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High-precision inertial sensors or accelerometers can provide references for free-falling motion in gravitational fields in space. They serve as the key payloads for gravity recovery missions such as CHAMP, the GRACE-type missions, and the planned Next-Generation Gravity Missions. In this work, a systematic method for electrostatic inertial sensor calibration of gravity recovery satellites is suggested, which is applied to and verified with the Taiji-1 mission. With this method, the complete operating parameters including the scale factors, the center of mass offset vector, and the intrinsic biased acceleration can be precisely calibrated with only two sets of short-term in-orbit experiments. This could reduce the gaps in data that are caused by necessary in-orbit calibrations during the lifetime of related missions. Taiji-1 is the first technology-demonstration satellite of the "Taiji Program in Space", which, in its final extended phase in 2022, could be viewed as operating in the mode of a high–low satellite-to-satellite tracking gravity mission. Based on the principles of calibration, swing maneuvers with time spans of approximately 200 s and rolling maneuvers for 19 days were conducted by Taiji-1 in 2022. Given the data of the actuation voltages of the inertial sensor, satellite attitude variations, precision orbit determinations, the inertial sensor's operating parameters are precisely re-calibrated with Kalman filters and are relayed to the Taiji-1 science team. The relative errors of the calibrations are <1% for the linear scale factors, <3% for center of mass, and <0.1% for biased accelerations. Data from one of the sensitive axes are re-processed with the updated operating parameters, and the resulting performance is found to be slightly improved over the former results. This approach could be of high reference value for the accelerometer or inertial sensor calibrations of the GFO, the Chinese GRACE-type mission, and the Next-Generation Gravity Missions. This could also create some insight into the in-orbit calibrations of the ultra-precision inertial sensors for future GW space antennas because of the technological inheritance between these two generations of inertial sensors. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System (GNSS) positioning technology has become the most effective method for real-time three-dimensional landslide monitoring. However, the GNSS observation signal is easily affected by the presence of a complex landslide environment with high occlusion and strong interference, in which case its accuracy and reliability cannot meet the requirements of landslide deformation monitoring. Although the accelerometers have strong autonomous working capacities and can complement the GNSS landslide monitoring technology, regular GNSS/accelerometer coupled deformation monitoring technology relies on high-quality GNSS measurement information in order to obtain high-precision accelerometer-reckoned results, derived by accurately estimating the baseline shift error (BSE). When the GNSS signal suffers severe interference, the GNSS monitoring error will be partially absorbed by the BSE component after Kalman filtering, resulting in the divergence of the deformation solution. In this study, an abnormal observation variance inflation model was used to process the simulated landslide monitoring data (GNSS and accelerometer raw observation) for three typical scenes—GNSS signal normally locked, signal partially lost, and short-term interruption. The results were as follows: (1) When the GNSS signal was normally locked, the accuracy was comparable to that of the coupled solution employing an accelerometer (the Root Mean Square (RMS) values in the East (E), North (N) and Upward (U) directions were 0.11 cm, 0.33 cm, and 0.30 cm, respectively). (2) When the GNSS signal was partially lost, the accelerometer could effectively suppress the low-precision float solution of the GNSS, but here, the accuracy of the coupled solution would also decrease with the duration of the floats (the RMS values were E—1.21 cm, N—0.31 cm, and U: 0.58—cm, respectively, when the floats lasted for 10 s, and increased to E—3.09 cm, N—0.39 cm, and U—1.14 cm when they lasted for 20 s, wherein E was the main simulated sliding direction). (3) When the GNSS signal was interrupted for a short time, the accuracy of the coupled solution gradually decreased during continuous interruption, and decreased more quickly during the sliding period of the landslide (when the interruption persisted for 10 s, the RMS values in the simulated landslide stability period were E—0.61 cm, N—0.24 cm, and U—0.25 cm, respectively, while in the simulated landslide sliding period they reached E—4.10 cm, N—6.84 cm, and U—2.30 cm). However, raw observations of the accelerometer could still effectively be used to assist in identifying the real state of the landslide, thereby providing auxiliary information pertinent to early landslide disaster warning. [ABSTRACT FROM AUTHOR]

