Carrier phase prediction method for GNSS precise positioning in challenging environment

Li, Zhuo; Zhang, Tisheng; Qi, Farui; Tang, Hailiang; Niu, Xiaoji

doi:10.1016/j.asr.2018.12.015

Cited by 7 publications

(7 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The objective of path planning for mobile robots is to find an optimal collision-free path from a starting point to a target point [25], however the high-cost of the technology used for these systems make the real and systematic application in the field difficult. Moreover, the signals of GNSS devices are blocked frequently in challenging environments, and the discontinuous carrier phases seriously affect the application of GNSS precise positioning reducing the adoption of these systems [26]. Robot mowers that move randomly are indeed nowadays largely used.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the Cutting Performance of a Robot Mower Using Custom Built Software

Martelloni

Fontanelli

Pieri³

et al. 2019

Agronomy

View full text Add to dashboard Cite

Before the introduction of positioning technologies in agriculture practices such as global navigation satellite systems (GNSS), data collection and management were time-consuming and labor-intensive tasks. Today, due to the introduction of advanced technologies, precise information on the performance of agricultural machines, and smaller autonomous vehicles such as robot mowers, can be collected in a relatively short time. The aim of this work was to track the performance of a robot mower in various turfgrass areas of an equal number of square meters but with four different shapes by using real-time kinematic (RTK)-GNSS devices, and to easily extract data by a custom built software capable of calculating the distance travelled by the robot mower, the forward speed, the cutting area, and the number of intersections of the trajectories. These data were then analyzed in order to provide useful functioning information for manufacturers, entrepreneurs, and practitioners. The path planning of the robot mower was random and the turfgrass area for each of the four shapes was 135 m2 without obstacles. The distance travelled by the robot mower, the mean forward speed, and the intersections of the trajectories were affected by the interaction between the time of cutting and the shape of the turfgrass. For all the different shapes, the whole turfgrass area was completely cut after two hours of mowing. The cutting efficiency decreased by increasing the time, as a consequence of the increase in overlaps. After 75 minutes of cutting, the efficiency was about 35% in all the turfgrass areas shapes, thus indicating a high level of overlapping.

show abstract

Section: Introductionmentioning

confidence: 99%

Assessment of the Cutting Performance of a Robot Mower Using Custom Built Software

Martelloni

Fontanelli

Pieri³

et al. 2019

Agronomy

View full text Add to dashboard Cite

show abstract

“…The standard deviation of INS, GNSS without predictor, and GNSS/INS with KNN predictor can be seen in Table 1, when the GNSS receiver with and without blocking in a few seconds. In References [13][14][15], different algorithm predictors to predict the output values of GPS were used, while, in this paper, a new algorithm was used to predict GNSS output values. Figures 5-7 and Table 1 show the main results of this new method.…”

Section: Scenario (Iii)mentioning

confidence: 99%

“…The results in this paper show that WNNs are more efficient than RBFNNs. Additionally, a carrier phase prediction method on baseband signal processing level to solve GNSS blocking signals in challenging environments was proposed in Reference [15].…”

Section: Introductionmentioning

confidence: 99%

Using KNN Algorithm Predictor for Data Synchronization of Ultra-Tight GNSS/INS Integration

et al. 2021

View full text Add to dashboard Cite

The INS system’s update rate is faster than that of the GNSS receiver. Additionally, GNSS receiver data may suffer from blocking for a few seconds for different reasons, affecting architecture integrations between GNSS and INS. This paper proposes a novel GNSS data prediction method using the k nearest neighbor (KNN) predictor algorithm to treat data synchronization between the INS sensors and GNSS receiver and overcome those GNSS receiver’s blocking, which may occur for a few seconds. The experimental work was conducted on a flying drone over a minor Hungarian (Mátyásföld, 47.4992 N, 19.1977 E) model airfield. The GNSS data are predicted by four different scenarios: the first is no blocking of data, and the other three have blocking periods of 1, 4, and 8 s, respectively. Ultra-tight architecture integration is used to perform the GNSS/INS integration to deal with the INS sensors’ inaccuracy and their divergence throughout the operation. The results show that using the GNSS/INS integration system yields better positioning data (in three axes (X, Y, and Z)) than using a stand-alone INS system or GNSS without a predictor.

show abstract

“…Integrated navigation based on information fusion and prediction algorithms is more advantageous than individual navigation systems [3,4]. Although the Global Navigation Satellite System (GNSS) has higher positioning accuracy than other positioning methods, it is susceptible to various interferences such as multipath effects which include radar, electromagnetic interference, and signal blocks [5,6], contrary to a single-antenna GNSS/Strap-down integrated navigation system (SINS) [7]. An enhanced method for single-antenna Global Position System (GPS) based attitude determination is proposed [8].…”

Section: Introductionmentioning

confidence: 99%

“…When the vehicle moves linearly, the gyro of the INS does not work, the heading angle gradually diverges, and there is a large error in the heading observation of the GNSS. Multi-baseline GNSS can determine attitude by using carrier-phase differential technology [5,9,10]. However, multi-antenna GNSS has a high-cost and complicated structure.…”

Section: Introductionmentioning

confidence: 99%

Research on Low-Cost Attitude Estimation for MINS/Dual-Antenna GNSS Integrated Navigation Method

Wang

Liu

et al. 2019

Micromachines

View full text Add to dashboard Cite

A high-precision navigation system is required for an unmanned vehicle, and the high-precision sensor is expensive. A low-cost, high-precision, dual-antenna Global Navigation Satellite System/Micro-electromechanical Systems-Inertial Navigation System (GNSS/MINS) combination method is proposed. The GNSS with dual antennas provides velocity, position, and attitude angle information as the measurement information is combined with the MINS. By increasing the heading angle, pitch angle, velocity, the accuracy of the integrated system is improved. The Extended Kalman Filtering (EKF) integrated algorithm simulation is designed to verify the feasibility and is realized based on the Field Programmable Gate Array and Advanced RISC Machine (ARM+FPGA) system. Static and dynamic tests were performed using the Synchronous Position, Attitude and Navigation (SPAN-CPT) as a reference system. The results show that the velocity, position, and attitude angle accuracy were improved. The yaw angle and pitch angle accuracy were 0.2° Root Mean Square (RMS) and 0.3° RMS, respectively. The method can be used as a navigation system for the unmanned vehicle.

show abstract

Carrier phase prediction method for GNSS precise positioning in challenging environment

Cited by 7 publications

References 24 publications

Assessment of the Cutting Performance of a Robot Mower Using Custom Built Software

Assessment of the Cutting Performance of a Robot Mower Using Custom Built Software

Using KNN Algorithm Predictor for Data Synchronization of Ultra-Tight GNSS/INS Integration

Research on Low-Cost Attitude Estimation for MINS/Dual-Antenna GNSS Integrated Navigation Method

Contact Info

Product

Resources

About