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Power flow is an extensively used tool in various operations and planning of power systems. In this paper, we propose an efficient hybrid method for solving the power flow problems in ill-conditioned power systems. This method is implemented in a framework of two-part alternating iteration. In Part I of this method, PV bus voltages are regarded as constant and PQ bus voltages are updated by the Z-bus Gauss method. In Part II of this method, PQ bus loads are equivalent to dynamic admittances, and PV bus voltage angles are updated by Newton’s method. The values of PQ bus voltage magnitudes and PV bus voltage angles are passed between Part I and Part II. The proposed method is validated in well- and ill-conditioned systems and compared with several well-known power flow methods. Results show that the proposed method is robust and efficient to address the issues related with large-scale ill-conditioned power systems and it is not significantly affected by the considered initial guess.

Abstract This study addresses the problem of geolocating a strictly non‐circular source on the surface of Earth by a cluster of passive satellites. The known satellite positions and velocities are subject to random errors. The authors propose a single‐step satellite geolocation algorithm that directly localises the transmitter from sensor outputs using the information of time delays and Doppler shifts but without explicitly estimating them. It exploits the non‐circular property of signals and a priori information of satellite orbit error distribution to jointly calibrate orbit errors and determine the longitude and latitude of the transmitter based on the ellipsoidal Earth model, which integrates an alternating iteration scheme for the estimation of various unknowns instead of the exhaustive grid search. Additionally, a detailed Cramér‐Rao bound (CRB) derivation is presented for the single‐step satellite geolocation of a non‐circular source on Earth with and without satellite orbit perturbations, and it is proved that these CRBs are lower than the associated CRBs for a circular source. The simulation results illustrate that the proposed method asymptotically attains the associated CRB, and shows greater performance robustness to signal‐to‐noise ratio (SNR) and satellite orbit errors compared with conventional two‐step satellite geolocation approaches.

This brief considers an efficient spectrum sharing strategy for radar and communication with space-time design, in which the performances of radar and communication systems are simultaneously optimized, under the radar constant modulus constraint and communication energy constraint. The degrees of freedom under the designer’s control are the spatial-temporal transmit waveform and receive filter for radar, as well as the communication system spatial-temporal codebook matrix. For the designed non-convex optimization, an alternating procedure based on majorization-minimization (MM) framework, minimum variance distortionless response (MVDR) and alternating direction method of multipliers (ADMM) is derived for the triplet variables design. Finally, thorough numerical results show the merits of the proposed schemes. [ABSTRACT FROM AUTHOR]

With the development of intelligent transportation, radar and communication on moving platforms are competing for the spectrum. In this paper, we propose and demonstrate a new algorithmic framework for radar-communication spectral coexistence system on moving platform with mutual interference suppression, in which communication rate and the radar signal-to-interference-plus-noise ratio (SINR) are simultaneously optimized, under the energy constraints for the two systems and the radar constant modulus constraint. The radar spatial-temporal filter at the receiver and transmitting waveform are optimized, while the codebook matrix is optimized for the communication system. To cope with the established non-convex problem with triplet variables, we decouple the original problem into multiple subproblems, for which an alternating algorithm based on iterative procedures is derived with lower computational complexity. Specifically, the subproblems of communication codebook and radar filter design are convex and the closed-form solutions can be easily obtained, while the radar waveform optimization is non-convex. Then we propose a novel scheme by exploiting the alternating direction method of multipliers (ADMM) based on minorization-maximization (MM) framework. Finally, to reveal the effectiveness of the proposed algorithm in different scenarios, numerical results are provided. [ABSTRACT FROM AUTHOR]

