Quadratic Equality Constraints Research Articles

Design of multiple‐input multiple‐output radar amplitude‐bounded waveforms with desired ambiguity function based on sequential quadratic programming

AbstractThe transmit waveform with ambiguity function specifications in the range‐Doppler plane has been of great interest in traditional single antenna radar systems in recent years. This paper considers the design of waveform sets with a desired slow‐time ambiguity function for multiple‐input multiple‐output radar. Specifically, it is desirable that the ambiguity function has low sidelobes where clutter occurs, as this reduces the impact of the clutter on target detection. Instead of unimodular waveforms, the authors consider amplitude modulation signals and introduce the bound constraint on signal amplitudes into the optimisation for the consideration of transmitter efficiency. The design problem is formulated as minimising the weighted integrated sidelobes level of the ambiguity function over the range‐Doppler plane under bound constraints. The resulting problem has a highly non‐linear objective function and is constrained by bound constraints and quadratic equality (constant energy) constraints, making it difficult to solve. The authors develop an efficient algorithm based on the sequential quadratic programming technique and non‐linear conjugate gradient method to solve it. Simulation results show that the proposed algorithm is superior to the existing methods.

Greedy permanent magnet optimization

A number of scientific fields rely on placing permanent magnets in order to produce a desired magnetic field. We have shown in recent work that the placement process can be formulated as sparse regression. However, binary, grid-aligned solutions are desired for realistic engineering designs. We now show that the binary permanent magnet problem can be formulated as a quadratic program with quadratic equality constraints, the binary, grid-aligned problem is equivalent to the quadratic knapsack problem with multiple knapsack constraints, and the single-orientation-only problem is equivalent to the unconstrained quadratic binary problem. We then provide a set of simple greedy algorithms for solving variants of permanent magnet optimization, and demonstrate their capabilities by designing magnets for stellarator plasmas. The algorithms can a-priori produce sparse, grid-aligned, binary solutions. Despite its simple design and greedy nature, we provide an algorithm that compares with or even outperforms the state-of-the-art algorithms while being substantially faster, more flexible, and easier to use.

Allocation of limited resources under quadratic constraints

The proper allocation/distribution of limited resources is a traditional problem with various applications. The mathematical formulation of such problems usually includes constraints describing the set of feasible solutions (feasible set), from which the (nearly) optimal or equilibrium solution should be selected. Often the feasible set is more difficult to determine than to find the optimal or equilibrium solution. Alternatively, the already known feasible set often makes it easier to select the optimal or equilibrium solution. In some other cases, any feasible solutions are the same satisfactory, additional optimization is needless. Accordingly, the main or only task in many cases is to determine the feasible set itself. In the paper, a new theorem is proved for the explicit expression of properly assigned (dependent) variables by means of the other (independent) variables in a system of inequality and quadratic equality constraints. The sum of the (nonnegative) variables can be either prefixed or not. The constraints may describe the feasible set in various resource allocation tasks (possibly in optimization or game-theoretical contexts) or in other problems. Two new lemmas are proved for supporting the proof of the above mentioned theorem, nevertheless, they can also be considered independent results, which may help future mathematical derivations. Supported by a further new lemma, a practical algorithm is derived for assigning in a feasible way the independent variables, to which (possibly limited) arbitrary nonnegative values can be prescribed. Various practical examples are provided to facilitate utilizing the results.

Novel cooperative localization method of over-the-horizon shortwave emitters based on direction-of-arrival and time-difference-of-arrival measurements

