## 1 Introduction
In [14], a comprehensive overview addressing the difficulties and challenges encountered in indoor positioning during the application of WiFi fingerprinting is presented. However, fingerprinting requires the creation of an extensive dataset [15; 16] and challenges still remain due to the instability of collected WiFi RSS values caused by multipath propagation, noise, and non-line-of-sight (NLOS) conditions [17]. In [18], a robotic platform, named a simultaneous localization and mapping algorithm, is introduced, which eliminates the need for predefined maps, reduces human intervention, and achieves accurate localization. The performance of WiFi fingerprinting largely relies on two key factors: AP filtering methods for eliminating noise in RSS measurements and matching algorithms. An effective AP filtering approach can significantly reduce computational overhead while enhancing positioning accuracy, as demonstrated in previous work [19]. In [20], _Stable APs_ were selected and optimized using kernel principal component analysis. To improve robustness in complex indoor environments, [21] proposed an AP filtering method based on the MaxMean criterion, which utilized the strongest RSS to reduce the computational complexity of the localization algorithm. Reference [22] introduced a localization method involving adaptive cluster splitting and AP re-selection. However, these AP selection methods are not easily integrated with matching algorithms, limiting the efficiency and precision of indoor localization. WiFi fingerprint recognition can be formulated as a matching problem, which can be solved using various machine learning (ML) methods, such as \(K\) nearest neighbors (KNN) [23], support vector machine (SVM) [24], backpropagation neural network (BP) [25], and artificial neural network (ANN) [26]. In [24], an SVM-based normalized rank was proposed to reduce signal fluctuations in WiFi fingerprinting. Reference [23] adopted weighted KNN (WKNN) to select fingerprints as possible datasets and further analyzed WKNN in terms of positioning accuracy and complexity. To improve localization efficiency, a BP neural network was employed for the localization procedures in [25]. In [26], an ANN-based localization scheme was proposed to predict new locations and analyze procedural efficiency. Each of these ML-based algorithms presents distinct advantages and disadvantages. An ANN, for instance, offers the benefit of low complexity due to its use of a multilayer perceptron (MLP) to model the relationship between signal strength and position [27]. MLP helps minimize the size of training sets [28] and optimizes computational complexity [29]. While SVM is easier to implement, it has a higher time complexity compared to ANN. The KNN algorithm selects the K positions with the highest similarity, resulting in relatively lower time complexity [30]. However, KNN is prone to errors when the samples are not uniformly distributed [31]. Moreover, a semi-supervised learning framework leveraging unlabeled WiFi fingerprints to improve indoor localization performance is studied in [32], presenting the mean teacher model. A hierarchical neural networks architecture that facilitates the transfer of learned parameters from higher-level tasks to subsequent tasks, promoting a cohesive learning process, is also used for indoor localization using WiFi fingerprints [33]. In this study, we adopted adaptive boosting (AdaBoost) as the matching algorithm for positioning. AdaBoost is a popular ensemble method known for combining multiple weak classifiers to create a strong classifier with high accuracy [34]. The algorithm's adaptive nature is evident in its iterative learning process, where it focuses on previously misclassified samples by adjusting their weights, thereby training subsequent classifiers to better handle these difficult cases [35]. At each step, AdaBoost trains a new weak classifier until either a predefined error rate is reached or a specified number of iterations are completed.