What about Carrier-to-Noise Density and AI for INS/GPS Integration?
“GNSS Solutions” is a regular column featuring questions and answers about technical aspects of GNSS. Readers are invited to send their questions to the columnist, Dr. Mark Petovello, Department of Geomatics Engineering, University of Calgary, who will find experts to answer them. firstname.lastname@example.org
Q: What is C/N0 and how is it calculated in a GNSS receiver?
A: C/N0 (carrier-to-noise density) is the ratio of received carrier (i.e., signal) power to noise density. Higher C/N0 results in reduced data bit error rate (when extracting the navigation data from the GNSS signals) and reduced carrier and code tracking loop jitter. Reduced carrier and code tracking loop jitter, in turn, results in less noisy range measurements and thus more precise positioning.
Note that C/N0 is not the same as SNR (signal-to-noise ratio), although the terms are sometimes used interchangeably. Effectively, C/N0 assumes that the noise has infinite bandwidth (and thus power) and therefore characterizes it using a density, that is, as the amount of noise power per unit of bandwidth (i.e., watts/Hz).
Conversely, SNR considers the total noise power in a certain bandwidth (i.e., watts). C/N0 can be derived from SNR if the noise bandwidth of the SNR measurement is known. For example, one manufacturer’s GPS receiver displays SNR as a figure of merit for a GNSS signal; however, in this receiver, C/N0 is typically 30 decibels higher than the displayed SNR.
C/N0 provides a metric that is more useful for comparing one GNSS receiver to another than SNR because the bandwidth of the receivers is eliminated in the comparison. How the effective noise bandwidth (NBW) of a GNSS receiver is chosen is beyond the scope of this article but can be computed/defined based on a receiver’s hardware implementation, as will be briefly discussed later.
. . .
Estimating CN0 in a GNSS Receiver
. . .
Noise Power Estimation
. . .
. . .
This conversion from SNR to C/N0 is specific to the given definition of SNR. However, regardless of how a manufacturer defines SNR, an analysis similar to that given in this article can be applied to determine C/N0.
(For Brad Badke’s complete answer to this question, including formulas and tables, please download the full article using the pdf link above.)
Q: What are the merits and limitations of artificial intelligence methods for INS/GPS integration?
A: Most current modules that integrate inertial navigation and GPS (INS/GPS) technologies typically rely on Kalman filtering (KF) to exploit their individual benefits and provide a reliable navigation solution. However, KF-based integration techniques for INS/GPS suffer from several limitations related to its predefined dynamics model, observability (i.e., the ability to determine, or observe, all of the relevant system parameters), the necessity of having accurate stochastic models of sensor random errors and accurate a priori covariance information for both INS and GPS data.
Over the years, non-linear integration modules based on artificial intelligence (AI) were proposed either as a complete replacement for KF or with augmentation by KF. Such modules were usually targeted for robust positioning applications in urban canyons, especially when these solutions incorporated low-end tactical grade or micro-electro-mechanical system (MEMS)–based sensors.
In this article, we wish to answer important questions and address some of the numerous concerns raised about AI-based INS/GPS integration.
Features of AI Compared to KF for INS/GPS Integration
However, AI modules still require correct system parameters obtained through training in order to be able to provide a reliable navigation solution. These parameters are unique to the inertial sensors and the GPS receivers used and are independent of the moving platform or the trajectory.
. . .
Current AI–Based Techniques for INS/GPS Integration
. . .
Training an AI module
. . .
AI-Module Design Considerations
. . .
Comparison of AI and KF Module Performance
(For Lepinsy Chanthalansy and Aboelmagd Noureldin’s complete answer to this question, including formulas and tables, please download the full article using the pdf link above.)
Copyright © 2017 Gibbons Media & Research LLC, all rights reserved.