A Zero Velocity Detection Method for Soldier Navigation Based on Deep Learning

Abstract

In this paper, deep learning is introduced into the zero-speed detection of individual navigation. Through the combination of one-dimensional CNN and LSTM, the accuracy of zero-speed detection is further improved, and other cumbersome adjustment procedures based on thresholds and other methods are avoided. At the same time, a method of establishing a zero-speed label using ultrasonic ranging method is also demonstrated. Since the error of running and other sports mainly comes from the device error and not the algorithm itself, in this paper, the 6-axis IMU data is first imported into the one-dimensional CNN and is divided into a part that can perform zero-speed detection and a part that is not suitable for zero-speed detection. It can be used for zero speed detection and reuse LSTM based zero speed detection. Compared with other algorithms, this method uses the deep learning method to train the appropriate model in advance and can be used without adjusting the reference. It is especially suitable for the urgency of the soldier's mission. The experimentally proven SHOE algorithm has a navigation accuracy of less than 3.5m/186.06m without adjustment, and the accuracy of the proposed method is stable above 1.0m/186.06m, which improves the overall navigation accuracy.