An indoor positioning method based on bluetooth array/PDR fusion using the SVD-EKF

With the development of mobile internet and artificial intelligence, the core of current surveying and mapping science and technology is no longer confined solely to outdoor applications. High-precision indoor positioning technology is now one of the core technologies in the era of artificial intelligence. An angle measurement and positioning system based on wireless signal array antennas can achieve high accuracy in indoor unblocked conditions. However, indoor environment is unpredictable, and the user's behavior is also random. Inevitably, some signal reflection and other factors will affect the positioning accuracy. Considering all the aforementioned, this study analyzes the principles and characteristics of a Bluetooth signal based array antenna angle measurement and positioning system. In addition, aiming at the multi-phase problem caused by antenna switching, the frequency estimation method based on FFT (Fast Fourier Transform) is studied in this paper, achieving high-precision angle measurement and positioning. Aiming at the problem that the system positioning error increase in the complex and variable indoor environment, a fusion positioning method of Bluetooth array/PDR (Pedestrian Dead Reckoning) based on the SVD-EKF (Singular Value Decomposition-Extended Kalman Filter) is proposed. This study introduces a few improvements to the EKF (Extended Kalman Filter). First, the predicted state covariance matrix is decomposed by singular value decomposition, which improves the robustness of the EKF. Second, a Bluetooth array self-evaluation factor is introduced and combined with the Huber function to construct an adaptive factor, thus further enhancing filtering accuracy and environmental adaptability. Through static and dynamic data collected in indoor environment, the feasibility of the algorithm is verified. The results of static and dynamic experimental results show that the array angle measurement and positioning system can achieve high accuracy, the near point positioning maximum error is 0.3m and the far point positioning accuracy is 0.6m. The dynamic test results in the room show that after SVD-EKF algorithm optimization, the positioning error is reduced by 0.05m, which is equivalent to the EKF algorithm. However, in corridor areas, the improvement of accuracy of SVD-EKF algorithm is better than EKF, achieving an improvement of 0.373 m and a smoother positioning result compared to the traditional EKF algorithm. This study provides a new practical technology with a high precision and easy deployment for indoor positioning.