This paper presents a new deterministic method and a polynomial-time algorithm for solving general huge-sized sensor network localization problems. The problem is first formulated as a nonconvex minimization, which was considered as an NP-hard based on conventional theories. However, by the canonical duality theory, this challenging problem can be equivalently converted into a convex dual problem. By introducing a new optimality measure, a powerful canonical primal_dual interior (CPDI) point algorithm is developed which can solve efficiently huge-sized problems with hundreds of thousands of sensors. The new method is compared with the popular methods in the literature. Results show that the CPDI algorithm is not only faster than the benchmarks but also much more accurate on networks affected by noise on the distances.

中文翻译：

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基于规范对偶理论的大型噪声传感器定位问题的高效确定性算法


本文提出了一种新的确定性方法和多项式时间算法来解决一般的大型传感器网络定位问题。该问题首先被表述为非凸最小化问题，根据传统理论，该问题被视为 NP 难问题。然而，通过正则对偶理论，这个具有挑战性的问题可以等价地转化为凸对偶问题。通过引入新的最优性度量，开发了强大的规范原始对偶内部（CPDI）点算法，该算法可以有效解决具有数十万个传感器的大型问题。将新方法与文献中流行的方法进行了比较。结果表明，CPDI 算法不仅比基准更快，而且在受距离噪声影响的网络上也更加准确。