The location of long-distance shortwave radiation sources is practically significant in national defense security. Shortwave multistation-positioning systems mainly include direction-of-arrival (DOA) and time-difference-of-arrival (TDOA) intersection locations. These two systems have their advantages and disadvantages in terms of performance. To combine the advantages of these two positioning technologies, this study focuses on the cooperative positioning problem based on DOA and TDOA measurements. First, the two-dimensional DOA and TDOA observation models are established for the over-the-horizon propagation scenario. Subsequently, the Cramér-Rao lower bound (CRLB) of positioning accuracy is derived in the presence of measurement errors in virtual ionosphere height. Aiming at the strong nonlinear characteristics of the observation equations, a new DOA/TDOA cooperative localization method is proposed. Finally, the asymptotic efficiency of the new estimator is proved using the first-order error analysis theory. The new method comprises two stages. In the first stage, the shortwave DOA observation equation is transformed into a pseudo-linear observation equation by introducing an auxiliary variable, as well as solving a quadratic equation with one unknown. Next, an optimization model with two quadratic equality constraints is constructed, and an iterative optimization algorithm based on matrix $~\bf{QR}$ decomposition is proposed to obtain an intermediate estimate of the emitter position. The second stage transforms the shortwave TDOA observation equation into a pseudo-linear observation equation by combining the intermediate estimates given in the first phase. Also, an iterative-constrained weighted-least-squares estimator based on the matrix decomposition is proposed again to localize the emitter. Simulation results show that the new cooperative localization method has good global convergence, the localization performance can reach the CRLB, and the obtained cooperative gain is quite high.

Strong Duality for General Quadratic Programs with Quadratic Equality Constraints

Strong Duality for General Quadratic Programs with Quadratic Equality Constraints

Fuel-Optimal Guidance for End-to-End Human-Mars Entry, Powered-Descent, and Landing Mission

This article investigates the fuel-optimal guidance problem of the end-to-end human-Mars entry, powered-descent, and landing (EDL) mission. It applies a unified modeling scheme and develops a computationally efficient new optimization algorithm to solve the multiphase optimal guidance problem. The end-to-end EDL guidance problem is first modeled as a multiphase optimal control problem with different dynamics and constraints at each phase. Via polynomial approximation and discretization techniques, this multiphase optimal control problem is then reformulated as a polynomial programming problem. By introducing intermediate variables and quadratic equality constraints, a polynomial program is equivalently converted into a nonconvex quadratically constrained quadratic program (QCQP). Then, a novel customized alternating direction method of multipliers (ADMM) is proposed to efficiently solve the large-scale QCQP with convergence proof to a local optimum under certain conditions on the algorithmic parameters. The fuel savings under the end-to-end human-Mars EDL guidance are verified by comparing to the fuel consumption using the separate phase guidance approach. Furthermore, the computational efficiency of the customized ADMM algorithm is validated by comparing to the state-of-the-art nonlinear programming method. The robustness of the customized ADMM algorithm is verified via extensive simulation cases with random initial conditions.

Automotive Radar-Based Vehicle Tracking Using Data-Region Association

For automotive radar-based extended object tracking, this paper proposes a new approach, which jointly estimates the kinematic state and the extension of a vehicle. The vehicle’s shape is described as a rectangle with its vertices treated as the extension state. “Having a rectangular shape” is described as a quadratic equality constraint on the state. To deal with the challenging problem of modeling measurement (scattering center) distribution over a vehicle, we partition the rectangular area into multiple regions and assume that in each region the scattering centers have a simple distribution. An approach is proposed to associate measurements with these regions. Given an association, the target state is estimated in a linear minimum mean-square-error framework with the shape constraint treated as a pseudo measurement. The probabilities for these regions to generate measurements are updated online. The estimate is then projected into the constraint space. The effectiveness of the proposed approach is illustrated using both simulated and real data.

Online trajectory optimization and guidance algorithm for space interceptors with nonlinear terminal constraints via convex programming

PurposeIn this paper, an online convex optimization method for the exoatmospheric ascent trajectory of space interceptors is proposed. The purpose of this paper is to transform the original trajectory optimization problem into a sequence of convex optimization subproblems.Design/methodology/approachFor convenience in calculating accuracy and efficiency, the complex nonlinear terminal orbital elements constraints are converted into several quadratic equality constraints, which can be better computed by a two-step correction method during the iteration. First, the nonconvex thrust magnitude constraint is convexified by the lossless convexification technique. Then, discretization and successive linearization are introduced to transform the original problem into a sequence of one convex optimization subproblem, considering different flight phases. Parameters of trust-region and penalty are also applied to improve the computation performance. To correct the deviation in real time, the iterative guidance method is applied before orbit injection.FindingsNumerical experiments show that the algorithm proposed in this paper has good convergence and accuracy. The successive progress can converge in a few steps and 3–4 s of CPU time. Even under engine failure or mission change, the algorithm can yield satisfactory results.Practical implicationsThe convex optimization method presented in this paper is expected to generate a reliable optimal trajectory rapidly in different situations and has great potential for onboard applications of space interceptors.Originality/valueThe originality of this paper lies in the proposed online trajectory optimization method and guidance algorithm of the space inceptors, especially for onboard applications in emergency situations.