This paper considers a novel cooperative design for radar-communication spectral sharing system with multiple-input multiple-output (MIMO) structure based on mutual information optimization. We present a new spectral sharing framework aiming at maximizing the radar mutual information. Specifically, the transmit sequences including radar waveform and communication codebook are designed simultaneously based on radar and communication system mutual information optimization. Subsequently, an iterative procedure is devised to maximize the radar mutual information under the constraints including the transmit power, the constant modulus requirement for radar waveform, and the communication system mutual information performance requirement constraint. The proposed approach exploits the alternating optimization based on the Minorization-Maximization (MM) framework, Lagrange algorithm, and the alternating direction method of multipliers (ADMM) techniques to decompose the original design into two simpler subproblems with a closed-form solution. Simulation results are presented to demonstrate the advantage of the proposed schemes. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• The purpose of this article is to study a power flow algorithm with strong convergence, initial value adaptability, and efficiency. • This paper proposes a two-part alternating iterative framework based on dynamic equivalent admittance. • The proposed algorithm has universality for well-and ill-conditioned systems. • The proposed method combines the convergence characteristics of the Z-bus Gauss method and Newton's method, so it is easy to implement based on existing program codes. Power flow is an extensively used tool in various operations and planning of power systems. In this paper, we propose an efficient hybrid method for solving the power flow problems in ill-conditioned power systems. This method is implemented in a framework of two-part alternating iteration. In Part I of this method, PV bus voltages are regarded as constant and PQ bus voltages are updated by the Z-bus Gauss method. In Part II of this method, PQ bus loads are equivalent to dynamic admittances, and PV bus voltage angles are updated by Newton's method. The values of PQ bus voltage magnitudes and PV bus voltage angles are passed between Part I and Part II. The proposed method is validated in well- and ill-conditioned systems and compared with several well-known power flow methods. Results show that the proposed method is robust and efficient to address the issues related with large-scale ill-conditioned power systems and it is not significantly affected by the considered initial guess. [ABSTRACT FROM AUTHOR]

This study addresses the problem of geolocating a strictly non‐circular source on the surface of Earth by a cluster of passive satellites. The known satellite positions and velocities are subject to random errors. The authors propose a single‐step satellite geolocation algorithm that directly localises the transmitter from sensor outputs using the information of time delays and Doppler shifts but without explicitly estimating them. It exploits the non‐circular property of signals and a priori information of satellite orbit error distribution to jointly calibrate orbit errors and determine the longitude and latitude of the transmitter based on the ellipsoidal Earth model, which integrates an alternating iteration scheme for the estimation of various unknowns instead of the exhaustive grid search. Additionally, a detailed Cramér‐Rao bound (CRB) derivation is presented for the single‐step satellite geolocation of a non‐circular source on Earth with and without satellite orbit perturbations, and it is proved that these CRBs are lower than the associated CRBs for a circular source. The simulation results illustrate that the proposed method asymptotically attains the associated CRB, and shows greater performance robustness to signal‐to‐noise ratio (SNR) and satellite orbit errors compared with conventional two‐step satellite geolocation approaches. [ABSTRACT FROM AUTHOR]

This brief investigates a spectrum sharing architecture for a colocated MIMO radar and a MIMO communication system. We develop an alternative formulation for joint resource allocation, aiming at minimizing the effective power of radar interference under the power and similarity constraints. The communication system mutual information (MI) is also simultaneously concerned in order to guarantee the communication performance. The degrees of freedom for system design are both the radar transceiver and the communication transmitter. For the joint designed original non-convex problem, an alternating iterative algorithm with multi-subproblems is derived based on the majorization-minimization algorithm, minimum variance distortionless response (MVDR) and semidefinite programming (SDP) algorithm. The convergence of the algorithm is guaranteed. Finally, we assess the effectiveness of the proposed approach via numerical results. [ABSTRACT FROM AUTHOR]

With the development of intelligent transportation, radar and communication on moving platforms are competing for the spectrum. In this paper, we propose and demonstrate a new algorithmic framework for radar-communication spectral coexistence system on moving platform with mutual interference suppression, in which communication rate and the radar signal-to-interference-plus-noise ratio (SINR) are simultaneously optimized, under the energy constraints for the two systems and the radar constant modulus constraint. The radar spatial-temporal filter at the receiver and transmitting waveform are optimized, while the codebook matrix is optimized for the communication system. To cope with the established non-convex problem with triplet variables, we decouple the original problem into multiple subproblems, for which an alternating algorithm based on iterative procedures is derived with lower computational complexity. Specifically, the subproblems of communication codebook and radar filter design are convex and the closed-form solutions can be easily obtained, while the radar waveform optimization is non-convex. Then we propose a novel scheme by exploiting the alternating direction method of multipliers (ADMM) based on minorization-maximization (MM) framework. Finally, to reveal the effectiveness of the proposed algorithm in different scenarios, numerical results are provided.

