Indoor GPS location estimation encounters accuracy challenges from intricate building structures and diverse signal interferences. Trilateration methods utilising APs are typically employed to estimate indoor locations. Nevertheless, estimation errors from multipath effects and high power consumption of sensors employed in location estimation curtail battery life. To address this issue, research into location estimation methods utilising machine learning has been conducted. However, challenges involving the selection of the optimal access point locations and obtaining dense RSSI data have been noted. In this article presents a solution based on sparse radio maps for decreasing the expenses of collecting RSSI data while simultaneously enhancing indoor location accuracy through the integration of image data. The proposed approach integrates matrix-based RSSI indoor positioning (M-RIP) for initial location estimation and feature-based image indoor positioning (F-IIP) for position determination via image feature matching. Furthermore, extended area-based post-processing (EA-PP) is employed to augment M-RIP's precision and minimize image matching computation in F-IIP, improving overall performance. This article utilizes actual building data to validate the precision of the position estimation and efficiency of computation reduction using the proposed method.

中文翻译：

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集成室内定位方法，优化计算并提高预测精度

室内GPS定位面临着复杂建筑结构和多样化信号干扰带来的精度挑战。利用接入点的三边测量方法通常用于估计室内位置。然而，多径效应造成的估计误差和位置估计中使用的传感器的高功耗会缩短电池寿命。为了解决这个问题，已经对利用机器学习的位置估计方法进行了研究。然而，已经注意到涉及选择最佳接入点位置和获取密集 RSSI 数据的挑战。本文提出了一种基于稀疏无线电地图的解决方案，以减少收集 RSSI 数据的费用，同时通过图像数据的集成提高室内定位精度。该方法集成了用于初始位置估计的基于矩阵的 RSSI 室内定位（M-RIP）和用于通过图像特征匹配确定位置的基于特征的图像室内定位（F-IIP）。此外，采用扩展区域后处理 (EA-PP) 来增强 M-RIP 的精度并最大限度地减少 F-IIP 中的图像匹配计算，从而提高整体性能。本文利用实际建筑数据来验证该方法的位置估计精度和计算减少效率。