We present a novel relaxed maximum likelihood solution to the problem of direct localization of multiple narrowband sources by partly calibrated arrays, i.e., arrays composed of fully calibrated subarrays yet lacking inter-array calibration. The proposed solution is based on eliminating analytically all the nuisance parameters in the problem, thus reducing the likelihood function to a maximization problem involving only the location of the sources. The performance of the solution is demonstrated via simulations.

%B 27th European Signal Processing Conference, EUSIPCO 2019 %C A Coruna, Spain %8 07/2019 %G eng %U http://eusipco2019.org/technical-program %0 Generic %D 2018 %T Constant Modulus Algorithms via Low-Rank Approximation %A Amir Adler %A Mati Wax %K Constant modulus %K convex optimization %K trace norm %XWe present a novel convex-optimization-based approach to the solutions of a family of problems involving constant modulus signals. The family of problems includes the constant modulus and the constrained constant modulus, as well as the modified constant modulus and the constrained modified constant modulus. The usefulness of the proposed solutions is demonstrated for the tasks of blind beamforming and blind multiuser detection. The performance of these solutions, as we demonstrate by simulated data, is superior to existing methods.

%8 04/2018 %2http://hdl.handle.net/1721.1/114672

%0 Conference Paper %B 2018 IEEE Statistical Signal Processing Workshop (SSP) %D 2018 %T Constant Modulus Beamforming Via Low-Rank Approximation %A Amir Adler %A Mati Wax %XWe present novel convex-optimization-based solutions to the problem of blind beamforming of constant modulus signals, and to the related problem of linearly constrained blind beamforming of constant modulus signals. These solutions are based on a low-rank approximation, ensure global optimality and are parameter free, namely, do not contain any tuneable parameters and do not require any a-priori parameter settings. The proposed approach outperforms state-of-the-art both in terms of the number of required samples for convergence, and in terms of the beamformer output SINR.

%B 2018 IEEE Statistical Signal Processing Workshop (SSP) %C Freiburg im Breisgau, Germany %@ 978-1-5386-1571-3 %G eng %U https://ieeexplore.ieee.org/document/8450799/ %R 10.1109/SSP.2018.8450799