Joint Determination of Precorrelation Bandwidth, Sampling Frequency and Quantization in Wideband Compass Receivers

Abstract

Precorrelation filtering, sampling, and quantization are three fundamental functions for GNSS receivers. Analytical model and numerical simulation methods have been developed to evaluate the implementation losses due to different combinations of these parameters, first in the context of white noise and later non‐white interference. However, the performance metric was loss of effective carrier‐to‐noise density ratio (C/N0). This is indirect in predicting the performance degradation of a delay‐lock loop (DLL). This paper proposes an analytical model to predict the DLL performance with loss of root‐mean‐squared (RMS) tracking accuracy in meters. In the current project, a wideband Compass receiver capable of processing modern GNSS signals is required to form the core of the GNSS Vulnerabilities Assessment and Validation Platform (VAVE). The method proposed was used to determine the optimal combination of precorrelation bandwidth, sampling frequency, and quantization to guide the design of VAVE receiver hardware. Here optimality means highest possible performance with minimized implementation cost. Copyright © 2012 Institute of Navigation

An Analytical Model for BDS B1 Spreading Code Self-Interference Evaluation Considering NH Code Effects

“…This could pose a serious problem to high-sensitivity processing during acquisition, tracking or navigation bit modulation. The potential hazard caused by this ( C / N 0 ) eff decrease could be best exemplified by Figure 13 , which shows the code tracking standard deviation versus ( C s / N 0 ) eff , using established analytical method by Zhang and Zhan [ 19 ]. The B1-I receiver used in Figure 13 is assumed to have a precorrelation bandwidth of 4 MHz, a sampling frequency of 16 MHz, and 2-bit quantization.…”

“…var{ e ( ε ) | τ s k } is measured discriminator output variance. var{ τ u k | τ s k } is variance of the unsmoothed code delay estimate τ u k , which is predicted by using Equation (26) [19]:$\mathrm{var}\left\{{\tau }_k^u|{\tau }_k^s\right\}=\frac{B_{L}true{\int }_{-B_r/2}^{B_r/2}{G}_s\left(f\right)G_w\left(f\right){\sin }^2\left(\pi f\Delta \right)df}{{\left(2\pi \right)}^2{C}_s{\left(true{\int }_{-B_r/2}^{B_r/2}f{G}_s\left(f\right)\sin \left(\pi f\Delta \right)df\right)}^2}\left[1+\frac{1}{T{\left(\frac{C_s}{N_0}\right)}_{eff}true{\int }_{-B_r/2}^{B_r/2}{G}_s\left(f\right)df}\right]$ which is the analytical form of code tracking variance. The term ‘( C s / N 0 ) eff ’ is effective carrier to noise ratio, and is equal to C s − I 0 , where C s is the measured signal power and I 0 the equivalent white noise as defined in Equation (16) and transformed into dB.…”

A Comprehensive Evaluation of Possible RNSS Signals in the S-Band for the KPS

“…The thermal noise jitter is determined according to the DLL discriminator type. When using coherent early-minus-late processing (CELP) and noncoherent early-minus-late processing (NELP) as discriminators, the DLL thermal noise jitter (in seconds) is defined as follows [ 41 , 42 , 43 ]: where is the DLL bandwidth and is the coherent integration time.…”

Generalised Theory on the Effects of Sampling Frequency on GNSS Code Tracking

“…It should be noted that a factor of (1–0·5B L T) appears in Equation (2) of Betz and Kolodziejski (2009a). In fact, this term originates from a flaw from that work as detailed by Zhang and Zhan (2012), thus it has been omitted here.…”