Smartphone indoor localization has attracted considerable attention over the past decade because of the considerable business potential in terms of indoor navigation and location-based services. In particular, Wi-Fi RSS (received signal strength) fingerprinting for indoor localization has received significant attention in the industry, for its advantage of freely using off-the-shelf APs (access points). However, RSS measured by heterogeneous mobile devices is generally biased due to the variety of embedded hardware, leading to a systematical mismatch between online measures and the pre-established radio maps. Additionally, the fingerprinting method based on a single RSS measurement usually suffers from signal fluctuations due to environmental changes or human body blockage, leading to possible large localization errors. In this context, this study proposes a space-constrained pairwise signal strength differences (PSSD) strategy to improve Wi-Fi fingerprinting reliability, and mitigate the effect of hardware bias of different smartphone devices on positioning accuracy without requiring a calibration process. With the efforts of these two aspects, the proposed solution enhances the usability of Wi-Fi fingerprint positioning. The PSSD approach consists of two critical operations in constructing particular fingerprints. First, we construct the signal strength difference (SSD) radio map of the area of interest, which uses the RSS differences between APs to minimize the device-dependent effect. Then, the pairwise RSS fingerprints are constructed by leveraging the time-series RSS measurements and potential spatial topology of pedestrian locations of these measurement epochs, and consequently reducing possible large positioning errors. To verify the proposed PSSD method, we carry out extensive experiments with various Android smartphones in a campus building. In the case of heterogeneous devices, the experimental results demonstrate that PSSD fingerprinting achieves a mean error ∼20% less than conventional RSS fingerprinting. In addition, PSSD fingerprinting achieves a 90-percentile accuracy of no greater than 5.5 m across the tested heterogeneous smartphones

The KBR (K-Band ranging instrument) and LRI (Laser Interferometer) are used to measure the distance variations between the twin spacecraft, which is one of the most important observations used for temporal gravity field recovery. The data pre-processing from raw or so-called Level-1A into the Level-1B format, which is suited for gravity field recovery, is a key step. Although Level-1B files are made publicly available by the GRACE-FO Science Data System (SDS), it has been shown that alternative Level-1B datasets may yield improved the results of gravity field[1]. Investigations of the pre-processing may allow us to improve the gravity recovery strategy and are essential to support developments of gravimetric satellite missions in China, such as TianQin-2 project. The pre-processing normally includes the time-tag synchronization, filtering and resampling, and other corrections, e.g. light-time correction for both instruments and antenna offset correction for KBR. We re-processed the Level-1A data of KBR and LRI to the Level1B using code developed at IGG/Wuhan. The results show good agreement in case of the RL04 KBR data, i.e. the differences between IGG-KBR1B and SDS-KBR1B are about three orders of magnitude lower than the instrument noise level for KBR. For the LRI, we found that phase jumps are not removed completely in the SDS-LRI1B products. As shown by Abich[2], these phase jumps in the LRI phase observations are mainly coincident with thruster activations. Our work will analyze the impacts of different processing methods of the raw data on post-fit residuals and the gravity field recovery based on IGG-KBR1B and IGG-LRI1B datasets.   [1] Wiese, D.: SDS Level-2/-3 JPL, GRACE/GRACE-FO Science Team Meeting 2020, online, 27 October–29 Oct 2020, GSTM2020-75, https://doi.org/10.5194/gstm2020-75, 2020. [2]    Abich K, Abramovici A, Amparan B, et al. In-Orbit Performance of the GRACE Follow-on Laser Ranging Interferometer [J]. Phys Rev Lett, 2019, 123(3): 031101, https://doi.org/10.1103/PhysRevLett.123.031101.