An improved boundary-focused adaptive quadtree algorithm for circle-polygon intersection area approximation
For applications like wireless sensor network coverage estimation, this algorithm offers a more efficient and accurate method for a fundamental geometric problem.
The paper presents an improved algorithm for computing the intersection area of multiple circles and a complex polygon, achieving O(1/ε^(3/2)) computational complexity with O(ε) error bound, outperforming five classical methods in relative error on numerical experiments.
In this paper, we present an improved numerical algorithm for computing the intersection area of multiple circles and a complex polygon efficiently. This geometric problem is fundamental to applications such as wireless sensor networks and base station deployment. The key idea is a curvature-multiplicity-guided adaptive sampling strategy that dynamically concentrates sampling points in geometrically complex boundary regions. The algorithm integrates three components: (i) adaptive quadtree partitioning, (ii) analytical integration via Green's theorem for cells intersecting a single circle, and (iii) curvature-multiplicity-guided Monte Carlo subsampling for cells intersecting multiple circles, where a minimum sample count and a constant factor are introduced into the sampling size. Theoretical analysis shows that the algorithm achieves O(1/ε3/2) computational complexity while maintaining an O(ε) error bound, improving upon the O(1/ε2) complexity of classical Monte Carlo and uniform grid methods for the same error tolerance ε. Numerical experiments on complex polygons, including synthetic data and real-world scenarios, demonstrate that our algorithm outperforms five classical methods in terms of relative error. Furthermore, parameter sensitivity analysis confirms that the algorithm is robust and could make it suited for practical applications such as wireless sensor network coverage estimation.