Precise Velocity Estimation Using a Stand-Alone GPS Receiver

Graas, Frank van; Soloviev, Andrey

doi:10.1002/j.2161-4296.2004.tb00359.x

Cited by 114 publications

(94 citation statements)

References 2 publications

(2 reference statements)

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“…In order to use the carrier phase in real-time navigation applications, the unknown integer N should be removed first, which can be achieved by time-differencing the carrier phase observable (Farrell 2001(Farrell , 2006Graas and Soloviev 2004),…”

Section: Time-differenced Carrier Phases and Their Applications In Gpmentioning

confidence: 99%

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Integrated GPS/INS navigation system with dual-rate Kalman Filter

Han

Wang

2011

GPS Solut

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A dual-rate Kalman Filter (DRKF) has been developed to integrate the time-differenced GPS carrier phases and the GPS pseudoranges with INS measurements. The time-differenced GPS carrier phases, which have low noise and millimeter measurement precision, are integrated with INS measurements using a Kalman Filter with high update rates to improve the performance of the integrated system. Since the time-differenced GPS carrier phases are only relative measurements, when integrated with INS, the position error of the integrated system will accumulate over time. Therefore, the GPS pseudoranges are also incorporated into the integrated system using a Kalman Filter with a low update rate to control the accumulation of system errors. Experimental tests have shown that this design, compared to a conventional design using a single Kalman Filter, reduces the coasting error by two-thirds for a medium coasting time of 30 s, and the position, velocity, and attitude errors by at least one-half for a 45-min field navigation experiment.

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Section: Time-differenced Carrier Phases and Their Applications In Gpmentioning

confidence: 99%

“…The time-differenced GPS carrier phase (Farrell 2001(Farrell , 2006Graas and Soloviev 2004) is a precise and unambiguous measurement. It is anticipated that, if integrated with INS, the time-differenced carrier phases can dramatically improve the navigation performances of the integrated system.…”

Section: Introductionmentioning

confidence: 99%

Integrated GPS/INS navigation system with dual-rate Kalman Filter

Han

Wang

2011

GPS Solut

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“…For precise velocity and frequency estimates, the change in the GPS carrier phase over a selected time interval is used [11]. The carrier phase Single Difference (SD) on the L1 frequency is considered for each satellite:…”

Section: Gps Precise Frequency Processingmentioning

confidence: 99%

Laboratory and Flight Test Analysis of Rubidium Frequency Reference Performance

et al. 2013

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The performance of Rubidium frequency standards is well-documented for stationary operation. In dynamic environments that include changes in orientation, acceleration, temperature, pressure, and magnetic field, the performance of frequency references for position, velocity, and timing applications is not well characterized in the literature. This paper includes results from laboratory and flight test experiments. Laboratory tests were conducted to characterize Rubidium oscillator performance under varying gravity and magnetic field conditions. Three Rubidium oscillators were flight tested. Each of the oscillators was connected to a GPS receiver for error characterization. The frequency stability performance is profiled against the flight dynamics based on a navigation-grade inertial navigation system. The goal of the paper is to increase the awareness of Rb oscillator error sources that affect in-flight frequency stability, with an emphasis on gravity sensitivity and magnetic field susceptibility.

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“…where is the sequential carrier-phase difference for satellite 'j' (equal to ), is the change in user position as computed by the inertial, consists of the transpose of the line-of-sight vector to satellite 'j', , and are two compensation terms for geometry and Doppler change correspondingly [17], and is the sequential clock drift error. GPS sequential difference geometry including change in position, is shown in Figure 8.…”

Section: Figure 6 Operational Scenario Modes Of the Aerial Platformmentioning

confidence: 99%

Seamless Indoor-Outdoor Navigation for Unmanned Multi-Sensor Aerial Platforms

Serrano

Haag

Dill

et al. 2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper discusses the development of navigation algorithms to enable seamless operation of a small-size multi-copter in an indoor-outdoor environment. In urban and indoor environments a GPS position capability may be unavailable not only due to shadowing, significant signal attenuation or multipath, but also due to intentional denial or deception. The proposed navigation algorithm uses data from a GPS receiver, multiple 2D laser scanners, and an Inertial Measurement Unit (IMU). This paper addresses the proposed multi-mode fusion algorithm and provides initial result using flight test data. This paper furthermore describes the 3DR hexacopter platform that has been used to collect data in an operational environment, starting in an open environment, transitioning to an indoor environment, traversing a building, and, finally, transitioning back to the outdoor environment. Implementation issues will be discussed.

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Precise Velocity Estimation Using a Stand-Alone GPS Receiver

Cited by 114 publications

References 2 publications

Integrated GPS/INS navigation system with dual-rate Kalman Filter

Integrated GPS/INS navigation system with dual-rate Kalman Filter

Laboratory and Flight Test Analysis of Rubidium Frequency Reference Performance

Seamless Indoor-Outdoor Navigation for Unmanned Multi-Sensor Aerial Platforms

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