No abstract

With great potential for being applied to Internet of Things (IoT) applications, the concept of cloud-based Snapshot Real Time Kinematics (SRTK) was proposed and its feasibility under zero-baseline configuration was confirmed recently by the authors. This article first introduces the general workflow of the SRTK engine, as well as a discussion on the challenges of achieving an SRTK fix using actual snapshot data. This work also describes a novel solution to ensure a nanosecond level absolute timing accuracy in order to compute highly precise satellite coordinates, which is required for SRTK. Parameters such as signal bandwidth, integration time and baseline distances have an impact on the SRTK performance. To characterize this impact, different combinations of these settings are analyzed through experimental tests. The results show that the use of higher signal bandwidths and longer integration times result in higher SRTK fix rates, while the more significant impact on the performance comes from the baseline distance. The results also show that the SRTK fix rate can reach more than 93% by using snapshots with a data size as small as 255 kB. The positioning accuracy is at centimeter level when phase ambiguities are resolved at a baseline distance less or equal to 15 km.

No abstract

Direct position estimation (DPE) has arisen as an appealing alternative to the conventional two‐step positioning (2SP) approach. The usual metric to assess the performance of estimators is the Cramér‐Rao Bound (CRB). However, the CRB is only accurate in the high signal‐to‐noise ratio (SNR) region. In the lower SNR regions, high estimation errors take place and the variance of the estimates approach the a priori domain of the parameter. The DPE approach is expected to overcome the 2SP approach in such conditions. With the objective of throwing light on this aspect, this paper shows the derivation of an approximate Ziv‐Zakai Bound (ZZB) for the DPE approach and a mean square error (MSE) performance approximation based on the ZZB for the conventional approach. The derived bounds are compared with their associated CRBs and also with the performance of the corresponding maximum likelihood estimator (MLE). © 2018 Institute of Navigation.

Based on the Ziv-Zakai methodology to bound estimators, we derived an estimation bound able to predict the mean square error degradation due to model mismatches. In this article, we build upon this result to provide a performance comparison between mean and median estimators in the presence of outliers. The latter is well known to be statistically more robust than the mean in the presence of outliers. Here we show this superiority by comparing their theoretical error bounds. Analytical results are obtained, which are validated by computer simulations.