A flexible array synthesis method using quadratic programming

Antenna Array Arbitrary Pattern Synthesis Using Conditional Generative Adversarial Network

“…First we wish to keep the dataset simple and small, and seccond, we will demonstrate in the following sections that the neural network is able to construct patterns with multiple nulls or mainlobes, despite not being explicitly trained on such patterns. Figure (11) illustrates the process of dataset preparation.…”

“…Numerous signal processing techniques have been reported over the past few decades to determine the optimal weights of an array, allowing for steering the main lobe towards a desired direction, steering multiple nulls towards desired directions to mitigate interference signals, and controlling the sidelobe levels. In [7][8][9][10] analytical methods for arrayfactor synthesis have been proposed, conventional optimization algorithms have been used in [11,12], and global optimization techniques such as genetic algorithms and PSO algorithms have been employed in pattern synthesis problems in [13][14][15]. Adaptive algorithms employing the Least Mean Squares (LMS) technique have been documented in a study by [16].…”

Modal Field Synthesis of Monopulse Difference Patterns for Radiating Aperture

“…Now the synthesis problem can be easily identified as a quadratic programming problem, with a closed-form solution [26], [27] for the complex modal amplitudes that produce the optimal difference pattern:…”

Convex Optimization and Array Orientation Diversity-Based Sparse Array Beampattern Synthesis

“…For nonuniformly spaced arbitrary arrays, there are several algorithms [3][4][5][6] that have been proposed to synthesize beampatterns. The design of thinned narrowbeam arrays has been well proposed in [3], which first fix element locations by eliminating the elements pair by pair according to the smallest possible sidelobe on the given interval and then optimize the weights via linear programming.…”

“…The design of thinned narrowbeam arrays has been well proposed in [3], which first fix element locations by eliminating the elements pair by pair according to the smallest possible sidelobe on the given interval and then optimize the weights via linear programming. For APS problem, which can also be formulated as a quadratic programming problem [4,5], the objective function is to minimize the squared errors between the synthesized pattern and the desired pattern. Besides, additional linear constraints [4] or weighting functions [7] are also added to the quadratic objective function to minimize the peaks of the synthesis error.…”