2D-DOA Estimation for EMVS Array with Nonuniform Noise

Electromagnetic vector sensor (EMVS) array is one of the most potential arrays for future wireless communications and radars because it is capable of providing two-dimensional (2D) direction-of-arrival (DOA) estimation as well as polarization angles of the source signal. It is well known that existing subspace algorithm cannot directly be applied to a nonuniform noise scenario. In this paper, we consider the 2D-DOA estimation issue for EMVS array in the presence of nonuniform noise and propose an improved subspace-based algorithm. Firstly, it recasts the nonuniform noise issue as a matrix completion problem. The noiseless array covariance matrix is then recovered via solving a convex optimization problem. Thereafter, the shift invariant principle of the EMVS array is adopted to construct a normalized polarization steering vector, after which 2D-DOA is easily estimated as well as polarization angles by incorporating the vector cross-product technique and the pseudoinverse method. The proposed algorithm is effective to EMVS array with arbitrary sensor geometry. Furthermore, the proposed algorithm is free from the nonuniform noise. Several simulations verify the improvement of the proposed method.

Source Localization Using Distributed Electromagnetic Vector Sensors

Electromagnetic vector sensor (EVS) array has drawn extensive attention in the past decades, since it offers two-dimensional direction-of-arrival (2D-DOA) estimation and additional polarization information of the incoming source. Most of the existing works concerning EVS array are focused on parameter estimation with special array architecture, e.g., uniform manifold and sparse arrays. In this paper, we consider a more general scenario that EVS array is distributed in an arbitrary geometry, and a novel estimator is proposed. Firstly, the covariance tensor model is established, which can make full use of the multidimensional structure of the array measurement. Then, the higher-order singular value decomposition (HOSVD) is adopted to obtain a more accurate signal subspace. Thereafter, a novel rotation invariant relation is exploited to construct a normalized Poynting vector, and the vector cross-product technique is utilized to estimate the 2D-DOA. Based on the previous obtained 2D-DOA, the polarization parameter can be easily achieved via the least squares method. The proposed method is suitable for EVS array with arbitrary geometry, and it is insensitive to the spatially colored noise. Therefore, it is more flexible than the state-of-the-art algorithms. Finally, numerical simulations are carried out to verify the effectiveness of the proposed estimator.

Patterns of Learning in Dynamic Technological System Lifecycles—What Automotive Managers Can Learn from the Aerospace Industry?

Not only with respect to the common overlaps within the market of urban air mobility, but also in terms of their requirement profile with regard to the systemic core, all mobility industries are converging. This article focuses on the required patterns of learning in order to cope with these changes, and what automotive managers can learn from the aerospace industry in this context. As organizational learning is the central parameter of economic evolution, and technology develops over trajectory shifts, companies are, at the very least, cyclically forced to learn ambidextrously, or are squeezed out of the market. They have to act and react as complex adaptive systems in their changing environment. Especially in these dynamics, ambidextrous learning is identified to be a conditio sine qua non for organizational success. Especially the combination of efficiency-oriented internal exploitation with an explorative and external-oriented open innovation network turns out to be a superior strategy. By combining patent data, patent citation analysis and data on the European Framework Programs, we show that there are temporal differences, i.e., position of the product in the product, technique, technology, and industry life cycle. Furthermore, we draw a conclusion dependent on the systemic product character, which enforces different learning requirements concerning supply chain position and, as an overarching conclusion, we identify product structure to be decisive for how organizational learning should be styled.

Robust Output Controllability Analysis and Control Design for Incomplete Boolean Networks with Disturbance Inputs

This paper addresses the problems of robust-output-controllability and robust optimal output control for incomplete Boolean control networks with disturbance inputs. First, by resorting to the semi-tensor product technique, the system is expressed as an algebraic form, based on which several necessary and sufficient conditions for the robust output controllability are presented. Second, the Mayer-type robust optimal output control issue is studied and an algorithm is established to find a control scheme which can minimize the cost functional regardless of the effect of disturbance inputs. Finally, a numerical example is given to demonstrate the effectiveness of the obtained new results.

Design and Prototyping Medium-Frequency Transformers Featuring a Nanocrystalline Core for DC–DC Converters

Medium frequency transformers (MFTs) are a key component of DC–DC dual active bridge (DAB)-type converters. These technologies are becoming a quintessential part of renewable energy solutions, such as photovoltaic systems and wind energy power plants, as well as in modern power grid interfaces functioning as solid-state transformers in smart-grid environments. The weight and physical dimensions of an MFT are key data for the design of these devices. The size of an MFT is reduced by increasing its operating frequency. This reduction implicates higher power density through the transformer windings, as well as other design requirements distinct to those used for conventional 60/50 Hz transformers; therefore, new MFT design procedures are needed. This paper introduces a novel methodology for designing MFTs, using nanocrystalline cores, and tests it using an MFT–DAB lab prototype. Different to other MFT design procedures, this new design approach uses a modified version of the area-product technique, which consists of smartly modifying the core losses computation, and includes nanocrystalline cores. The core losses computation is supported by a full analysis of the dispersion inductance. For purposes of validation, a model MFT connected to a DAB converter is simulated in Matlab-Simulink (The MathWorks, v2014a, Mexico City, Mexico). In addition, a MFT–DAB lab prototype (1 kVA at 5 kHz) is implemented to experimentally probe further the validity of the methodology just proposed. These results demonstrate that the analytic calculations results match those obtained from simulations and lab experiments. In all cases, the efficiency of the MFT is greater than 99%.

ON THE TIME-DEPENDENT BEHAVIOR OF A MARKOVIAN REENTRANT-LINE MODEL

We use the random-product technique from [5] to study both the steady-state and time-dependent behavior of a Markovian reentrant-line model, which is a generalization of the preemptive reentrant-line model studied in the work of Adan and Weiss [2]. Our results/observations yield additional insight into why the stationary distribution of the reentrant-line model from [2] exhibits an almost-geometric product-form structure: indeed, our generalized reentrant-line model, when stable, admits a stationary distribution with a similar product-form representation as well. Not only that, the Laplace transforms of the transition functions of our reentrant-line model also have a product-form structure if it is further assumed that both Buffers 2 and 3 are empty at time zero.