Linearly Constrained Research Articles

Adaptive Antenna Utilizing Power Inversion and Linearly Constrained Minimum Variance Algorithms

This paper presents a new algorithm based on the power inversion (PI) and the linearly constrained minimum variance (LCMV). This algorithm is capable of adjusting the weights of the antenna array in real time to respond to and improve the global positioning system (GPS) received signals coming from the desired directions and at the same time to highly suppress the jammers coming from the other directions. The simulation is performed for fixed and moving jammers. It indicates that this structure can give deeper nulls, more than 115 dB depths for fixed jammers and more than 94 dB depths for moving jammers.

Penalty Parameter for Linearly Constrained 0–1 Quadratic Programming

A linearly constrained 0–1 quadratic programming problem is proved to be equivalent to a continuous concave quadratic problem with an easily computed penalty parameter. Moreover, it is proved that the feasibility of the former problem can be checked by solving the latter.

Adaptive linearly constrained inverse QRD-RLS beamforming algorithm for moving jammers suppression

A general, linearly constrained (LC) recursive least squares (RLS) array-beamforming algorithm, based on an inverse QR decomposition, is developed for suppressing moving jammers efficiently. In fact, by using the inverse QR decomposition-recursive least squares (QRD-RLS) algorithm approach, the least-squares (LS) weight vector can be computed without back substitution and is suitable for implementation using a systolic array to achieve fast convergence and good numerical properties. The merits of this new constrained algorithm are verified by evaluating the performance, in terms of the learning curve, to investigate the convergence property and numerical efficiency, and the output signal-to-interference-and-noise ratio. We show that our proposed algorithm outperforms the conventional linearly constrained LMS (LCLMS) algorithm, and the one using the fast linear constrained RLS algorithm and its modified version.

Technical Note—Construction of Difficult Linearly Constrained Concave Minimization Problems

Given a polytope and an arbitrary subset of its vertices, we show how to construct a differentiable concave function that assumes any arbitrary value (within a specified ε-tolerance) at each vertex of the subset, with each vertex in the subset a strong local constrained minimum. We also show how this construction method can be used to generate test problems for linearly constrained concave minimization algorithms.

Algorithm 563: A Program for Linearly Constrained Discrete I 1 Problems

Algorithm 563: A Program for Linearly Constrained Discrete <i>I</i> ₁ Problems