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In this paper, the main research object is evolution algorithm applied for an ultra-wideband three-dimensional (3D) circular antenna array and the significance of elevation angles and their influence on the system performance are highlighted. The 3D channel is calculated by means of 3D shooting and bouncing ray/image techniques which specify multipath elevation angles and azimuth for horizontal plane angles. The capacities of the two-dimensional (2D) and three-dimensional transmitting antenna arrays have been presented. Beam-synthesizing techniques are applied at the transmitter to focus the transmitter energy for the sake of mitigating the multi-path effect and improving channel capacity. A combination of asynchronous particle swarm optimization (APSO) and dynamic differential evolution (DDE) methods are introduced to make adjustment to the length of the feed line on each array element for the maximum capacity. In comparison with 2D azimuth-only antenna arrays, a higher directive gain can be achieved from the 3D antenna arrays results in high channel capacity. Besides, it is discovered that the capacity for the 3D array improves by as much as over 10% when compared to that for the 2D array. As revealed by numerical results, APSO is capable of obtaining better beam-forming pattern to improve the channel capacity than DDE by about 9%. Not only does our research provide qualitative outcomes, it also presents quantitative results. [ABSTRACT FROM AUTHOR]

This paper considers a passive target localization problem based on the noisy time of arrival measurements obtained from multiple receivers and a single transmitter. The maximum likelihood (ML) estimator for this localization problem is formulated as a highly nonlinear and non-convex optimization problem, where conventional optimization methods are not suitable for solving such a problem. Consequently, this paper proposes an improved adaptive hybrid firefly differential evolution (AHFADE) algorithm, based on hybridization of firefly algorithm (FA) and differential evolution (DE) algorithm to estimate the unknown position of the target. The proposed AHFADE algorithm dynamically adjusts the control parameters, thus maintaining high population diversity during the evolution process. This paper aims to improve the accuracy of the global optimal solution by incorporating evolutionary operators of the DE in different searching stages of the FA. In this regard, an adaptive parameter is employed to select an appropriate mutation operator for achieving a proper balance between global exploration and local exploitation. In order to efficiently solve the ML estimation problem, this paper proposes the well-known semidefinite programming (SDP) method to convert the non-convex problem into a convex one. The simulation results obtained from the proposed AHFADE algorithm and well-known algorithms, such as SDP, DE and FA, are compared against Cramér–Rao lower bound (CRLB). The statistical analysis has been performed to compare the performance of the proposed AHFADE algorithm with several well-known algorithms on CEC2014 benchmark problems. The obtained simulation results show that the proposed AHFADE algorithm is more robust in high-noise environments compared to existing algorithms. [ABSTRACT FROM AUTHOR]