method

Efficient subspace fitting algorithms for diversely polarized arrays

Diversely polarized antenna arrays are widely used in RF ap-plications. The diverrity of rerponse provided b y such arrays can greatly improve direction finding performance over arrays sensi-tive to only one polarixation component. For d emitterr, direct implementation of a multidimensional estimation algorithm (e.g., maximum likelihood) requires a search f o r 3d parameters: d direc-tions of arrival (DOAs), and 2d polarization parameters. In this paper, we derive a more &cent solution based on noise subspace fitting (NSF). The NSF algorithm is decoupled into a two-step procedure, where the DOAs are estimated first, and then the p o-larization parametera are obtained b y solving a linear equation. The main advantage of this approach is that the search dimen-sion is reduced b y a factor of three. In addition, the algorithm can be shown to yield asymptotically minimum variance estimates provided no perfectlg coherent signals are present. 1

Radar target characterization by the polarimetric high resolution method

We consider in this paper the characterization of a radar target by the high resolution method (MUSIC) with polarization diversity. A stepped-frequency radar system is used and the target is modelled by the scattering centers . We propose a new high resolution method which exploits optimally the polarization of the received waves, the calculation time of this method is comparable to that of the scalar HR method . We show by simulation that the proposed method can not only give more informations about the target (polarization state), but also provide better performance than the classical scalar high resolution methods in terms of resolution of the scattering centers .Cet article s'inscrit dans le cadre de la caractérisation d'une cible radar par une méthode à haute résolution en incorporant la polarisation des ondes reçues. La cible radar est modélisée par des contributeurs élémentaires et le radar est à diversité de fréquence et de polarisation. On propose une nouvelle méthode généralisant la méthode MUSIC à la diversité de polarisation. Cette méthode permet d'exploiter pleinement l'information contenue dans les signaux vectoriels, tout en gardant un temps de calcul comparable à celui des méthodes ne tenant pas compte de l'aspect vectoriel des signaux reçus. Les simulations montrent que la prise en compte de la polarisation dans la méthode à haute résolution (MUSIC) permet non seulement de fournir plus d'information sur la cible, mais également d'améliorer sensiblement le pouvoir de résolution

Blind Two-Dimensional Super-Resolution and Its Performance Guarantee

In this work, we study the problem of identifying the parameters of a linear system from its response to multiple unknown input waveforms. We assume that the system response, which is the only given information, is a scaled superposition of time-delayed and frequency-shifted versions of the unknown waveforms. Such kind of problem is severely ill-posed and does not yield a unique solution without introducing further constraints. To fully characterize the linear system, we assume that the unknown waveforms lie in a common known low-dimensional subspace that satisfies certain randomness and concentration properties. Then, we develop a blind two-dimensional (2D) super-resolution framework that applies to a large number of applications such as radar imaging, image restoration, and indoor source localization. In this framework, we show that under a minimum separation condition between the time-frequency shifts, all the unknowns that characterize the linear system can be recovered precisely and with very high probability provided that a lower bound on the total number of the observed samples is satisfied. The proposed framework is based on 2D atomic norm minimization problem which is shown to be reformulated and solved efficiently via semidefinite programming. Simulation results that confirm the theoretical findings of the paper are provided