Antenna Array Layout Optimization for Side-Lobe Suppression
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Solution Overview
Problem
Antenna array configurations in confined spaces often result in distorted beams with diminished main-lobe power and elevated side-lobe power, leading to reduced accuracy and manufacturing inefficiencies.
Innovation Solution
An optimization system using randomization and gradient operations to compute positions for antenna elements, minimizing distance and suppressing side-lobe power by dynamically adjusting the placement area and optimizing element positions based on side-lobe power constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If antenna elements are densely packed to maximize space utilization, then area efficiency is improved, but side-lobe power increases and beam distortion occurs
Solution Approach 1:
The patent applies dynamic optimization by iteratively adjusting element positions using gradient-based algorithms. The system starts with random initial positions and continuously refines them through gradient descent operations, allowing the configuration to adapt and evolve from a static random layout to an optimized dynamic configuration that minimizes side-lobe power while maintaining space efficiency.
Solution Approach 2:
The system changes positional parameters of antenna elements through gradient operations. By modifying the coordinates (x, y positions) of each element based on calculated gradients of the objective function, the system transforms the parameter set to achieve lower side-lobe power while maintaining compact area utilization.
2Productivity
If random initial positions are used to explore configuration space, then manufacturing yield is improved, but computational time increases
Solution Approach 1:
The system performs preliminary random sampling to generate initial element positions before applying gradient optimization. This preliminary action explores the configuration space to identify promising regions, and then the gradient-based refinement efficiently converges to optimal solutions, reducing the need for extensive random sampling and thus decreasing total computational time.
Solution Approach 2:
The gradient optimization provides continuous refinement of element positions after initial random placement. This continuous action efficiently narrows down the search space by following the gradient direction, maintaining useful computational work throughout the process rather than relying solely on discrete random sampling, thereby improving productivity while managing computational time.
3Object-generated harmful factors
If gradient operations are applied to fine-tune element positions, then side-lobe suppression is improved, but convergence speed decreases for non-continuous actions
Solution Approach 1:
The system dynamically switches between random exploration and gradient refinement phases. During early iterations, random adjustments allow rapid exploration of the configuration space. As the optimization progresses, gradient operations take over for precise fine-tuning, adapting the optimization speed to the current stage and improving overall convergence while maintaining effective side-lobe suppression.
Solution Approach 2:
The optimization employs periodic alternating actions between random position adjustments and gradient-based refinements. This periodic application of different optimization strategies allows the system to escape local minima through random perturbations while achieving precise convergence through gradient operations, balancing convergence speed and side-lobe suppression effectiveness.
Data Source
AI summary
System, methods, and other embodiments described herein relate to computing positions for manufacturing elements of an antenna array using randomization and gradient operations that suppress side-lobe power. In one embodiment, a method includes computing positions for elements on an antenna array within a placement area using randomization that accounts for varying quantities of the elements according to a distance constraint and a side-lobe power. The method also includes adjusting the placement area according to a location associated with one of the elements. The method also includes optimizing, in response to the elements satisfying criteria after predetermined iterations, the positions for a physical layout of the antenna array using a gradient operation according to the side-lobe power.