We propose an algorithm for solution of high-dimensional evolutionary equations (ODEs and discretized time-dependent PDEs) in the Tensor Train (TT) decomposition, assuming that the solution and the right-hand side of the ODE admit such a decomposition with a low storage. A linear ODE, discretized via one-step or Chebyshev differentiation schemes, turns into a large linear system. The tensor decomposition allows to solve this system for several time points simultaneously using an extension of the Alternating Least Squares algorithm. This method computes a reduced TT model of the solution, but in contrast to traditional offline-online reduction schemes, solving the original large problem is never required. Instead, the method solves a sequence of reduced Galerkin problems, which can be set up efficiently due to the TT decomposition of the right-hand side. The reduced system allows a fast estimation of the time discretization error, and hence adaptation of the time steps. Besides, conservation laws can be preserved exactly in the reduced model by expanding the approximation subspace with the generating vectors of the linear invariants and correction of the Euclidean norm. In numerical experiments with the transport and the chemical master equations, we demonstrate that the new method is faster than traditional time stepping and stochastic simulation algorithms, whereas the invariants are preserved up to the machine precision irrespectively of the TT approximation accuracy. [ABSTRACT FROM AUTHOR]

The lp (0 1 regularization. Recently, we developed an approximate general analytic thresholding representation for any lp regularization with 0 1 regularization and the hard-threshold filtering for l0 regularization. Because the lp regularization is a nonconvex problem, an iterative algorithm can only converge to local optima instead of the global optimum. In this paper, we propose an alternating iteration algorithm for computed tomography reconstruction in a thresholding form based on our general analytic thresholding representation for better convergent properties. The alternating iteration algorithm alternatively minimizes one l1 and one lp (0 1 regularization can help to monitor the solution not away from the global optimum. Both numerical simulations and phantom experiments are performed to evaluate the proposed alternating iteration algorithm. Compared with the lp (0

Highlights • The proposed methods are more precise than the conventional two-step approaches in the presence of observer location errors. • The proposed methods are more robust than the two-step methods in poor location geometry. • The improved performance gained from consideration of observer location errors is more prominent at high signal-to-noise ratios (SNRs) or for large observer location errors. • The proposed method for a known signal exhibits superior localization performance to the proposed method for an unknown signal even for a weak source in the presence of observer location uncertainties. Abstract Direct position determination (DPD) is a promising technique that offers superior performance compared with conventional two-step localization methods. Existing DPD methods presume that the observer locations are known exactly, whereas in practical environments, a small error in the observer locations will lead to an erroneous localization. This study considers the localization of a stationary transmitter by separated moving arrays from passive measurements taken at different points along the trajectory. The precise locations and velocities of the observers are not available, but their errors are assumed to be Gaussian distributed. Using this probability distribution, we propose maximum likelihood-based DPD approaches in the presence of observer location errors for both unknown and known signals. The proposed DPDs rely on alternating iteration schemes, which reduce the multidimensional nonlinear optimization problem to optimizations of dimensions that are much smaller than the number of unknowns. As opposed to the conventional two-step methods that extract measurement parameters and then estimate the positions from them, the proposed DPDs achieve the localization in a single step by exploiting the information of angles, time delays, and Doppler frequency shifts, but without computing them. Additionally, we derive the Cramér–Rao bound (CRB) formula for this DPD problem in the presence of observer location errors. The simulation results prove that the performance of our methods attains the associated CRB. Moreover, they are more robust than the conventional two-step approaches with respect to observer location errors. We demonstrate our methods for the scenario of multiple moving arrays, but these methods can easily be extended to DPD problems accounting for observer location errors in different scenarios. [ABSTRACT FROM AUTHOR]

