GPS/INS enhancement for land navigation using neural network

Kaygisiz, BH
We propose in this paper a method to enhance the performance of a coupled global positioning/inertial navigation system (GPS/INS) for land navigation applications during GPS signal loss. Our method is based on the use of an artificial neural network (ANN) to intelligently aid the GPS/INS Coupled navigation system in the absence of GPS signals. The proposed enhanced GPS/INS is tested in the dynamic environment of a land vehicle navigating around a closed path on the METU campus and we provide the results. Our GPS/INS + ANN system performance is thus demonstrated with a land trial.


Analysis of vision aided inertial navigation systems
Yuksel, Yigiter; Kaygisiz, H. Burak (2006-04-19)
We propose in this paper a method to integrate inertial navigation systems with electro optic imaging devices. Our method is based on updating the inertial navigation system in a Kalman filter structure using line of sight measurements obtained from a camera. The proposed method is analyzed based on a UAV scenario generated by our trajectory simulator and the results are provided here. The results show that even a single vision aid can improve the performance of inertial navigation system.
Transient signal detection in continuous GPS coordinate time series using empirical mode decomposition and principal component analysis
Özdemir, Soner; Karslıoğlu, Mahmut Onur.; Department of Geodetic and Geographical Information Technologies (2019)
Continuous Global Positioning System (GPS) coordinate time series might be exposed to tectonic and non-tectonic transient signals as well as the persistent signals such as secular rates and seasonal motions. Transient signal detection becomes challenging when the targeted signal is weak and buried in the noise. Incoherency of the transient signal in space and large number of sites in the GPS network make the detection even more complicated. We propose a new approach based on Empirical Mode Decomposition (EM...
Vector tracking loop design for GPS receivers
Üzel, Deniz; Baykal, Buyurman; Department of Electrical and Electronics Engineering (2016)
This study describes the design of a modern GPS receiver architecture based on vector tracking loops. Since the traditional tracking loops process the signals independently, there is no information exchange between channels. Due to that fact, aiding of weaker signals in the presence of relatively strong signals is impossible. On the other hand, vector tracking loops simultaneously process the signals from all visible channels. Therefore, they are able to perform better than the traditional tracking loops in...
Measurement of differential top-quark-pair production cross sections in pp collisions at root s=7 TeV
Chatrchyan, S.; et. al. (Springer Science and Business Media LLC, 2013-03-01)
Normalised differential top-quark-pair production cross sections are measured in pp collisions at a centre-of-mass energy of 7 TeV at the LHC with the CMS detector using data recorded in 2011 corresponding to an integrated luminosity of 5.0 fb(-1). The measurements are performed in the lepton + jets decay channels (e + jets and mu + jets) and the dilepton decay channels (e(+)e(-), mu(+)mu(-), and mu(+/-)e(-/+)). The t (t) over bar differential cross section is measured as a function of kinematic properties ...
Feedback motion planning of unmanned surface vehicles via random sequential composition
Ege, Emre; Ankaralı, Mustafa Mert (SAGE Publications, 2019-08-01)
In this paper, we propose a new motion planning method that aims to robustly and computationally efficiently solve path planning and navigation problems for unmanned surface vehicles (USVs). Our approach is based on synthesizing two different existing methodologies: sequential composition of dynamic behaviours and rapidly exploring random trees (RRT). The main motivation of this integrated solution is to develop a robust feedback-based and yet computationally feasible motion planning algorithm for USVs. In ...
Citation Formats
B. Kaygisiz, “GPS/INS enhancement for land navigation using neural network,” JOURNAL OF NAVIGATION, pp. 297–310, 2004, Accessed: 00, 2020. [Online]. Available: