Enhancing Weak-Signal Carrier Phase Tracking in GNSS Receivers

Curran, James T.

doi:10.1155/2015/295029

Cited by 12 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… Data KF tracking on the data channel with integration time limited to 1 ms and a Costas loop as carrier discriminator. Pure pilot KF tracking with long coherent integration and a four‐quadrant arctangent as carrier discriminator. This discriminator is characterized by a wider linearity region in comparison to other discriminators, such as the Costas, and consequently, it allows enhanced sensitivity performance (Curran, 2015). Secondary code synchronization is at first performed and the secondary code symbols are removed from the correlator outputs.…”

Section: Tracking Strategies and Experimental Setupmentioning

confidence: 99%

Kalman filtering with noncoherent integrations for Galileo E6‐B tracking

Susi

Borio

2020

Navigation

View full text Add to dashboard Cite

The former Galileo Commercial Service (CS) will provide a High Accuracy Service (HAS) and a Commercial Authentication Service (CAS). The first will disseminate free Precise Point Positioning (PPP) corrections through the E6‐B signal whereas the second will provide authentication capabilities through the E6‐C pilot component that will be encrypted. For this reason, improved processing strategies for data‐only processing need to be investigated. The combined use of Kalman filtering and noncoherent integrations is analyzed in this work. A Software Defined Radio (SDR) receiver has been developed and used for the analysis. Different scenarios were considered including simulated data with decreasing Carrier‐to‐Noise Power Spectral Density Ratio (C/N0) levels and live static and dynamic data collected under harsh environments. It emerges that the use of Kalman filtering with extended noncoherent integrations is an effective approach to improve the performance of data‐only processing.

show abstract

Section: Tracking Strategies and Experimental Setupmentioning

confidence: 99%

Kalman filtering with noncoherent integrations for Galileo E6‐B tracking

Susi

Borio

2020

Navigation

View full text Add to dashboard Cite

show abstract

“…δ(n) is the phase difference between the input voltage wave and the NCO wave at the sampling time t n . According to the two-quadrant arctangent method in [6], PFD output expression is…”

Section: Transfer Functionmentioning

confidence: 99%

“…This paper proposes a frequency meter based on Costas FLL [6], which is different from the aforementioned methods. This frequency meter circuit consists of two parts.…”

Section: Introductionmentioning

confidence: 99%

A frequency meter based on Costas FLL for resonant accelerometers

Yafei,

Rui,

Liuhui

et al. 2024

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

The resonant accelerometer (RA) is an important part for high-performance inertial devices. And high accuracy frequency measurement is one of the key technology for RA. This paper developed a frequency meter based on Costas frequency lock loop (FLL). This structure is flexible as the analog input sinusodial voltage wave is converted into digital domain by an ADC and the frequency measurement function dealing with that voltage could be implemented only with digital circuits. The transfer function of the frequency meter is determined by the loop filter of the FLL and the closed system is always stable for PID controller. The noise in the loop is reshaped by a first order ∑-Δ modulator formed by the frequency meter loop, which relaxes the requirement for the accuracy of ADC. A test board for the frequency meter is implemented with a 24-bit ADC and FPGA, and achieves a noise of 1 mHz / Hz at low frequency band, 7 µ Hz / Hz around 50 Hz with a 40 kHz sinusodial input voltage.

show abstract

“…The process gain of the discriminator can be compensated as described in Curran [15], although the variance of the discriminator output will be increased when the process gain is adjusted. The variance of the discriminator output can be calculated according to Curran [15] as…”

Section: Implementation Of the Joint Vpllmentioning

confidence: 99%

Improvement of Carrier Phase Tracking Based on a Joint Vector Architecture

Chen

Gao

2017

International Journal of Aerospace Engineering

View full text Add to dashboard Cite

Carrier phase measurements are essential to high precision positioning. Usually, the carrier phase measurements are generated from the phase lock loop in a conventional Global Navigation Satellite System (GNSS) receiver. However there is a dilemma problem to the design of the loop parameters in a conventional tracking loop. To address this problem and improve the carrier phase tracking sensitivity, a carrier phase tracking method based on a joint vector architecture is proposed. The joint vector architecture contains a common loop based on extended Kalman filter to track the common dynamics of the different channels and the individual loops for each channel to track the satellite specific dynamics. The transfer function model of the proposed architecture is derived. The proposed method and the conventional scalar carrier phase tracking are tested with a high quality simulator. The test results indicate that carrier phase measurements of satellites start to show cycle slips using the proposed method when carrier noise ratio is equal to and below 15 dB-Hz instead of 21 dB-Hz with using the conventional phase tracking loop. Since the joint vector based tracking loops jointly process the signals of all available satellites, the potential interchannel influence between different satellites is also investigated.

show abstract

Enhancing Weak-Signal Carrier Phase Tracking in GNSS Receivers

Cited by 12 publications

References 14 publications

Kalman filtering with noncoherent integrations for Galileo E6‐B tracking

Kalman filtering with noncoherent integrations for Galileo E6‐B tracking

A frequency meter based on Costas FLL for resonant accelerometers

Improvement of Carrier Phase Tracking Based on a Joint Vector Architecture

Contact Info

Product

Resources

About