FEATURE EXTRACTION AND RECOGNITION FOR ECHOES OF HRR RADAR

This paper describes a novel target recognition scheme using High Range Resolution (HRR) radar signatures. AutoRegressive (AR) method is used to extract features from HRR radar echoes based on scattering center model of target. The optimal linear transformation based on Euclidian distribution distance criterion is performed on AR model parameter vectors to reduce dimension of feature vectors further and improve the class discrimination capability of feature vectors. The optimization algorithm is designed utilizing the quadratic property of criterion function and Gaussian kernel based Parzen window density function estimator. The concept of Stochastic Information Gradient (SIG) is incorporated into the gradient of cost function to decrease the computational complexity of the algorithm. Simulation results using three real airplanes,data show the effectiveness of the proposed method.

Reliability-based congestion pricing model under endogenous equilibrated market penetration and compliance rate of ATIS

A reliability-based stochastic system optimum congestion pricing(SSOCP) model with endogenous market penetration and compliance rate in an advanced traveler information systems(ATIS) environment was proposed. All travelers were divided into two classes. The first guided travelers were referred to as the equipped travelers who follow ATIS advice, while the second unguided travelers were referred to as the unequipped travelers and the equipped travelers who do not follow the ATIS advice(also referred to as non-complied travelers). Travelers were assumed to take travel time, congestion pricing, and travel time reliability into account when making travel route choice decisions. In order to arrive at on time, travelers needed to allow for a safety margin to their trip.The market penetration of ATIS was determined by a continuous increasing function of the information benefit, and the ATIS compliance rate of equipped travelers was given as the probability of the actually experienced travel costs of guided travelers less than or equal to those of unguided travelers. The analysis results could enhance our understanding of the effect of travel demand level and travel time reliability confidence level on the ATIS market penetration and compliance rate; and the effect of travel time perception variation of guided and unguided travelers on the mean travel cost savings(MTCS) of the equipped travelers, the ATIS market penetration, compliance rate, and the total network effective travel time(TNETT).

On the local convergence of a stochastic semismooth Newton method for nonsmooth nonconvex optimization

In this work,we present probabilistic local convergence results for a stochastic semismooth Newton method for a class of stochastic composite optimization problems involving the sum of smooth nonconvex and nonsmooth convex terms in the objective function.We assume that the gradient and Hessian information of the smooth part of the objective function can only be approximated and accessed via calling stochastic firstand second-order oracles.The approach combines stochastic semismooth Newton steps,stochastic proximal gradient steps and a globalization strategy based on growth conditions.We present tail bounds and matrix concentration inequalities for the stochastic oracles that can be utilized to control the approximation errors via appropriately adjusting or increasing the sampling rates.Under standard local assumptions,we prove that the proposed algorithm locally turns into a pure stochastic semismooth Newton method and converges r-linearly or r-superlinearly with high probability.