Indefinite least squares with a quadratic constraint

An abstract indefinite least squares problem with a quadratic constraint is considered. This is a quadratic programming problem with one quadratic equality constraint, where neither the objective nor the constraint is a convex function. Necessary and sufficient conditions are found for the existence of solutions.

State estimation of distribution network based on hybrid measurement combined with multi-source asynchronous data

At present, the redundancy of real-time measurement in distribution network is low and the mixed measurement is incompatible, Hybrid measurement combined with multi-source asynchronous data such as SCADASCADA/ smart distribution transformer information acquisition system (SDTIAS), anew state estimation based on SCADASCADA and SDTIAS is proposed in this paper. Firstly, SDTIAS and SCADA measurements are matched and synchronized; Then, the quadratic multi-source asynchronous measurement model is constructed, the quadratic equality constraint is established, the unified modeling of multi-source asynchronous measurement is realized; Finally, the proposed model and algorithm are simulated and verified in the standard test system, The results show the error seven times lower than traditional methods, and the calculation efficiency (100 Statistics) is increased by 35%, indicating better accuracy and higher efficiency.

NISQ Algorithm for Hamiltonian simulation via truncated Taylor series

Simulating the dynamics of many-body quantum systems is believed to be one of the first fields that quantum computers can show a quantum advantage over classical computers. Noisy intermediate-scale quantum (NISQ) algorithms aim at effectively using the currently available quantum hardware. For quantum simulation, various types of NISQ algorithms have been proposed with individual advantages as well as challenges. In this work, we propose a new algorithm, truncated Taylor quantum simulator (TQS), that shares the advantages of existing algorithms and alleviates some of the shortcomings. Our algorithm does not have any classical-quantum feedback loop and bypasses the barren plateau problem by construction. The classical part in our hybrid quantum-classical algorithm corresponds to a quadratically constrained quadratic program (QCQP) with a single quadratic equality constraint, which admits a semidefinite relaxation. The QCQP based classical optimization was recently introduced as the classical step in quantum assisted eigensolver (QAE), a NISQ algorithm for the Hamiltonian ground state problem. Thus, our work provides a conceptual unification between the NISQ algorithms for the Hamiltonian ground state problem and the Hamiltonian simulation. We recover differential equation-based NISQ algorithms for Hamiltonian simulation such as quantum assisted simulator (QAS) and variational quantum simulator (VQS) as particular cases of our algorithm. We test our algorithm on some toy examples on current cloud quantum computers. We also provide a systematic approach to improve the accuracy of our algorithm.

First- and Second-Order Optimality Conditions for Quadratically Constrained Quadratic Programming Problems

We consider a quadratic programming problem with quadratic cone constraints and an additional geometric constraint. Under suitable assumptions, we establish necessary and sufficient conditions for optimality of a KKT point and, in particular, we characterize optimality by using strong duality as a regularity condition. We consider in details the case where the feasible set is defined by two quadratic equality constraints and, finally, we analyse simultaneous diagonalizable quadratic problems, where the Hessian matrices of the involved quadratic functions are all diagonalizable by means of the same orthonormal matrix.

Location-Based Timing Advance Estimation for 5G Integrated LEO Satellite Communications

Integrated satellite-terrestrial communications networks aim to exploit both the satellite and the ground mobile communications, thus providing genuine ubiquitous coverage. For 5G integrated low earth orbit (LEO) satellite communication systems, the timing advance (TA) is required to be estimated in the initial random access procedure in order to facilitate the uplink frame alignment among different users. However, due to the inherent characteristics of LEO satellite communication systems, e.g., wide beam coverage and long propagation delays, the existing 5G terrestrial uplink TA scheme is not applicable in the satellite networks. In this paper, we investigate location-based TA estimation for 5G integrated LEO satellite communication systems. We obtain the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements in the downlink timing and frequency synchronization phase, which are made from the satellite at different time instants. We propose to take these measurements for either UE geolocation or ephemeris estimation, thus calculating the TA value. The estimation is then formulated as a quadratic optimization problem whose globally optimal solution can be obtained by a quadratic penalty algorithm. To reduce the computational complexity, we further propose an alternative approximation method based on iteratively performing a linearization procedure on the quadratic equality constraints. Numerical results show that the proposed methods can approach the constrained Cramér-Rao lower bound (CRLB) of the TA estimation and thus assure uplink frame alignment for different users.

