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Related Experiment Video

Updated: May 26, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Intelligent sensor positioning and orientation through constructive neural network-embedded INS/GPS integration

Kai-Wei Chiang¹, Hsiu-Wen Chang

¹Department of Geomatics, National Cheng-Kung University, No.1, Ta-Hsueh Road, Tainan 701, Taiwan. kwchiang@mail.ncku.edu.tw

Sensors (Basel, Switzerland)

|December 14, 2011

Summary

This study introduces Cascade Correlation Neural Networks (CCNNs) for more automated mobile mapping system positioning. The CCNN approach enhances accuracy in Global Positioning System (GPS) and Inertial Navigation System (INS) integration, overcoming limitations of prior methods.

Keywords:

GPS/INS constructive neural networks mobile mapping systems sensor integration

Related Experiment Videos

Last Updated: May 26, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Area of Science:

Geomatics Engineering
Artificial Intelligence
Sensor Fusion

Background:

Mobile mapping systems (MMS) are crucial for spatial data acquisition, with Global Positioning System (GPS) and Inertial Navigation System (INS) integration being common.
Traditional Kalman Filter (KF) methods and high costs limit widespread adoption of MMS.
Previous intelligent schemes using Multi-layer Feed-forward Neural Networks (MFNNs) with KF/smoothers faced automation challenges in Micro Electro Mechanical System (MEMS) INS/GPS integration.

Purpose of the Study:

To address the insufficient automation in conventional MFNN-KF/smoother algorithms for INS/GPS integrated systems.
To develop and analyze alternative intelligent sensor positioning and orientation schemes for more automatic sensor integration.
To overcome limitations of KF/smoother and MFNN-smoother schemes using a novel neural network approach.

Main Methods:

Implementation of Cascade Correlation Neural Networks (CCNNs), a type of constructive Artificial Neural Network (ANN).
Integration of CCNNs into INS/GPS systems for enhanced positioning and orientation.
Comparison of CCNN-based schemes against conventional KF/smoother algorithms and MFNN-smoother schemes using experimental data.

Main Results:

Preliminary results demonstrate the effectiveness of the proposed CCNN schemes.
The CCNN approach shows improved performance compared to standard smoother algorithms.
The CCNN-smoother schemes outperform previously developed MFNN-smoother schemes in automated INS/GPS integration.

Conclusions:

Cascade Correlation Neural Networks offer a more flexible and automated solution for mobile mapping system positioning.
The proposed CCNN-based schemes provide a viable alternative to overcome limitations of existing methods.
This study highlights the potential of CCNNs for advancing intelligent sensor integration in geomatics applications.