Evaluating the Impact of Convolutional Neural Network Layer Depth on the Enhancement of Inertial Navigation System Solutions

TL;DR

This paper investigates how the depth of convolutional neural network layers affects the accuracy of error correction in inertial navigation systems, aiming to optimize neural network design for improved navigation accuracy.

Contribution

It introduces a supervised ConvNet approach to enhance INS accuracy and evaluates the impact of layer depth on correction effectiveness.

Findings

Deeper ConvNet layers improve error correction up to an optimal point.

Optimal layer depth significantly enhances navigation accuracy.

ConvNet-based correction reduces bias and noise effects in INS.

Abstract

Secure navigation is pivotal for several applications including autonomous vehicles, robotics, and aviation. The inertial navigation system estimates position, velocity, and attitude through dead reckoning especially when external references like GPS are unavailable. However, the three accelerometers and three gyroscopes that compose the system are exposed to various types of errors including bias errors, scale factor errors, and noise, which can significantly degrade the accuracy of navigation constituting also a key vulnerability of this system. This work aims to adopt a supervised convolutional neural network (ConvNet) to address this vulnerability inherent in inertial navigation systems. In addition to this, this paper evaluates the impact of the ConvNet layer's depth on the accuracy of these corrections. This evaluation aims to determine the optimal layer configuration maximizing the effectiveness of error correction in INS (Inertial Navigation System) leading to precise navigation solutions.