A QCQP-based splitting SQP algorithm for two-block nonconvex constrained optimization problems with application

This paper discusses a class of two-block nonconvex smooth optimization problems with nonlinear constraints. Based on a quadratically constrained quadratic programming (QCQP) approximation, an augmented Lagrangian function (ALF), and a Lagrangian splitting technique into small-scale subproblems, we propose a novel sequential quadratic programming (SQP) algorithm. First, inspired by the augmented Lagrangian method (ALM), we penalize the quadratic equality constraints associated with the QCQP approximation subproblem in its objective by means of the ALF, and then split the resulting subproblem into two small-scale ones, but both of them are not quadratic programming (QP) due to the square of the quadratic equality constraints in the objective. Second, by ignoring the three-order infinitesimal arising from the squared term, the two small-scale subproblems are reduced to two standard QP subproblems, which can yield an improved search direction. Third, taking the ALF of the discussed problem as a merit function, the next iterate point is generated by the Armijo line search. As a result, a new SQP method, called QCQP-based splitting SQP method, is proposed. Under suitable conditions, the global convergence, strong convergence, iteration complexity and convergence rate of the proposed method are analyzed and obtained. Finally, preliminary numerical experiments and applications were carried out, and these show that the proposed method is promising.

Convex Relaxation of Combined Heat and Power Dispatch

Combined heat and power dispatch promotes interactions and synergies between electric power systems and district heating systems. However, nonlinear and nonconvex heating flow imposes significant challenges on finding qualified solutions efficiently. Most existing methods rely on constant flow assumptions to derive a linear heating flow model, sacrificing optimality for computational simplicity. This paper proposes a novel convex combined heat and power dispatch model based on model simplification and constraint relaxation, which improves solution quality and avoids assumptions on operating regimes of district heating systems. To alleviate mathematical complexity introduced by the commonly used node method, a simplified thermal dynamic model is proposed to capture temperature changes in networked pipelines. Conic and polyhedral relaxations are then applied to convexify the original problems with bilinear and quadratic equality constraints. Furthermore, an adaptive solution algorithm is proposed to successively reduce relaxation gaps based on dynamic bivariate partitioning, improving solution optimality with desirable computational efficiency. The proposed method is verified on a 33-bus electric power system integrated with a 30-node district heating system and compared to nonlinear programming solvers and constant-flow-based solutions.

Phase retrieval of low-rank matrices by anchored regression

Abstract We study the low-rank phase retrieval problem, where our goal is to recover a $d_1\times d_2$ low-rank matrix from a series of phaseless linear measurements. This is a fourth-order inverse problem, as we are trying to recover factors of a matrix that have been observed, indirectly, through some quadratic measurements. We propose a solution to this problem using the recently introduced technique of anchored regression. This approach uses two different types of convex relaxations: we replace the quadratic equality constraints for the phaseless measurements by a search over a polytope and enforce the rank constraint through nuclear norm regularization. The result is a convex program in the space of $d_1 \times d_2$ matrices. We analyze two specific scenarios. In the first, the target matrix is rank-$1$, and the observations are structured to correspond to a phaseless blind deconvolution. In the second, the target matrix has general rank, and we observe the magnitudes of the inner products against a series of independent Gaussian random matrices. In each of these problems, we show that anchored regression returns an accurate estimate from a near-optimal number of measurements given that we have access to an anchor matrix of sufficient quality. We also show how to create such an anchor in the phaseless blind deconvolution problem from an optimal number of measurements and present a partial result in this direction for the general rank problem.

