Objective array design for three-dimensional temperature and salinity observation:Application to the South China Sea

In this study,a moored array optimization tool(MAOT)was developed and applied to the South China Sea(SCS)with a focus on three-dimensional temperature and salinity observations.Application of the MAOT involves two steps:(1)deriving a set of optimal arrays that are independent of each other for different variables at different depths based on an empirical orthogonal function method,and(2)consolidating these arrays using a K-center clustering algorithm.Compared with the assumed initial array consisting of 17 mooring sites located on a 3°×3°horizontal grid,the consolidated array improved the observing ability for three-dimensional temperature and salinity in the SCS with optimization efficiencies of 19.03%and 21.38%,respectively.Experiments with an increased number of moored sites showed that the most cost-effective option is a total of 20 moorings,improving the observing ability with optimization efficiencies up to 26.54%for temperature and 27.25%for salinity.The design of an objective array relies on the ocean phenomenon of interest and its spatial and temporal scales.In this study,we focus on basin-scale variations in temperature and salinity in the SCS,and thus our consolidated array may not well resolve mesoscale processes.The MAOT can be extended to include other variables and multi-scale variability and can be applied to other regions.

Dwell Scheduling Algorithm for Digital Array Radar

Aiming at the problem of resource allocation for digital array radar（ DAR）,a dwell scheduling algorithm is proposed in this paper. Firstly,the integrated priority of different radar tasks is designed,which ensures that the imaging tasks are scheduled without affecting the search and tracking tasks; Then,the optimal scheduling model of radar resource is established according to the constraints of pulse interleaving; Finally,a heuristic algorithm is used to solve the problem and a sparse-aperture cognitive ISAR imaging method is used to achieve partial precision tracking target imaging. Simulation results demonstrate that the proposed algorithm can both improve the performance of the radar system,and generate satisfactory imaging results.

Load optimal design for a primary test stand facility based on a zero-dimensional load model

In order to couple the numerical simulation of a primary test stand driver with an optimal load design, a zero- dimensional wire array load model is designed based on the Saturn load model using PSPICE, which is an upgraded version of the Simulation Program with Integrated Circuit Emphasis （SPICE） designed by the ORCAD Corporation to perform circuit simulations. This paper calculates different load parameters and discusses factors influencing the driving current curve. With appropriate driving current curves chosen, further magneto-hydrodynamic calculations are carried out and discussed to provide the best results for experiments. The suggested optimal load parameters play an important role in experimental load design.

Model-based optimization of phased array ultrasonic testing

Simulation of phased array beams in dovetail and austenitic welds is conducted to optimize the setup of phased array ultrasonic testing（PAUT）.To simulate the beam in such material with complex geometry or with characteristic of anisotropy and inhomogeneity, firstly,linear phased multi-Gaussian beam（LPMGB） models are introduced and discussed. Then,in the case of dovetail,wedge is designed to maximize the stable amplitude of the beam along the steering path;in the case of austenitic weld,modified focal law are developed to solve the problem of beam screwing and defocusing due to the material properties.To verify the effectiveness of the modified focal law,beam fields are calculated using LPMGB model in austenitic welds.

DOA estimation via sparse recovering from the smoothed covariance vector

A direction of arrival（DOA） estimation algorithm is proposed using the concept of sparse representation. In particular, a new sparse signal representation model called the smoothed covariance vector（SCV） is established, which is constructed using the lower left diagonals of the covariance matrix. DOA estimation is then achieved from the SCV by sparse recovering, where two distinguished error limit estimation methods of the constrained optimization are proposed to make the algorithms more robust. The algorithm shows robust performance on DOA estimation in a uniform array, especially for coherent signals. Furthermore, it significantly reduces the computational load compared with those algorithms based on multiple measurement vectors（MMVs）. Simulation results validate the effectiveness and efficiency of the proposed algorithm.