A Novel KGP Algorithm for Improving INS/GPS Integrated Navigation Positioning Accuracy.

Huibing Zhang,Dingke Liu,Ya Zhou,Jingwei Zhang,Lihua Yin,Fang Pan,Tong Li

doi:10.3390/s19071623

Abstract

The fusion of multi-source sensor data is an effective method for improving the accuracy of vehicle navigation. The generalization abilities of neural-network-based inertial devices and GPS integrated navigation systems weaken as the nonlinearity in the system increases, resulting in decreased positioning accuracy. Therefore, a KF-GDBT-PSO (Kalman Filter-Gradient Boosting Decision Tree-Particle Swarm Optimization, KGP) data fusion method was proposed in this work. This method establishes an Inertial Navigation System (INS) error compensation model by integrating Kalman Filter (KF) and Gradient Boosting Decision Tree (GBDT). To improve the prediction accuracy of the GBDT, we optimized the learning algorithm and the fitness parameter using Particle Swarm Optimization (PSO). When the GPS signal was stable, the KGP method was used to solve the nonlinearity issue between the vehicle feature and positioning data. When the GPS signal was unstable, the training model was used to correct the positioning error for the INS, thereby improving the positioning accuracy and continuity. The experimental results show that our method increased the positioning accuracy by 28.20–59.89% compared with the multi-layer perceptual neural network and random forest regression.

Highlights

With the development of sensor technology, context-aware vehicles are becoming increasingly popular
The experimental results show that our method increased the positioning accuracy by 28.20–59.89% compared with the multi-layer perceptual neural network and random forest regression
By observing the data characteristics of the vehicle, we found that the gyroscope and accelerometer accelerometer contained in the Inertial Measurement Unit (IMU) are in the process of measuring the real road, and the data contained in the IMU are in the process of measuring the real road, and the data outliers will inevitably outliers will inevitably be collected due to the interference of the urban environment

Summary

Introduction

With the development of sensor technology, context-aware vehicles (e.g., location services and automatic driving) are becoming increasingly popular. These applications require high context perception accuracy, especially in assisted and automatic driving, which has increasingly high requirements for the continuity, reliability, and accuracy of vehicle positioning. For the data fusion problem in integrated navigation, Kalman Filter (KF) is the existing optimal trajectory estimation method, which solves the problem of tedious calculation caused by the weak nonlinear ability. In integrated navigation, when the GPS signal is interrupted [5,6], the positioning error in the Inertial Measurement Unit (IMU) accumulates over time, decreasing the overall performance of the Sensors 2019, 19, 1623; doi:10.3390/s19071623 www.mdpi.com/journal/sensors

Methods

Results

Conclusion