# Robust Bluetooth AoA Estimation for Indoor Localization Using Particle Filter Fusion
## 1 Introduction  
Indoor positioning is a critical component of a positioning system [1] that provides information about the position of a moving target at any given moment. Global navigation satellite systems (GNSSs), including the Global Positioning System (GPS), GLONASS, and Galileo, perform well in outdoor environments but poorly in indoor environments because the GNSS signals cannot enter buildings [2; 3]. Due to the limitations of GNSSs, the indoor positioning problem has attracted much attention from the industry and academia. Three-dimensional indoor localization is in urgent demand, and in order to solve the indoor localization problem, several localization systems have been proposed and implemented using different techniques and approaches, including the utilization of multi-sensor-based localization-related techniques [4; 5]. Although many existing systems try to solve this problem using a wide variety of techniques, indoor positioning systems based on wireless sensor networks (WSNs) are one of the most adopted solutions, including some specific IEEE standard radio signals such as Wi-Fi, ZigBee, Bluetooth, UWB, and RFID [6; 7; 8; 9], which have been widely used in many real-world applications such as medical monitoring, target tracking, navigation, emergency call services, and transport. The vast majority of solutions for such systems are solved by standard-ranging techniques such as Received Signal Strength (RSS) [10], Time of Arrival (TOA), Time Difference of Arrival (TDOA), and AoA [11]. In the application of fifth-generation (5G) technology, Bluetooth Low Energy (BLE) is a key wireless technology for IoT systems. It is widely used in consumer electronicdevices due to its advantages in terms of cost, efficiency, and availability. The Bluetooth Core Specification v5.1 introduces new capabilities that support high-accuracy direction-finding. The Angle of Departure (AoD) represents the direction from which the signal is sent from the transmitting device, while the Angle of Arrival (AoA) represents the direction from which the signal reaches the receiving device. The article mentions that direction of arrival (DoA) positioning technology is based on the 2D plane to obtain the angle \(\theta\), while AoA mostly represents three-dimensional space angle estimation \((\theta,\varphi)\). The Constant Tone Extension (CTE) [12] has been added to BLE packets. With the help of the Switched Antenna Array (SAA), BLE devices can estimate the direction of the incident wave by utilizing the phase difference information of the signals received by different sensors. This effectively enables Angle of Arrival (AoA) estimation, eliminating the reliance on signal time synchronization without excessive hardware cost. Multi-signal classification (MUSIC) algorithms based on subspace methods, as the most referenced approach [13], can provide highly accurate estimation under high signal-to-noise ratio (SNR) conditions. However, it still faces challenges associated with high computational complexity and sensitivity to noise due to multipath and non-line-of-sight (NLOS) propagation. To overcome this challenge, various improved MUSIC algorithms [14; 15; 16] and advanced subspace methods have been explored. For example, the root-MUSIC algorithm estimates the direction of arrival (DoA) by finding polynomial roots and exhibits superior complexity and resolution compared to MUSIC [17]. The literature [18] proposes a sectoral You music method to reduce the algorithm complexity and efficiently realize fast DoA estimation. A previous study [19] used the Support Vector Machine (SVM) algorithm to improve DoA estimation accuracy with a small sample size. [PERSON] et al. [20] introduced a novel sparse Bayesian learning method that improves the estimation accuracy with a low signal-to-noise ratio and a limited number of snapshots. Another study [21] used support vector regression (SVR) for adaptive target detection to obtain a system model related to target azimuth and radar data features.