A method for estimating the step size of underground personnel based on generative adversarial networks

Abstract

In response to the problems of cumulative errors in step size estimation and the large sample size required by traditional deep learning methods in the pedestrian dead reckoning (PDR) based underground personnel positioning system in coal mines, a step size estimation method for underground personnel based on generative adversarial network (GAN) is proposed. The GAN model mainly includes two parts: generative model and discriminative model, both of which are implemented using deep neural networks (DNNs). The generative model aims to generate continuous result distributions (i.e. labels) based on input data. Its output layer uses a linear activation function to preserve the linear features of the network, allowing the model to predict the step size of any personnel during walking. The discriminant model aims to distinguish whether the input data and labels are real labels or labels generated by the generator. Its output layer uses a Sigmoid activation function to achieve binary classification of results. After determining the generative model and discriminant model, the GAN model combines two models for training. By constructing and optimizing the dynamic competition between the generator and discriminator, the generator can learn to generate more realistic and indistinguishable data samples in continuous iterations. The experimental results show that under the same training and testing sets, the average error of the GAN model is 0.14 m, and the standard deviation and root mean square error are both smaller than those of the DNNs model, with the minimum values being 0.74 m. The outdoor test results show that the GAN based underground personnel step estimation method has a minimum error of 3.21% and a maximum error of 4.79% in uphill and downhill scenarios. Compared to uphill and downhill scenarios, the error in playground scenarios is smaller, with a maximum error of 1.91%.

Keywords

• underground personnel positioning
• pedestrian dead reckoning
• pdr
• generative adversarial networks
• step size estimation
• generative model
• discriminant model
• inertial measurement unit
• imu