A filter algorithm for receiver tracking loops assisted by inertial information

In traditional satellite navigation receivers, the parameters of tracking loop such as loop bandwidth and integration time are usually set in the design of the receivers according to different scenarios. The signal tracking performance is limited in traditional receivers. In addition, when the tracking ability of weak signals is improved by extending the integration time, negative effect of residual frequency error becomes more and more serious with extension of the integration time. To solve these problems, this paper presents out research on receiver tracking algorithms and proposes an optimised tracking algorithm with inertial information. The receiver loop filter is designed based on Kalman filter, reducing the phase jitter caused by thermal noise in the weak signal environment and improving the signal tracking sensitivity. To confirm the feasibility of the proposed algorithm, simulation tests are conducted.

Exploring Post COVID-19 Outbreak Intradaily Mobility Pattern Change in College Students: A GPS-Focused Smartphone Sensing Study

With the outbreak of the COVID-19 pandemic in 2020, most colleges and universities move to restrict campus activities, reduce indoor gatherings and move instruction online. These changes required that students adapt and alter their daily routines accordingly. To investigate patterns associated with these behavioral changes, we collected smartphone sensing data using the Beiwe platform from two groups of undergraduate students at a major North American university, one from January to March of 2020 (74 participants), the other from May to August (52 participants), to observe the differences in students' daily life patterns before and after the start of the pandemic. In this paper, we focus on the mobility patterns evidenced by GPS signal tracking from the students' smartphones and report findings using several analytical methods including principal component analysis, circadian rhythm analysis, and predictive modeling of perceived sadness levels using mobility-based digital metrics. Our findings suggest that compared to the pre-COVID group, students in the mid-COVID group generally 1) registered a greater amount of midday movement than movement in the morning (8–10 a.m.) and in the evening (7–9 p.m.), as opposed to the other way around; 2) exhibited significantly less intradaily variability in their daily movement; 3) visited less places and stayed at home more everyday, and; 4) had a significant lower correlation between their mobility patterns and negative mood.

Improved spatio-temporal measurements of visually evoked fields using optically-pumped magnetometers

Recent developments in performance and practicality of optically-pumped magnetometers (OPMs) have enabled new capabilities in non-invasive brain function mapping through magnetoencephalography. In particular, the lack of cryogenic operating conditions allows for more flexible placement of sensor heads closer to the brain, leading to improved spatial resolution and source localisation capabilities. Through recording visually evoked brain fields (VEFs), we demonstrate that the closer sensor proximity can be exploited to improve temporal resolution. We use OPMs, and superconducting quantum interference devices (SQUIDs) for reference, to measure brain responses to flash and pattern reversal stimuli. We find highly reproducible signals with consistency across multiple participants, stimulus paradigms and sensor modalities. The temporal resolution advantage of OPMs is manifest in a twofold improvement, compared to SQUIDs. The capability for improved spatio-temporal signal tracing is illustrated by simultaneous vector recordings of VEFs in the primary and associative visual cortex, where a time lag on the order of 10–20 ms is consistently found. This paves the way for further spatio-temporal studies of neurophysiological signal tracking in visual stimulus processing, and other brain responses, with potentially far-reaching consequences for time-critical mapping of functionality in healthy and pathological brains.

Efficient and Unique learning of the Complex Receiver Structure of Galileo E5 AltBOC using an Educational Software in Matlab

This paper presents an efficient and unique method to learn the complex receiver structure of Galileo E5 AltBOC (Alternative Binary Offset Carrier). The software application is designed in Matlab to present each step involved in the design of software receiver of Galileo E5 signal. This software application contains the fundamental concepts of the Galileo E5 signal in the form of signal acquisition, signal tracking, extraction of the navigation data, power spectral density (PSD) of the AltBOC (15, 10), Fast Fourier Transform (FFT) of the E5a and E5b signal and implementation of the subcarrier used for the AltBOC (15,10). This paper also presents the extraction of navigation data by a novel based approach using the prompt channel of carrier tracking from the code loop discriminator.

