ArticleScientific reports2025
An indoor positioning method based on bluetooth array/PDR fusion using the SVD-EKF.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.