Showing papers by "Mats Viberg published in 1999"

Element position considerations for robust direction finding using sparse arrays

Abstract: Estimating the directions to incoming signals using an array of sensors is of interest in a wide range of applications. A uniform sensor spacing is most often assumed. However, arrays employing a sparse spatial sampling make more efficient use of a given number of sensors. This paper re-examines the use of the Dolph-Chebyshev criterion for selecting the element positions. A simplified implementation is proposed. The array design is linked to estimation performance by a numerical example. It is found that at least what concerns the MUSIC algorithm, a small main-lobe area is more important than low sidelobe levels. This remains true even in scenarios with many signals of different power levels present.

Using the bootstrap for robust detection in array signal processing

Abstract: A critical problem in many signal processing applications is the determination of the correct model order for example, the number of multipath components in a received communication signal. One approach to detect the model order is to use the distribution of the criterion function of the estimator applied to find interesting parameters. Unfortunately, the nominal distribution of such a criterion function relies heavily on a correct model of the observed signal. In practice, with modeling errors present, the distribution is unknown. For robust detection based on the criterion function of a certain class of estimators, a two-step procedure is proposed. First, an alternative representation of the residuals is found using a predictor. Second, using bootstrap resampling, a parameter is estimated from the new residuals. This parameter transforms the criterion function to pivotal form. Numerical experiments show robustness to a range of possible modeling errors. An example from real measured array data is included.