scholarly journals High-accuracy differential tracking of low-cost GPS receivers

Author(s):  
Will Hedgecock ◽  
Miklos Maroti ◽  
Janos Sallai ◽  
Peter Volgyesi ◽  
Akos Ledeczi
Keyword(s):  
Low Cost ◽  
Navigation ◽  
2007 ◽  
Vol 54 (1) ◽  
pp. 53-63 ◽  
Author(s):  
TOMAS BERAN ◽  
RICHARD B. LANGLEY ◽  
SUNIL B. BISNATH ◽  
LUIS SERRANO

2006 ◽  
Vol 59 (3) ◽  
pp. 365-379 ◽  
Author(s):  
Chris Hide ◽  
Terry Moore ◽  
Chris Hill ◽  
David Park

It is well known that GPS measurements are regularly obstructed in urban environments. Positioning accuracy in such environments is significantly degraded and in many areas, it is not possible to obtain a GPS position fix at all. There are currently two methods that can be used to improve availability in the urban environment. Firstly, GPS receivers can be augmented with dead reckoning sensors such as an INS. Alternatively, High Sensitivity GPS (HSGPS) receivers can be used which are able to acquire and track very weak signals. This paper assesses the performance obtained from a GPS and low cost INS integrated system and a HSGPS receiver in an urban environment in Nottingham, UK. The navigation systems are compared to a high accuracy integrated GPS/INS system which is used to provide a reference trajectory. It is demonstrated that the differential GPS and low cost INS system can provide horizontal positioning accuracy of better than 2·5 m RMS in real-time, and better than 1 m RMS in post-processing, whereas the non-differential HSGPS receiver provides a real-time performance of 5 m RMS. These results were obtained in an environment where, with conventional GPS receivers, a position solution is only available 48·4% of the time. Operational considerations such as initial alignment of the GPS and low cost INS are also discussed when comparing the two systems for urban positioning applications.


2020 ◽  
Author(s):  
Derek Schulte ◽  
Kyam Krieger ◽  
Carl W. Chin ◽  
Alexander Sonn
Keyword(s):  
Low Cost ◽  

Author(s):  
Jonas Austerjost ◽  
Robert Söldner ◽  
Christoffer Edlund ◽  
Johan Trygg ◽  
David Pollard ◽  
...  

Machine vision is a powerful technology that has become increasingly popular and accurate during the last decade due to rapid advances in the field of machine learning. The majority of machine vision applications are currently found in consumer electronics, automotive applications, and quality control, yet the potential for bioprocessing applications is tremendous. For instance, detecting and controlling foam emergence is important for all upstream bioprocesses, but the lack of robust foam sensing often leads to batch failures from foam-outs or overaddition of antifoam agents. Here, we report a new low-cost, flexible, and reliable foam sensor concept for bioreactor applications. The concept applies convolutional neural networks (CNNs), a state-of-the-art machine learning system for image processing. The implemented method shows high accuracy for both binary foam detection (foam/no foam) and fine-grained classification of foam levels.


Author(s):  
Jong-Hwa Yoon ◽  
Huei Peng

Knowing vehicle sideslip angle accurately is critical for active safety systems such as Electronic Stability Control (ESC). Vehicle sideslip angle can be measured through optical speed sensors, or dual-antenna GPS. These measurement systems are costly (∼$5k to $100k), which prohibits wide adoption of such systems. This paper demonstrates that the vehicle sideslip angle can be estimated in real-time by using two low-cost single-antenna GPS receivers. Fast sampled signals from an Inertial Measurement Unit (IMU) compensate for the slow update rate of the GPS receivers through an Extended Kalman Filter (EKF). Bias errors of the IMU measurements are estimated through an EKF to improve the sideslip estimation accuracy. A key challenge of the proposed method lies in the synchronization of the two GPS receivers, which is achieved through an extrapolated update method. Analysis reveals that the estimation accuracy of the proposed method relies mainly on vehicle yaw rate and longitudinal velocity. Experimental results confirm the feasibility of the proposed method.


2013 ◽  
Author(s):  
Erica Nocerino ◽  
Fabio Menna ◽  
Salvatore Troisi
Keyword(s):  
Low Cost ◽  

2013 ◽  
Vol 834-836 ◽  
pp. 930-934
Author(s):  
Shou Liang Yang ◽  
Bao Liang Yang

The paper proposes a new design of high-accuracy On-line Metal Thickness Measuring Instrument, which was based on EP2C20 series FPGA chip, through adding NiosII soft processor and other interfaces to FPGA, equipped with high precision data collection system and TFT LCD module and so on. The key hardware blocks schematics and components of the RC Oscillation Circuit,eddy current sensor Circuit,rectifier and filter Circuit,A/D converting circuit,FPGA Circuit are described,software flow charts and sample codes are given. According to practice, The measurement range of this system is 1~100 mm and the resolving power is 0.1 μm. degree of linearity is 1%, The system has many features including small volume of hardware, low cost, high detecting precision, convenient operating, high intelligent and so on, leading to broad and bright future. Key words: NiosII processor; eddy current sensor; metal thickness


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