Novel passive localization algorithm based on double side matrix-restricted total least squares

AbstractIn order to solve the bearings-only passive localization problem in the presence of erroneous observer position, a novel algorithm based on double side matrix-restricted total least squares (DSMRTLS) is proposed. First, the aforementioned passive localization problem is transferred to the DSMRTLS problem by deriving a multiplicative structure for both the observation matrix and the observation vector. Second, the corresponding optimization problem of the DSMRTLS problem without constraint is derived, which can be approximated as the generalized Rayleigh quotient minimization problem. Then, the localization solution which is globally optimal and asymptotically unbiased can be got by generalized eigenvalue decomposition. Simulation results verify the rationality of the approximation and the good performance of the proposed algorithm compared with several typical algorithms

In-Plane Behavior of Masonry Infill Wall Considering Out-of-Plane Loading

The in-plane seismic performance of framed masonry wall has been studied by many researchers all over the world, whereas structures subject not only in-plane load but also outof-plane load simultaneously in actual events. This paper utilizes three dimensional nonlinear finite element analysis techniques to investigate the eect of out-of-plane load on the inplane seismic behavior of reinforced concrete (RC) framed masonry wall. The analytical results indicate that the pre-applied out-of-plane loads alter the in-plane failure mode and reduce the in-plane strength of the masonry infilled RC frame. When the out-of-plane load is small enough, masonry wall fails in terms of in-plane strut mode. With the increase of the out-of-plane load, failure mode under in-plane load tends to become arching failure mode

A New Class of Non-Quest-Newton Methods and Their Global Convergence with Goldstein Line Search

In this paper, on the basis of the DFP method a class of non-quasi-Newton methods is presented. Under some condition the global convergence property of these methods with Goldstein line search on uniformly convex objective function is proved