SUMMARY: The Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE Follow-On mission have been estimating temporal gravity field models for various geoscience applications since 2002. While the errors in atmospheric and oceanic de-aliasing (AOD) model are still the key limitation for GRACE-type missions especially taking advantages of improved laser ranging interferometer (LRI), this paper explores the realistic assumption of continuously reducing AOD model error in global scale and regional scale (e.g. ocean, Greenland, Qinghai-Tibet plateau and South America) in the anticipated future. For this, using a realistic orbit scenario and error assumptions both for instrument and background model errors, the 5-yr full-scale simulations are implemented in the context of 26 scenarios with different AOD model errors. Our overall simulation results demonstrate that: (1) when the AOD model errors are reduced in the global scale, the corresponding gain in temporal gravity field determination is significant, with the cumulative geoid height error decrease from 11.28 to 6.25 mm in spectral domain, as well as 26.3–65.2 per cent noise reduction in terms of mean RMS residuals over ocean in spatial domain. (2) When the AOD model errors are reduced in the regional scale, the improvements of temporal gravity field estimations are diverse in different simulation scenarios. The most notable noise reduction, with 22.9–43.9 per cent noise reduction in spatial domain, is observed when the AOD model error decreases in ocean. The limited noise reductions (0.1 per cent and 1.3 per cent respectively in spectral and spatial domain) are observed in the temporal gravity field estimations if the AOD model is refined in other typical regions such as the Qinghai-Tibet plateau, the Greenland and the South America. (3) However, when the satellites passing over the AOD model refined regions, the along-orbit range rate analysis indicates that, there are visible differences by about 50.0 nm s−1 in terms of range rate residuals as well as 11.0–48.5 nm s−1 in terms of the mean RMS of range rate residuals. These results reflect the benefits of reducing AOD model error in both global and regional scale for improving GRACE-type temporal gravity estimation, especially considering the development of LRI technology.

The current state‐of‐the‐art of satellite gravity data processing makes use of de‐aliasing products to reduce high‐frequency mass anomalies. For example, the most recent official Atmosphere and Ocean De‐aliasing products (AOD1B‐RL06) are applied for the Gravity Recovery and Climate Experiment (GRACE) and GRACE‐Follow On (GRACE‐FO) missions. The temporal resolution of AOD1B‐RL06 is 3 h, and spectrally, they are computed up to degree and order 180. In this study, we explore a refined, e.g., geometrically, physically, and numerically improved, mass integration approach that is important for computing the atmosphere part of these products. Besides, the newly available ERA5 climate data are used to produce a new set of non‐tidal atmosphere de‐aliasing product (HUST‐ERA5) that is computed hourly up to degree and order 100, covering 2002 onwards. Despite an overall agreement with AOD1B‐RL06 (correlation0.99), considerable discrepancies still exist between HUST‐ERA5 and AOD1B‐RL06. The possible reasons are therefore analyzed, and we find the input climate data, sampling rate and integration method may result in a product difference of ∼0.3, ∼0.15 and ∼0.05 millimeter geoid height, respectively. The total differences between HUST‐ERA5 and AOD1B‐RL06 can lead to a mean variation of ∼7.34 on the laser ranging interferometry (LRI) range‐rate residuals, for example, during January 2019, which is already close to the LRI precision. This impact is invisible for the GRACE (‐FO) gravity inversion because of the less accurate on‐board KBR (K‐band ranging) instrument, however, it will be non‐negligible and should be considered when the LRI completely replaces the KBR in the future gravity missions.

Indoor navigation and location-based services increasingly show promising marketing prospects. Indoor positioning based on Wi-Fi radio signal has been studied for more than a decade because Wi-Fi, a signal of opportunity without extra cost, is extensively deployed for internet connections. Bayesian fingerprinting positioning, a classical Wi-Fi-based indoor positioning method, consists of two phases: radio map learning and position inference. Thus far, the application of Bayesian fingerprinting positioning is limited due to its poor usability; radio map learning requires an adequate number of received signal strength indication (RSSI) observables at each reference point, long-term fieldwork, and high development and maintenance costs. In this paper, based on a statistical analysis of actual RSSI observables, a Weibull–Bayesian density model is proposed to represent the probability density of Wi-Fi RSSI observables. The Weibull model, which is parameterized with three parameters that can be calculated with fewer samples, can calculate the probability density with a higher accuracy than the traditional histogram method. Furthermore, the parameterized Weibull model can simplify the radio map by storing only three parameters that can restore the whole probability density, i.e., it is not necessary to store the probability distribution based on traditionally separated RSSI bins. Bayesian positioning inference is performed in the positioning phase using probability density rather than the traditional probability distribution of predefined RSSI bins. The proposed method was implemented on an Android smartphone, and the performance was evaluated in different indoor environments. Results revealed that the proposed method enhanced the usability of Wi-Fi Bayesian fingerprinting positioning by requiring fewer RSSI observables and improved the positioning accuracy by 19–32% in different building environments compared with the classic histogram-based method, even when more samples were used.

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