Range filtering for sequential GPS receivers with external sensor augmentation

The filtering of the satellite range and range-rate measurements from single channel sequential Global Positioning System receivers is usually done with an extended Kalman filter which has state variables defined in terms of an orthogonal navigation reference frame. An attractive suboptimal alternative is range-domain filtering, in which the individual satellite measurements are filtered separately before they are combined for the navigation solution. The main advantages of range-domain filtering are decreased processing and storage requirements and simplified tuning. Several range filter mechanization alternatives are presented, along with an innovative approach for combining the filtered range-domain quantities to determine the navigation state estimate. In addition, a method is outlined for incorporating measurements from auxiliary sensors such as altimeters into the navigation state estimation scheme similarly to the satellite measurements. A method is also described for incorporating inertial measurements into the navigation state estimator as a process driver

Maintaining real-time precise point positioning during outages of orbit and clock corrections

The precise point positioning (PPP) is a popular positioning technique that is dependent on the use of precise orbits and clock corrections. One serious problem for real-time PPP applications such as natural hazard early warning systems and hydrographic surveying is when a sudden communication break takes place resulting in a discontinuity in receiving these orbit and clock corrections for a period that may extend from a few minutes to hours. A method is presented to maintain real-time PPP with 3D accuracy less than a decimeter when such a break takes place. We focus on the open-access International GNSS Service (IGS) real-time service (RTS) products and propose predicting the precise orbit and clock corrections as time series. For a short corrections outage of a few minutes, we predict the IGS-RTS orbits using a high-order polynomial, and for longer outages up to 3 h, the most recent IGS ultra-rapid orbits are used. The IGS-RTS clock corrections are predicted using a second-order polynomial and sinusoidal terms. The model parameters are estimated sequentially using a sliding time window such that they are available when needed. The prediction model of the clock correction is built based on the analysis of their properties, including their temporal behavior and stability. Evaluation of the proposed method in static and kinematic testing shows that positioning precision of less than 10 cm can be maintained for up to 2 h after the break. When PPP re-initialization is needed during the break, the solution convergence time increases; however, positioning precision remains less than a decimeter after convergence

A description of QUALCOMM Automatic Satellite Position Reporting (QASPR(R)) for mobile communications

Two satellite position reporting has been introduced into the OmniTRACS mobile satellite communication system. This system significantly improves position reporting reliability and accuracy while simplifying the terminal's hardware. The positioning technique uses the original OmniTRACS TDMA timing signal formats in the forward and return link directions plus an auxiliary, low power forward link signal through a second satellite to derive distance values. The distances are then converted into the mobile terminal's latitude and longitude in real time. A minor augmentation of the spread spectrum profile of the return link allowed the resolution of periodic ambiguities. The system also locates the two satellites in real time with fixed platforms in known locations using identical mobile terminal hardware. Initial accuracies of 1/4 mile have been realized uniformly throughout the USA using a satellite separation of 22 degrees and there are no dead zones, skywaves, or cycle slips as found in terrestrial systems like LORAN-C

The potential of LEO mega-constellations in aiding GNSS to enable positioning in challenging environments

Signals from the emerging Low Earth Orbit (LEO) satellites from mega-constellations that broadcast internet, such as Starlink (Space X), OneWeb, Iridium etc., also known as “signals of opportunity” (SOP), can potentially aid positioning. These LEO satellites are approximately 20 times closer to Earth compared to the GNSS medium-earth orbit (MEO) satellites – with 300-1500km altitudes, and 90-120 minutes orbital periods. Hence, LEO satellites provide a new navigation space infrastructure with much stronger signal power than GNSS signals. This makes these LEO signals more resilient to interference and available in deep attenuation settings. In challenging environments, with limited GNSS observations that may not allow positioning, such as in urban canyons, bushland, or bottom of mining pits, integrating LEO signals with the available GNSS observations can enable positioning. Moreover, the corresponding high speed of LEO satellites enables faster satellite geometry change, and hereby significantly shortens the convergence time for precise point positioning (PPP). In this contribution, the positioning from LEO Doppler shift time variation integrated with GNSS and two challenges in positioning using LEO will be briefly discussed. For positioning, the orbits of LEO satellites and their clock behaviour must be known. In addition, unlike GNSS satellites, LEO satellites are not equipped with atomic clocks, and typically use ultra-stable oscillators (USOs) or oven-controlled crystal oscillators (OCXOs), nor are they tightly time-synchronised with each other. The estimation and prediction of these orbits and clock errors and drift are discussed

Review of Unbiased FIR Filters, Smoothers, and Predictors for Polynomial Signals

Extracting an estimate of a slowly varying signal corrupted by noise is a common task. Examples can be found in industrial, scientific and biomedical instrumentation. Depending on the nature of the application the signal estimate is allowed to be a delayed estimate of the original signal or, in the other extreme, no delay is tolerated. These cases are commonly referred to as filtering, prediction, and smoothing depending on the amount of advance or lag between the input data set and the output data set. In this review paper we provide a comprehensive set of design and analysis tools for designing unbiased FIR filters, predictors, and smoothers for slowly varying signals, i.e. signals that can be modeled by low order polynomials. Explicit expressions of parameters needed in practical implementations are given. Real life examples are provided including cases where the method is extended to signals that are piecewise slowly varying. A critical view on recursive implementations of the algorithms is provided