# Robust Bluetooth AoA Estimation for Indoor Localization Using Particle Filter Fusion
## 1 Introduction
In [22], a deep neural network (DNN) was used to achieve higher DoA estimation accuracy with lower algorithmic complexity. This study demonstrated the generalization and effectiveness of the algorithm in various scenarios. [PERSON] and [PERSON] [23] used a neural network and subarray covariance matrices to estimate the covariance matrix of the entire array. This approach produced additional DoA estimation results for multiple targets. Of the above methods, the traditional subspace methods rely heavily on a priori knowledge, such as the number of targets, and face limitations in the absence of such knowledge. The Music and Capon algorithms lack effective handling of low signal-to-noise ratio signals highly susceptible to high noise components, which affects the accuracy of target DoA estimation. Algorithms such as SVM, SVR, and Bayesian methods achieve target direction estimation by fitting a complex mapping relationship between the incident direction of the signal and the signal features. However, as the signal-to-noise ratio decreases, noise significantly affects the signal, complicating the relationship between the incident angle and the signal. This situation requires many samples for accurate function fitting, which is challenging. As the sample size increases, the information redundancy intensifies, making machine learning algorithms unable to estimate the DoA accurately. These issues also hinder the effectiveness of large-scale models such as NNs and DNNs in DoA estimation. In most cellular localization systems, AoA is usually not used as a primary localization parameter but as a secondary one. Particle filters (PFs), introduced [24] as a numerical approximation to the nonlinear Bayesian filtering problem, can robustly track objects moving with accurate trajectories while automatically maintaining tracking associations. It can approximate the system state's posterior distribution even if the posterior's exact form is unknown. It may require a large number of particles and a large number of computations to achieve acceptable accuracy. Although particle-filtering algorithms have a broad application background in the target tracking problem, the research on tracking and navigation for a pure direction angle is still in the exploratory stage. Most of the moving targets in space are localized by coordinate positions, and there is a nonlinear problem in the transformation of coordinatepositions and direction angles in space. For the nonlinear interference factors, particle filtering provides a good solution. In this paper, a spatial multipath array model is proposed by fusing particle filtering algorithms and AoA measurements, and the mathematical model is built in a single reflection scenario where the locations of the localization station and the reflector are known. The system combines a robust adaptive angle estimation algorithm with a priori knowledge of target motion into a probabilistic framework of PF to localize a moving target. The shortcoming of the traditional AoA algorithm, which only localizes the stationary signal source, has been overcome. Then, a weight calculation function based on the Gaussian function is designed to reasonably calculate the weights of the particles and reduce the particle scarcity. It is verified that our algorithm can realize the tracking navigation of the target, and the algorithm is robust to the nonlinear model of the moving target.