Performance analysis of a stand-alone high-sensitivity receiver

MacGougan, Glenn; Lachapelle, Gérard; Klukas, Richard; Siu, Kai‐Yeung; Garin, Lionel; Shewfelt, J.; Cox, Glenn W.

doi:10.1007/s10291-002-0029-z

Cited by 56 publications

(6 citation statements)

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“…The second method, derived empirically, collects data at frequent intervals, and integrates the area within the approximate boundary. This latter approach is faster, more convenient, and experience shows is suitable for use with lower accuracy (and therefore lower cost) standalone instruments (MacGougan et al 2002(MacGougan et al , Øvstedal 2002. Consequently, these tools are now the standard practice for field measurement within the CAP.…”

Section: Introductionmentioning

confidence: 99%

Assessing the error of polygonal area measurements: a general formulation with applications to agriculture

Bogaert¹,

Delincé²,

Kay³

2005

Meas. Sci. Technol.

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This paper proposes a simple and general theoretical framework for the error assessment of area measurements of planar polygonal surfaces. The general formulation is first developed, both for the case of correlated and for the case of independent measurements, where a compact formulation can be obtained for the latter. These results are then used in the context of agriculture, with the aim of assessing field area measurement errors when using a global positioning system (GPS) device, enhanced using a simulated EGNOS (European geostationary navigation overlay service) time series. They show that for GPS/EGNOS measurements made by an operator moving along the border of a field, area measurement error is linked both to the operator speed and to the acquisition rate of the GPS device. For typical field sizes found in the European Union, ranging from 0.5 ha to 5 ha, the coefficient of variation (CV) for area measurement errors is about 1% to 5%. These results depend on the field area, but they can be considered to be insensitive with respect to the field shape. They also show that field area measurement errors can be limited if an appropriate combination of operator speed and GPS acquisition rate is selected. Though the practical case study presented here is focused on polygonal agricultural fields, it is expected that various other fields (medical and remotely sensed imagery, geographical information system data, computer vision analysis,…) should also benefit from the theoretical results hereby obtained.

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Section: Introductionmentioning

confidence: 99%

Assessing the error of polygonal area measurements: a general formulation with applications to agriculture

Bogaert¹,

Delincé²,

Kay³

2005

Meas. Sci. Technol.

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“…With the development of safety critical applications of Intelligent Transportation Systems (ITS), the necessity for navigation systems to be reliable has become more and more stringent (Feng and Ochieng 2007;Wang et al 2020). However, it should be noted that GNSS pseudoranges may contain gross errors due to multipath interferences and non-line-of-sight (NLOS) receptions in urban environments (Macgougan et al 2002;Sun et al 2022). Faulty GNSS measurements significantly reduce the reliability of vehicular navigation systems in urban environments, with the driver's life becoming threatened in the worst cases (Cheng et al 2021).…”

Section: Introductionmentioning

confidence: 99%

A factor set-based GNSS fault detection and exclusion for vehicle navigation in urban environments

et al. 2023

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With the rapid development of safety critical applications of Intelligent Transportation Systems (ITS), Global Navigation Satellite System (GNSS) fault detection and exclusion (FDE) methods have made navigation systems increasingly reliable.However, in multi-fault scenarios of urban environments, FDE methods generally demand massive calculations and have a high risk of missed detection and false alarm.To deal with this issue, we proposed a factor set based FDE algorithm for the integration of GNSS and Inertial Measurement Units (IMU). The FDE is first performed efficiently via consistency checking over far fewer subsets of the pseudorange. Afterwards, the FDE results are validated by missed-detection and false-alarm checks. The misseddetection-check factor is designed by predicting the maximum horizontal GNSS positioning error, while the false-alarm-check factor is designed with the aid of IMU mechanization. Following FDE, a loosely coupled GNSS/IMU integration is carried out to output the final estimation of the position, velocity and attitude of the vehicle.The proposed algorithm improved both horizontal and 3D positioning accuracy by more than 50% in the field test, compared to the traditional GNSS/IMU loosely coupled scheme. Additionally, with the proposed algorithm, the resultant accuracy of the velocity and of the heading angle were improved by over 20% and 50% respectively.

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“…Nevertheless, the EKF accuracy relies on an accurate random model, the accuracy of which is difficult to ensure because of the large motion of the carrier and the urban environment [11,12]. In this case, if the random model is inaccurate, significant errors appear in the integrated navigation positions [13][14][15]. Accordingly, a series of adaptive filtering methods have been proposed to improve the accuracy of dynamic and measurement models.…”

Section: Introductionmentioning

confidence: 99%

GNSS/MEMS IMU vehicle integrated navigation algorithm constrained by displacement vectors in urban environment

Feng,

Huang,

Jing

et al. 2023

Meas. Sci. Technol.

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The global navigation satellite system (GNSS) and a micro-electro-mechanical system inertial measurement unit (MEMS IMU) can be integrated to form a robust navigation system, providing continuous and accurate location information on dynamic vehicles. However, the performance of a GNSS/MEMS IMU integrated navigation system is easily affected by the accuracy of measurement noise matrix, which is significantly reduced in the urban environment. The adaptive Kalman filter can improve the measurement noise matrix reliability by constructing robust equivalent weight factors; however, GNSS observations are easily misjudged as exception sources that can deteriorate the filter stability. To address these problems, a novel integrated algorithm with a displacement vector constraint is proposed based on the kinematic characteristics of vehicles, which are considered as judgment factors. In this case, the measurement noise can be adjusted accordingly only when the GNSS observations are judged as abnormal. In this study, two types of vehicle navigation experiments were conducted: a low-speed wheeled robot in a campus environment and a dynamic vehicle in an actual urban environment. The experimental results showed that the three-dimensional navigation accuracy was improved by 47% and 55% compared with the conventional algorithm under the two different environments, respectively.

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Performance analysis of a stand-alone high-sensitivity receiver

Cited by 56 publications

References 1 publication

Assessing the error of polygonal area measurements: a general formulation with applications to agriculture

Assessing the error of polygonal area measurements: a general formulation with applications to agriculture

A factor set-based GNSS fault detection and exclusion for vehicle navigation in urban environments

GNSS/MEMS IMU vehicle integrated navigation algorithm constrained by displacement vectors in urban environment

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