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Tracking Architecture Based on Dual-Filter with State Feedback and Its Application in Ultra-Tight GPS/INS

Xi Zhang¹, Lingjuan Miao², Haijun Shao³

¹School of Automation, Beijing Institute of Technology (BIT), Beijing 100081, China. 3120120355@bit.edu.cn.

Sensors (Basel, Switzerland)

|May 5, 2016

Summary

This study introduces a dual-filter Kalman Filter (KF) for Global Positioning System (GPS) signal preprocessing, improving Doppler shift estimation accuracy. The novel approach enhances ultra-tight GPS/Inertial Navigation System (INS) integration, even in harsh conditions.

Keywords:

baseband signal preprocessing dual-filter navigation filter with expanded dimension state feedback ultra-tight GPS/INS integration

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Area of Science:

Signal Processing
Navigation Systems
Estimation Theory

Background:

Kalman Filters (KF) are crucial for Global Positioning System (GPS) baseband signal preprocessing, enabling low-variance estimates.
Traditional single KF models can suffer from carrier tracking errors linked to code tracking.

Purpose of the Study:

To present a novel dual-filter preprocessing scheme for GPS signals.
To enhance the accuracy of Doppler shift estimation in ultra-tight GPS/Inertial Navigation System (INS) integration.
To ensure robust INS error estimation and calibration under harsh conditions.

Main Methods:

A dual-filter structure is proposed, removing loop filters and utilizing state feedback for local carrier and code generation.
External navigation information is not directly used as local carrier frequency in ultra-tight GPS/INS integration.
An integrated navigation filter is developed to model non-negligible errors in estimated Doppler shifts.

Main Results:

The dual-filter KF scheme improves Doppler shift estimation accuracy, despite local carrier frequency fluctuations.
The method avoids carrier tracking being subjected to code tracking errors.
Field tests and simulations validate the effectiveness of the proposed methods for GPS/INS integration.

Conclusions:

The novel dual-filter KF preprocessing scheme offers improved performance for GPS signal processing and INS integration.
The approach ensures reliable INS calibration by modeling Doppler shift errors, particularly under challenging environmental conditions.
This research contributes to more accurate and robust navigation systems in dynamic and adverse scenarios.