High-precision, low-cost, and wide coverage indoor positioning technology is the key to indoor and outdoor integrated location-based services, and it has broad market prospects and social value. However, achieving sub-meter level positioning accuracy in indoor environments remains a real challenge due to the blockage of indoor Global Navigation Satellite System (GNSS) signals, the complexity of indoor environments, and the unpredictability of user behavior. In this paper, we introduce a multi-module BLE broadcaster (MMBB)-based indoor positioning solution in which a tightly coupled fusion architecture is implemented on a smartphone. The solution integrates ranging measurements from multiple MMBB and the measurements of the accelerometer built into a smartphone. It becomes an instant positioning solution without any training phase by adopting a calibrated linearly segmented path loss model for ranging. We apply the pedestrian walking speed derived by the smartphone accelerometer to constrain an unscented Kalman filter method that is used to estimate the location and speed. The accuracy of the proposed method is 50% at 0.79 m and 95% at 1.6 m at in terms of horizontal error distance. Position update frequency is 10 Hz and the time to first fix is 0.1 s. The system can easily adapt a global coordinator system so that it can seamlessly work together with the GNSS to form an indoor/outdoor positioning solution. [ABSTRACT FROM AUTHOR]

Monitoring the dynamic responses of bridge structures has received considerable attention. It is important to synchronously measure both the quasi-static and dynamic displacements of bridge structures. However, the traditional accelerometer method cannot capture the quasi-static displacement component, although it can detect the dynamic displacement component. To this end, a novel composite instrument of a smartstation was proposed to monitor vibration displacements of footbridges. Full-scale experiments were conducted on a footbridge to validate the feasibility of the composite instrument-based monitoring method. A Chebyshev filter and wavelet algorithms were developed to process the composite instrument measurements. It was concluded that the measurement noise of the composite instrument was mainly distributed in a frequency range of 0–0.1 Hz. In two case studies with displacement peaks of 5.7–10.0 mm and 1.3– 2.5 mm, the composite instrument accurately identified the quasi-static and dynamic displacements. The composite instrument will be a potential tool for monitoring structural dynamics because of its enhanced overall performance. [ABSTRACT FROM AUTHOR]

Behaviour is a useful indicator of an individual animal's overall wellbeing. There is widespread agreement that measuring and monitoring individual behaviour autonomously can provide valuable opportunities to trigger and refine on-farm management decisions. Conventionally, this has required visual observation of animals across a set time period. Technological advancements, such as animal-borne accelerometers, are offering 24/7 monitoring capability. Accelerometers have been used in research to quantify animal behaviours for a number of years. Now, technology and software developers, and more recently decision support platform providers, are integrating to offer commercial solutions for the extensive livestock industries. For these systems to function commercially, data must be captured, processed and analysed in sync with data acquisition. Practically, this requires a continuous stream of data or a duty cycled data segment and, from an analytics perspective, the application of moving window algorithms to derive the required classification. The aim of this study was to evaluate the application of a 'clean state' moving window behaviour state classification algorithm applied to 3, 5 and 10 second duration segments of data (including behaviour transitions), to categorise data emanating from collar, leg and ear mounted accelerometers on five Merino ewes. The model was successful at categorising grazing, standing, walking and lying behaviour classes with varying sensitivity, and no significant difference in model accuracy was observed between the three moving window lengths. The accuracy in identifying behaviour classes was highest for the ear-mounted sensor (86%–95%), followed by the collar-mounted sensor (67%–88%) and leg-mounted sensor (48%–94%). Between-sheep variations in classification accuracy confirm the sensor orientation is an important source of variation in all deployment modes. This research suggests a moving window classifier is capable of segregating continuous accelerometer signals into exclusive behaviour classes and may provide an appropriate data processing framework for commercial deployments. [ABSTRACT FROM AUTHOR]

In the past few decades, global navigation satellite system (GNSS) technology has been widely used in structural health monitoring (SHM), and the monitoring mode has evolved from long-term deformation monitoring to dynamic monitoring. This paper gives an overview of GNSS-based dynamic monitoring technologies for SHM. The review is classified into three parts, which include GNSS-based dynamic monitoring technologies for SHM, the improvement of GNSS-based dynamic monitoring technologies for SHM, as well as denoising and detrending algorithms. The significance and progress of Real-Time Kinematic (RTK), Precise Point Position (PPP), and direct displacement measurement techniques, as well as single-frequency technology for dynamic monitoring, are summarized, and the comparison of these technologies is given. The improvement of GNSS-based dynamic monitoring technologies for SHM is given from the perspective of multi-GNSS, a high-rate GNSS receiver, and the integration between the GNSS and accelerometer. In addition, the denoising and detrending algorithms for GNSS-based observations for SHM and corresponding applications are summarized. Challenges of low-cost and widely covered GNSS-based technologies for SHM are discussed, and problems are posed for future research. [ABSTRACT FROM AUTHOR]