Research on AR-AKF Model Denoising of the EMG Signal

Electromyography (EMG) signals can be used for clinical diagnosis and biomedical applications. It is very important to reduce noise and to acquire accurate signals for the usage of the EMG signals in biomedical engineering. Since EMG signal noise has the time-varying and random characteristics, the present study proposes an adaptive Kalman filter (AKF) denoising method based on an autoregressive (AR) model. The AR model is built by applying the EMG signal, and the relevant parameters are integrated to find the state space model required to optimally estimate AKF, eliminate the noise in the EMG signal, and restore the damaged EMG signal. To be specific, AR autoregressive dynamic modeling and repair for distorted signals are affected by noise, and AKF adaptively can filter time-varying noise. The denoising method based on the self-learning mechanism of AKF exhibits certain capabilities to achieve signal tracking and adaptive filtering. It is capable of adaptively regulating the model parameters in the absence of any prior statistical knowledge regarding the signal and noise, which is aimed at achieving a stable denoising effect. By comparatively analyzing the denoising effects exerted by different methods, the EMG signal denoising method based on the AR-AKF model is demonstrated to exhibit obvious advantages.

A model for marine sedimentary carbonate diagenesis and paleoclimate proxy signal tracking: IMP v1.0

The preservation of calcium carbonate in marine sediments is central to controlling the alkalinity balance of the ocean and, hence, the ocean–atmosphere partitioning of CO2. To successfully address carbon cycle–climate dynamics on geologic (≫1 kyr) timescales, Earth system models then require an appropriate representation of the primary controls on CaCO3 preservation. At the same time, marine sedimentary carbonates represent a major archive of Earth history, as they have the potential to preserve how seawater chemistry, isotopic composition, and even properties of planktic and benthic ecosystems, change with time. However, changes in preservation and even chemical erosion of previously deposited CaCO3, along with the biogenic reworking of upper portions of sediments, whereby sediment particles are translocated both locally and nonlocally between different depths in the sediments, all act to distort the recorded signal. Numerical models can aid in recovering what the “true” environmental changes might have been, but only if they appropriately account for these processes. Building on a classical 1-D reaction-transport framework, we present a new diagenetic model – IMP (Implicit model of Multiple Particles (and diagenesis)) – that simulates biogeochemical transformations in carbonate-hosted proxy signals by allowing for populations of solid carbonate particles to possess different physicochemical characteristics such as isotopic value, solubility and particle size. The model also utilizes a variable transition matrix to implement different styles of bioturbation. We illustrate the utility of the model for deciphering past environmental changes using several hypothesized transitions of seawater proxies obscured by sediment mixing and chemical erosion. To facilitate the use of IMP, we provide the model in Fortran, MATLAB and Python versions. We described IMP with integration into Earth system models in mind, and we present the description of this coupling of IMP with the “cGENIE.muffin” model in a subsequent paper.

Research on Acquisition and Tracking Algorithm of Global Satellite Positioning Receiver Based on UWB

In order to overcome the problems of the traditional algorithm, such as the time-consuming execution of acquisition instructions, low signal tracking accuracy, and low signal capture accuracy, a global satellite positioning receiver acquisition and tracking algorithm based on UWB technology is designed in this study. On the basis of expounding the pulse generation method and working principle in UWB technology, this paper analyzes in detail the characteristics of UWB technology, such as antimultipath, low power consumption, and strong penetration. Then, on the basis of window function filtering, in the process of three-dimensional search of global satellite positioning signal, firstly, the satellite signal entering the GPS software receiver is processed by RF front-end mixing and AD sampling, and then, the signal tracking and navigation message solving are completed according to the relationship between the influence factor and Doppler frequency offset. The experimental results show that the execution time of the acquisition instruction of the proposed algorithm varies between 1129 ms and 1617 ms; the signal tracking accuracy ranges between 0.931 and 0.951, and the signal capture accuracy ranges between 93.3% and 95.6%, which proves that the proposed algorithm has achieved the design expectation.

Design and Implementation of Vector Tracking Loop for High-Dynamic GNSS Receiver

For the tracking of high-dynamic satellite navigation signals, the conventional scalar tracking loop (STL) is vulnerable. Frequent signal-tracking interruption affects the continuity of navigation. The vector tracking loop (VTL) can overcome this disadvantage. However, there are some difficulties in implementing existing vector tracking methods on a real-time hardware receiver, such as the synchronization problem and computation load. This paper proposes an implementation framework of VTL based on a partial open-loop numerically controlled oscillator (NCO) control mode that can be implemented with minor modifications on an existing receiver platform. The structure of VTL, the design of the navigation filter, and the key points of hardware implementation are introduced in detail. Lastly, the VTL performance was verified by a GPS simulator test. The results show that the proposed VTL can run in real-time and be significantly improved in the tracking continuity of high-dynamic signals, tracking sensitivity, positioning accuracy, and recovery time for interrupted signals compared with those of STL.