A distance calibration method, device and equipment in a non-line-of-sight scene

Abstract

Embodiments of the present invention provide a distance calibration method, device, and equipment in a non-line-of-sight scene. The method includes: acquiring a first original distance and a first channel impulse response waveform of ultra-wideband radio frequency ranging between a first target point and a second target point; combining the first original distance and the first channel impulse response waveform, Input the trained deep learning model to obtain the distance calibration result and confidence between the first target point and the second target point; wherein, the trained deep learning model is a variational autoencoder and a probabilistic neural network composed deep learning framework. The embodiment of the present invention uses the deep learning model to calibrate the ranging result and gives the confidence of the calibration result, and gives a lower confidence for the distance that is difficult to calibrate correctly, reducing the probability of serious system errors caused by unreliable prediction results.

Description

technical field [0001] Embodiments of the present invention relate to the fields of deep learning and pattern recognition, and in particular to a distance calibration method, device and equipment in a non-line-of-sight scene. Background technique [0002] Due to the complex indoor environment and the influence of non-line-of-sight transmission and multipath transmission, the ranging algorithm based on electromagnetic wave propagation time is not accurate enough. [0003] Currently, there have been some works on indoor odometry correction using traditional machine learning algorithms. However, these traditional machine learning algorithms do not work well. First, they lack sufficient model capacity and generalization ability to provide a sufficiently accurate prediction of the true distance based on environmental information; second, due to the limitations of the model itself, the prediction cannot be given confidence level. In many indoor positioning applications that requ...

AI Technical Summary

Problems solved by technology

However, these traditional machine learning algorithms do not work well. First, they lack sufficient model capacity and generalization ability to provide a sufficiently accurate prediction of the true distance based on environmental information; second, due to the limitations of the model itself, the prediction cannot be given Confidence of

In many indoor positioning applications that require high precision and high robustness, traditional machine learning algorithms cannot meet the precision requirements, and the output results lack sufficient reliability.

The complex indoor environment makes it almost impossible to collect enough data in various possible environments, which leads to the traditional machine learning model having to give predictions in various never-before-seen scenarios. When this new When the statistical distribution of the scene is different from the statistical distribution of the model training data, the model will give very unreasonable prediction results

Images