Abstract. For indoor positioning, ultra-wideband (UWB) radar comes to the forefront due to its strong penetration, anti-jamming, and high-precision ranging abilities. However, due to the complex indoor environment and disorder of obstacles, the problems of diffraction, penetration, and ranging instability caused by UWB radar signals also emerge, which make it difficult to predict the noise and leads to a great impact on the accuracy and stability of the measurement data in the short term. Therefore, the abnormal value migration of the positioning trajectory occurred in real-time positioning. To eliminate this phenomenon and provide more accurate results, the abnormal values need to be removed. It is not difficult to eliminate abnormal values accurately based on a large number of data, but it is still a difficult problem to ensure the stability of the positioning system by using a small number of measurement data in a short time to eliminate abnormal value in real-time ranging data. Thus, this paper focuses on the experimental analysis of a UWB-based indoor positioning system. By repeatedly measuring the range , a large number of measurement data can be obtained. Using the massive data to train linear regression models, we get the parameter of the linear model of range data measured with the UWB radar. Based on the Gaussian function outlier detection, abnormal values are eliminated, and putting the new range data into the regression model trained by us, the ranging error is reduced by nearly 50 % compared with the peak and mean ranging errors in general.

Abstract. For indoor positioning, ultra-wideband (UWB) radar comes to the forefront due to its strong penetration, anti-jamming, and high precision ranging abilities. However, due to the complex indoor environment and disorder of obstacles, the problems of diffraction, penetration, and ranging instability caused by UWB radar signals also emerge. During the experiment of indoor positioning with UWB radar ranging module P440, it was found that the distance information measured in a short time was unstable, because of the complex indoor environment and unpredictable noise signal. Therefore, the abnormal value migration of the positioning trajectory occurred in real-time positioning. To eliminate this phenomenon and provide more accurate results, the abnormal values need to be removed. It is not difficult to eliminate abnormal value accurately based on a large number of data, but it is still a difficult problem to ensure the stability of the positioning system by using a small amount of measurement data in a short time to eliminate abnormal value in real-time ranging data. Thus, this paper focuses on the experimental analysis of a UWB-based indoor positioning system. To improve the stability of UWB radar ranging data and increase the overall accuracy, this paper studies a large number of UWB radar ranging data by using high-frequency ranging instead of mean value to train estimation model. Based on the Gaussian function outlier detection, abnormal values are eliminated. By using the training distance estimation model and estimating the distance value, the ranging error obtained is nearly 50 % lower than the peak and mean ranging errors in general.

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