Next Article in Journal
A Secure Occupational Therapy Framework for Monitoring Cancer Patients’ Quality of Life
Previous Article in Journal
Soft Sensors in the Primary Aluminum Production Process Based on Neural Networks Using Clustering Methods
Open AccessArticle

PSO-LSSVR Assisted GPS/INS Positioning in Occlusion Region

Chuzhou College, School of Geographic Information and Tourism, ChuZhou 239000, China
AnHui Province Geographic Information Intelligent Perception and Service Engineering Laboratory, Chuzhou 239000, China
Jiangsu Normal University, School of Geographic Surveying and Mapping and Urban and Rural Planning, XuZhou 221000, China
Author to whom correspondence should be addressed.
Sensors 2019, 19(23), 5256;
Received: 18 October 2019 / Revised: 15 November 2019 / Accepted: 24 November 2019 / Published: 29 November 2019
(This article belongs to the Section Intelligent Sensors)
Satellite signals are easily lost in complex observation environments and high dynamic motion states, and the position and posture errors of pure inertial navigation quickly diverges with time. This paper therefore proposes a scheme of occlusion region navigation based on least squares support vector regression (LSSVR), and particle swarm optimization (PSO), used to seek the global optimal parameters. Firstly, the scheme uses the incremental output of GPS (Global Positioning System) and Inertial Navigation System (INS) when the observation is normal as the training output and the training input sample, and then uses PSO to optimize the regression parameters of LSSVR. When the satellite signal is unavailable, the trained mapping model is used to predict the GPS pseudo position. Secondly, the observed anomaly is detected by the test statistic in the integrated navigation solution filtering estimation, and the exponential fading adaptive factor is introduced to suppress the influence of the abnormal pseudo observation value. The results indicate that the algorithm can predict the higher precision GPS position increment, and can effectively judge some abnormal observations that may occur in the predicted value, and adjust the observed noise covariance to suppress the anomaly observation, which can effectively improve the continuity and reliability of the integrated navigation system in the occlusion region. View Full-Text
Keywords: integrated navigation; least squares support vector regression; particle swarm optimization; fading adaptive filtering integrated navigation; least squares support vector regression; particle swarm optimization; fading adaptive filtering
Show Figures

Figure 1

MDPI and ACS Style

Xiaoming, L.; Xinglong, T.; Changsheng, Z. PSO-LSSVR Assisted GPS/INS Positioning in Occlusion Region. Sensors 2019, 19, 5256.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop