INS-Assisted GNSS Signal Tracking Modeling and Assessment

2011 ◽  
Vol 271-273 ◽  
pp. 603-608
Author(s):  
Ping Ye ◽  
Xing Qun Zhan ◽  
Gang Du
Keyword(s):  
Inertial Sensor ◽  
Inertial Navigation ◽  
Phase Lock Loop ◽  
Signal Tracking ◽  
Gnss Receiver ◽  
Tracking Condition ◽  
Tracking Ability ◽  
Tracking Loops ◽  
Tracking Behavior ◽  
Gyro Drift

To improve the tracking performance of GNSS receiver in signal-attenuated environments, phase lock loop (PLL) and delay lock loop (DLL) assisted with Inertial Navigation System (INS) measurements are considered. Combining inertial navigation principles with signal processing, this paper proposes a simplified but efficient mathematical model of INS-assisted second-order tracking loops. Compared with unaided GNSS receiver, the tracking behavior of INS-assisted receiver is quantitatively analyzed, and the kind of INS suitable to guarantee the tracking condition is determined. The results indicate that an INS with 1 deg/h gyro drift is necessary to support PLL, and MEMS inertial sensor with 100 deg/h gyro drift is sufficient to aid DLL to keep favored tracking ability.

A filter algorithm for receiver tracking loops assisted by inertial information

2021 ◽  
pp. 1-11
Author(s):  
Zhifeng Han ◽  
Zheng Fang
Keyword(s):  
Integration Time ◽  
Frequency Error ◽  
Loop Filter ◽  
Signal Tracking ◽  
Phase Jitter ◽  
Loop Bandwidth ◽  
Tracking Ability ◽  
Negative Effect ◽  
Tracking Loops ◽  
Signal Environment

Abstract In traditional satellite navigation receivers, the parameters of tracking loop such as loop bandwidth and integration time are usually set in the design of the receivers according to different scenarios. The signal tracking performance is limited in traditional receivers. In addition, when the tracking ability of weak signals is improved by extending the integration time, negative effect of residual frequency error becomes more and more serious with extension of the integration time. To solve these problems, this paper presents out research on receiver tracking algorithms and proposes an optimised tracking algorithm with inertial information. The receiver loop filter is designed based on Kalman filter, reducing the phase jitter caused by thermal noise in the weak signal environment and improving the signal tracking sensitivity. To confirm the feasibility of the proposed algorithm, simulation tests are conducted.

An Improved and Efficient Algorithm for SINS/GPS/Doppler Integrated Navigation Systems

2012 ◽  
Vol 245 ◽  
pp. 323-329 ◽  
Author(s):  
Muhammad Ushaq ◽  
Jian Cheng Fang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Efficient Manner ◽  
Position Errors

Inertial navigation systems exhibit position errors that tend to grow with time in an unbounded mode. This degradation is due, in part, to errors in the initialization of the inertial measurement unit and inertial sensor imperfections such as accelerometer biases and gyroscope drifts. Mitigation to this growth and bounding the errors is to update the inertial navigation system periodically with external position (and/or velocity, attitude) fixes. The synergistic effect is obtained through external measurements updating the inertial navigation system using Kalman filter algorithm. It is a natural requirement that the inertial data and data from the external aids be combined in an optimal and efficient manner. In this paper an efficient method for integration of Strapdown Inertia Navigation System (SINS), Global Positioning System (GPS) and Doppler radar is presented using a centralized linear Kalman filter by treating vector measurements with uncorrelated errors as scalars. Two main advantages have been obtained with this improved scheme. First is the reduced computation time as the number of arithmetic computation required for processing a vector as successive scalar measurements is significantly less than the corresponding number of operations for vector measurement processing. Second advantage is the improved numerical accuracy as avoiding matrix inversion in the implementation of covariance equations improves the robustness of the covariance computations against round off errors.

Tracking in Two Dimensions as a Function of Dimension Priorities and Tracking Difficulty

1977 ◽  
Vol 21 (2) ◽  
pp. 118-122 ◽  
Author(s):  
Daniel Gopher ◽  
David Navon ◽  
Nela Chillag
Keyword(s):  
Two Dimensions ◽  
Regression Equations ◽  
Time Sharing ◽  
Evaluation Of Performance ◽  
Adaptive Procedures ◽  
Pursuit Tracking ◽  
Tracking Ability ◽  
And Performance ◽  
Task Conditions ◽  
Tracking Behavior

The present paper develops the argument that an effective evaluation of performance under time-sharing conditions requires a joint manipulation of tasks difficulty and operator's resources allocation. An experiment is presented in which each of the dimensions in a two dimensional pursuit tracking task was manipulated and controlled seperately. Single and dual task conditions were created by presenting one dimension or two dimensions simultaneously. Time-sharing efficiency was assessed under a joint manipulation of tracking difficulty on each dimension and their relative priorities. Subjects' tracking ability was individually calibrated by adaptive procedures. Regression equations and performance functions were obtained to describe the joint effects of the experimental variables. Results are discussed in terms of their implications to the problem of measuring capacity, and their contribution to the understanding of tracking behavior.

Estimating Gyro Drift of Strapdown Inertial Navigation System Based on Star Sensor

2012 ◽  
Vol 566 ◽  
pp. 235-238
Author(s):  
Guang Tao Zhou ◽  
Gui Min Shi ◽  
Lei Zhang ◽  
Kai Li
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Model Simulation ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
Star Sensor ◽  
Integrated Navigation ◽  
Working Principle ◽  
Gyro Drift ◽  
Strapdown Inertial Navigation

In the strapdown inertial navigation system (SINS), gyro drift will result in navigation errors. A new algorithm based on star sensor is proposed in this paper to estimate gyro drift. The paper analyzed the working principle of star sensor and the technique of estimating gyro drift. Gyro drift can be estimated through the high-precision attitude information provided by a star sensor. Kalman filter is used in the integrated navigation model. Simulation results show that the proposed algorithm can estimate gyro drift accurately and improve the precision of SINS.

Sign in / Sign up

Export Citation Format

Share Document