This paper investigates the shape reconstructions of sub-wavelength objects from near-field measurements in transverse electromagnetic scattering. This geometric inverse problem is notoriously ill-posed and challenging. We develop a novel reconstruction scheme of using plasmon resonances with significantly enhanced sensitivity and resolution. First, by spectral analysis we establish a sharp quantitative relationship between the sensitivity of the reconstruction and the plasmon resonance. It shows that the sensitivity functional blows up when plasmon resonance occurs. Hence, the signal to noise ratio is significantly improved and the robustness and effectiveness of the reconstruction are ensured. Second, a variational regularization method is proposed to overcome the ill-posedness, and an alternating iteration method is introduced to automatically select the regularization parameters. Third, we use Laplace approximation method to capture the statistical information of the target scattering object. Both rigorous theoretical analysis and extensive numerical experiments are conducted to validate the promising features of our method. [ABSTRACT FROM AUTHOR]

Consider the reconstruction of the complex refraction index of an object, which is immersed in a known homogeneous background, from the knowledge of scattered waves of the point sources outside of the object. We firstly establish the uniqueness for this inverse problem, which provides the theoretical basis for the reconstruction scheme. Then based on the contrast source inversion (CSI) method, we propose an algorithm determining the refraction index and the artificial wave sources alternately by a dynamic iterative scheme. The algorithm defines the iterates by solving a series of minimization problems with uniformly convex penalty terms, which are allowed to be non-smooth to include L1 and total variation like functionals, ensuring the reconstruction quality when the unknown refraction index has the special features such as sparsity and discontinuity. By choosing the regularizing parameter automatically, the algorithm is terminated in terms of discrepancy principle. The convergence property of the iterative sequence is rigorously proven. Numerical implementations demonstrate the validity of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of active calibration when channel gain/phase uncertainties and sensor mutual coupling effects are simultaneously present. The numerical algorithms used to compensate for array error matrix, which is formed by the product of mutual coupling matrix and channel gain/phase error matrix, are presented especially tailored to uniform linear array (ULA) and uniform circular array (UCA). First, the array spatial responses corresponding to different azimuths are numerically evaluated using a set of time-disjoint auxiliary sources at known locations. Subsequently, a least-squares (LS) minimization model with respect to array error matrix is established. To solve this LS problem, two novel algorithms, namely algorithm I and algorithm II, are developed. In algorithm I, the array error matrix is considered as a whole matrix parameter to be optimized and an explicit closed-form solution to the error matrix is obtained. Compared with some existing algorithms with similar computation framework, algorithm I is able to utilize all potentially linear characteristics of ULA's and UCA's error matrix, and the calibration accuracy can be increased. Unlike algorithm I, algorithm II decomposes the array error matrix into two matrix parameters (i.e., mutual coupling matrix and channel gain/phase error matrix) to be optimized and all (nonlinear) numerical properties of the error matrix can be exploited. Therefore, algorithm II is able to achieve better calibration precision than algorithm I. However, algorithm II is more computationally demanding as a closed-form solution is no longer available and iteration computation is involved. In addition, the compact Cramér-Rao bound (CRB) expressions for all array error parameters are deduced in the case where auxiliary sources are assumed to be complex circular Gaussian distributed. Finally, the two novel algorithms are appropriately extended to the scenario where non-circular auxiliary sources are used, and the estimation variances of the array error parameters can be further decreased if the non-circularity is properly employed. Simulation experiments show the superiority of the presented algorithms. [ABSTRACT FROM AUTHOR]

A novel variational model for removing multiplicative noise is proposed in this paper. In the model, a novel regularization term is elaborately designed which is inherently equivalent to a combination of the classical total variation regularizer and a nonconvex regularizer. The proposed regularization term, on the one hand, can better remove the noise in homogeneous regions of a noisy image and, on the other hand, can preserve edge details of the image during the denoising process. In order to solve the model efficiently, we design an alternating iteration process in which two coupling minimization problems are solved. For each of the two minimization problems, the existence and uniqueness of their solutions are proved under some necessary assumptions. Numerical results are reported to demonstrate the effectiveness of the proposed regularization term for multiplicative noise removal. [ABSTRACT FROM PUBLISHER]