Geometrical portrait of Multipath error propagation in GNSS Direct Position Estimation

Direct Position Estimation (DPE) is a method that directly estimate position, velocity, and time (PVT) information from cross ambiguity function (CAF) of the GNSS signals, significantly enhancing receiver robustness in urban environments. However, there is still a lack of theoretical characterization on multipath errors in the context of DPE theory. Geometric observations highlight the unique characteristics of DPE errors stemming from multipath and thermal noise as estimation bias and variance respectively. Expanding upon the theoretical framework of DPE noise variance through geometric analysis, this paper focuses on a geometric representation of multipath errors by quantifying the deviations in CAF and PVT solutions caused by off-centering bias relative to the azimuth and elevation angles. A satellite circular multipath bias (SCMB) model is introduced, amalgamating CAF and PVT errors from multiple satellite channels. The boundaries for maximum or minimum PVT bias are established through discussions encompassing various multipath conditions. The correctness of the multipath geometrical portrait is confirmed through both Monte Carlo simulations and urban canyon tests. The findings indicate that the maximum PVT bias depends on the largest multipath errors observed across various satellite channels. Additionally, the PVT bias increases with satellite elevation angles, influenced by the CAF multipath bias projection. This serves as a reference for selecting DPE satellites from a geometric standpoint, underscoring the importance of choosing a balanced combination of high and low elevation angles to achieve an optimal satellite geometry configuration.

Weiss-Weinstein bound of frequency estimation error for very weak GNSS signals

Tightness remains the center quest in all modern estimation bounds. For very weak signals, this is made possible with judicial choices of prior probability distribution and bound family. While current bounds in GNSS assess performance of carrier frequency estimators under Gaussian or uniform assumptions, the circular nature of frequency is overlooked. In addition, of all bounds in Bayesian framework, Weiss-Weinstein bound (WWB) stands out since it is free from regularity conditions or requirements on the prior distribution. Therefore, WWB is extended for the current frequency estimation problem. A divide-and-conquer type of hyperparameter tuning method is developed to level off the curse of computational complexity for the WWB family while enhancing tightness. Synthetic results show that with von Mises as prior probability distribution, WWB provides a bound up to 22.5% tighter than Ziv-Zaka\"i bound (ZZB) when SNR varies between -3.5 dB and -20 dB, where GNSS signal is deemed extremely weak.