Abstract
To solve the problem of low positioning accuracy and ease environmental impact of wearable devices in the Internet of things, a wearable device indoor positioning algorithm based on deep learning was proposed. Firstly, a basic model of deep learning composed of an input layer, hidden layer, and output layer is proposed to realize the continuous prediction and positioning with higher accuracy. Secondly, the automatic stacking encoder is trained with signal strength data, and features are extracted from a large number of signal strength samples with noise to build the location fingerprint database. Finally, the stacking automatic coding machine is used to obtain the signal strength characteristics of the points to be measured, which are matched with the signal strength characteristics in the fingerprint database, and the location of the points to be measured is estimated by the nearest neighbor algorithm. The experimental results show that the indoor positioning algorithm based on the stacking automatic coding machine has higher positioning accuracy, and the average error of points on the complete path can reach within 3 m in 93% cases.
Highlights
With the development of computer technology, smart phones and other smart wearable devices with a variety of sensors have been popularized in everyday life, and people’s demand for indoor positioning services is increasingly strong
Due to the indoor complex environment, the accuracy is affected by obstacles, non-line-of-sight propagation, noise, and other factors, and there is a certain positioning error [8, 9]. erefore, it is of major practical significance and practical value to study indoor positioning algorithm of smart wearable devices
Experimental results show that the proposed indoor positioning algorithm of wearable devices based on subconscious learning has higher positioning accuracy, and the average error of points on the complete path can reach within 3 m in 93% of cases
Summary
With the development of computer technology, smart phones and other smart wearable devices with a variety of sensors have been popularized in everyday life, and people’s demand for indoor positioning services is increasingly strong. Traditional machine learning methods are very sensitive to data and often require data normalization, filtering, and other preprocessing They are easy to overfit and lead to Mathematical Problems in Engineering bland generalization ability, which will affect the prediction performance of the network over time. (2) Most of the existing methods, easy from the problem of fitting result in weak generalization ability, and the algorithm using the signal strength data of stacked automatic coding machine for training, and from a lot of signal with noise characteristics, strength of samples is taken to establish the position fingerprint database, which effectively solves the fitting result in weak generalization ability problem. Experimental results show that the proposed indoor positioning algorithm of wearable devices based on subconscious learning has higher positioning accuracy, and the average error of points on the complete path can reach within 3 m in 93% of cases
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