The access point (AP) deployment is a fundamental task for constructing an accurate localization system. Existing literature mainly deals with the AP placement problem using optimal geometry analysis since the target-AP geometry will affect the localization performance. However, some non-ideal phenomena in practical scenario, e.g., the existence of obstacles, array orientation and path loss, will degrade the accuracy of angle-of-arrival (AoA) estimation as well as the localization accuracy. In this article, we reformulate the AP planning incorporating these factors. We decompose the problem into two subproblems, namely AP selection problem and error minimization problem. The AP selection problem selects the minimum number of APs to satisfy a desired localization accuracy, aided by a refined orientation updating procedure. We design a centralized and a distributed error minimization algorithm to further decrease the localization error. The centralized algorithm shows superiority in time efficiency. Nevertheless, the case with large number of APs may lead to excessive computational cost. Accordingly, we further devise the distributed algorithm which is adaptive to large-scale deployment. Numerical studies in indoor environments with barriers are conducted to verify our proposed approach.

中文翻译：

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基于到达角的本地化的实用接入点部署

接入点（AP）部署是构建准确的本地化系统的基本任务。由于目标AP的几何形状会影响定位性能，因此现有文献主要使用最佳几何分析来解决AP放置问题。但是，实际情况中的一些非理想现象（例如，障碍物的存在，阵列方向和路径损耗）将降低到达角（AoA）估计的精度以及定位精度。在本文中，我们将结合这些因素重新制定AP规划。我们将该问题分解为两个子问题，即AP选择问题和错误最小化问题。AP选择问题借助精确的方位更新过程来选择最少数量的AP，以满足所需的定位精度。我们设计了集中式和分布式误差最小化算法，以进一步减少定位误差。集中式算法在时间效率上显示出优越性。然而，具有大量AP的情况可能会导致过多的计算成本。因此，我们进一步设计了适用于大规模部署的分布式算法。在有障碍的室内环境中进行了数值研究，以验证我们提出的方法。