Abstract
Sparse arrays can fix array aperture with a reduced number of elements to maintain resolution while reducing cost. However, grating lobe suppression, high peak side-lobe level reduction (PSLL), and constraints on the location of the array elements in the practical deployment of arrays are challenging problems. Based on simulated annealing, the element locations of a sparse planar array in smart ocean applications with minimum spacing and geographic constraints are optimized in this paper by minimizing the sum of PSLL. The robustness of the deployment-optimized spare planar array with mis-calibration is further considered. Numerical simulations show the effectiveness of the proposed solution.
Highlights
Sensor and antenna arrays play an important role in fields such as radar and sonar due to their higher processing gain and angular resolution; they are often employed in smart ocean applications for collecting information, positioning and communication
This paper presents the optimization of a sparse planar array in smart ocean applications with minimum spacing and geographic constraints, where the cost function is defined as the sum of peak side-lobe level reduction (PSLL)
The elements of both sparse planar arrays are not located in areas corresponding to geographic constraints, and the minimum spacing between any two adjacent elements is greater than ∆d
Summary
Sensor and antenna arrays play an important role in fields such as radar and sonar due to their higher processing gain and angular resolution; they are often employed in smart ocean applications for collecting information, positioning and communication. This paper presents the optimization of a sparse planar array in smart ocean applications with minimum spacing and geographic constraints, where the cost function is defined as the sum of PSLL for different beam shifting directions, and simulated annealing is assumed for optimization with many parameters and constraints. These two types of constraints in optimization are independently implemented in the direction of the y-axis and x-axis, respectively. With robust adaptive beamforming used for verification, it is shown that an effective DOA estimation can be performed with a deployment-optimized sparse planar array
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