White noise reduction for wideband linear array signal processing

The performance of wideband array signal processing algorithms is dependent on the noise level in the system. A method is proposed for reducing the level of white noise in wideband linear arrays via a judiciously designed spatial transformation followed by a bank of highpass filters. A detailed analysis of the method and its effect on the spectrum of the signal and noise is presented. The reduced noise level leads to a higher signal to noise ratio (SNR) for the system, which can have a significant beneficial effect on the performance of various beamforming methods and other array signal processing applications such as direction of arrival (DOA) estimation. Here we focus on the beamforming problem and study the improved performance of two well-known beamformers, namely the reference signal based (RSB) and the linearly constrained minimum variance (LCMV) beamformers. Both theoretical analysis and simulation results are provided

Continuous-Space Gaussian Process Regression and Generalized Wiener Filtering with Application to Learning Curves

Abstract. Gaussian process regression is a machine learning paradigm, where the regressor functions are modeled as realizations from an a priori Gaussian process model. We study abstract continuous-space Gaussian regression problems where the training set covers the whole input space instead of consisting of a finite number of distinct points. The model can be used for analyzing theoretical properties of Gaussian process regressors. In this paper, we present the general continuous-space Gaussian process regression equations and discuss their close connection with Wiener filtering. We apply the results to estimation of learning curves as functions of training set size and input dimensionality

Optimal Path Planning for Information based Localization

International audienceThis paper addresses the problem of optimizing the navigation of an intelligent mobile in a real world environment, described by a map. The map is composed of features representing natural landmarks in the environment. The vehicle is equipped with a sensor which implies range and bearing measurements from observed landmarks. These measurements are correlated with the map to estimate the mobile localization through a filtering algorithm. The optimal trajectory can be designed by adjusting a measure of performance for the filtering algorithm used for the localization task. As the state of the mobile and the measurements provided by the sensors are random data, criterion based on the estimation of the Posterior Cramer-Rao Bound (PCRB) is a well-suited measure. A natural way for optimal path planning is to use this measure of performance within a (constrained) Markovian Decision Process framework and to use the Dynamic Programming method for optimizing the trajectory. However, due to the functional characteristics of the PCRB, Dynamic Programming method is generally irrelevant. We investigate two different approaches in order to provide a solution to this problem. The first one exploits the Dynamic Programming algorithm for generating feasible trajectories, and then uses Extreme Values Theory (EV) in order to extrapolate the optimum. The second one is a rare evnt simulation approach, the Cross-Entropy (CE) method introduced by Rubinstein & al. As a result of our implementation, the CE optimization is assessed by the estimated optimum derived from the EV