Power Allocation for Reducing PAPR of Artificial-Noise-Aided Secure Communication System

The research of improving the secrecy capacity (SC) of wireless communication system using artificial noise (AN) is one of the classic models in the field of physical layer security communication. In this paper, we consider the peak-to-average power ratio (PAPR) problem in this AN-aided model. A power allocation algorithm for AN subspaces is proposed to solve the nonconvex optimization problem of PAPR. This algorithm utilizes a series of convex optimization problems to relax the nonconvex optimization problem in a convex way based on fractional programming, difference of convex (DC) functions programming, and nonconvex quadratic equality constraint relaxation. Furthermore, we also derive the SC of the proposed signal under the condition of the AN-aided model with a finite alphabet and the nonlinear high-power amplifiers (HPAs). Simulation results show that the proposed algorithm reduces the PAPR value of transmit signal to improve the efficiency of HPA compared with benchmark AN-aided secure communication signals in the multiple-input single-output (MISO) model.

Four-Element Array for GNSS Attitude Determination Using IRLS: An Improved Rounding of Long-Short Baseline Approach

Integer ambiguity resolution (AR) is the key to high-precision attitude determination with carrier phases. Traditional search-based AR methods require considerable computational power that is extremely challenging for high-rate and single-epoch implementations. Whereas, search-free AR methods via direct rounding suffer from the difficult deployment of short baseline and increased measurement noise. In this paper, an improved rounding strategy without any exhaustive search is proposed for properly designed four-element arrays to overcome the deployment issues and improve the AR success rate simultaneously. First, the novel four-element array is proposed to reduce the measurement noise of the virtual baseline, thus improve the AR success rate. The fourth antenna brings another virtual short baseline, such that the measurement quality can be improved by averaging. Second, the average operation is realized after detecting the integer ambiguity parity of each satellite. With a rounding scheme, the detection is performed on an epoch-by-epoch basis. Third, the success rate of AR in the long-short baseline method with a bias term is derived. Fourth, an improved rounding strategy is proposed by resolving the ambiguities sequentially. It is abbreviated as the Improved Rounding of Long-Short baseline (IRLS) approach. The least-squares method with quadratic equality constraints is further incorporated into IRLS to improve the accuracy of baseline estimation, namely QC-IRLS. Last, numerical simulations and field tests are carried out to verify the proposed array geometry and AR approach. In the field test, the proposed array reduces the standard deviation of short baseline estimation by up to two times, wherein all the ambiguities of 1886 valid epochs are successfully resolved.

A Lifting-Penalty Method for Quadratic Programming with a Quadratic Matrix Inequality Constraint

In this paper, a lifting-penalty method for solving the quadratic programming with a quadratic matrix inequality constraint is proposed. Additional variables are introduced to represent the quadratic terms. The quadratic programming is reformulated as a minimization problem having a linear objective function, linear conic constraints and a quadratic equality constraint. A majorization–minimization method is used to solve instead a l 1 penalty reformulation of the minimization problem. The subproblems arising in the method can be solved by using the current semidefinite programming software packages. Global convergence of the method is proven under some suitable assumptions. Some examples and numerical results are given to show that the proposed method is feasible and efficient.

Distributionally Robust Chance-constrained Optimal Power-Gas Flow under Bidirectional Interactions Considering Uncertain Wind Power

This article proposes a distributionally robust chance-constrained (DRCC) model for the optimal power-gas flow (OPGF) problem with uncertain wind power. In the proposed model, a unified affine policy is introduced to adjust the flexible resources in both systems for uncertainty mitigation. Gas-fired units and power-to-gas facilities are considered to exploit the benefits of bidirectional interactions, and distributed gas storage is also incorporated to facilitate the coordinated operation of power and gas systems. Wind power uncertainty is described by an ambiguity set with any distribution built on only the mean and covariance. The distributionally robust chance constraint, especially the two-sided form, is reformulated to an exact second-order cone program. A binary expansion approach is employed to address the bilinear equality constraints. A sequential convexification method is developed to address the nonconvex quadratic equality constraints. Thus, the DRCC-OPGF model is reformulated as a tractable mixed-integer convex programming problem. Numerical results validate the effectiveness of the proposed DRCC-OPGF approach.