Open-source MATLAB code for GPS vector tracking on a software-defined receiver

Xu, Bing; Hsu, Li‐Ta

doi:10.1007/s10291-019-0839-x

Cited by 58 publications

(54 citation statements)

References 18 publications

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“…For example, the previous work of the authors has proven that vector tracking loop (VTL) has a different response to the MP/NLOS signal compared with the scalar tracking loop (STL) used in this paper [36]. This difference provides an opportunity to detect MP/NLOS more efficiently [38,44]. Therefore, correlator-level features extracted from VTL may further improve the signal classification rate.…”

Section: Discussionmentioning

confidence: 92%

“…The NSL Stereo front-end with an active antenna, Allystar AGR6303, was used to collect the raw GPS IF data for postprocessing. An open-sourced GPS L1 SDR that is able to output pseudorange measurements was used in this paper [38], which was modified with the implementation of multiple correlators. The multicorrelator outputs were used for correlator-level LOS/MP/NLOS classification.…”

Section: Resultsmentioning

confidence: 99%

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Intelligent GPS L1 LOS/Multipath/NLOS Classifiers Based on Correlator-, RINEX- and NMEA-Level Measurements

Jia

Luo

et al. 2019

Remote Sensing

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This paper proposes to use a correlator-level global positioning system (GPS) line-of-sight/multipath/non-line-of-sight (LOS/MP/NLOS) signal reception classifier to improve positioning performance in an urban environment. Conventional LOS/MP/NLOS classifiers, referred to as national marine electronics association (NMEA)-level and receiver independent exchange format (RINEX)-level classifiers, are usually performed using attributes extracted from basic observables or measurements such as received signal strength, satellite elevation angle, code pseudorange, etc. The NMEA/RINEX-level classification rate is limited because the complex signal propagation in urban environment is not fully manifested in these end attributes. In this paper, LOS/MP/NLOS features were extracted at the baseband signal processing stage. Multicorrelator is implemented in a GPS software-defined receiver (SDR) and exploited to generate features from the autocorrelation function (ACF). A robust LOS/MP/NLOS classifier using a supervised machine learning algorithm, support vector machine (SVM), is then trained. It is also proposed that the Skymask and code pseudorange double difference observable are used to label the real signal type. Raw GPS intermediate frequency data were collected in urban areas in Hong Kong and were postprocessed using a self-developed SDR, which can easily output correlator-level LOS/MP/NLOS features. The SDR measurements were saved in the file with the format of NMEA and RINEX. A fair comparison among NMEA-, RINEX-, and correlator-level classifiers was then carried out on a common ground. Results show that the correlator-level classifier improves the metric of F1 score by about 25% over the conventional NMEA-and RINEX-level classifiers for testing data collected at different places to that of training data. In addition to this finding, correlator-level classifier is found to be more feasible in practical applications due to its less dependency on surrounding scenarios compared with the NMEA/RINEX-level classifiers.The former, like the conventional GNSS positioning algorithm, still makes use of pseudorange measurements, meanwhile it is aided with the information of satellite visibility or additional path delay of reflected signals obtained using 3D city models. For shadow matching, the basic idea is to compare the measured signal availability and strength with predictions obtained using 3D city models over a range of candidate positions. For detailed implementation of 3DMA GNSS positioning, readers are referred to [5][6][7][8][9][10]. For the 3DMA GNSS positioning, the accuracy of line-of-sight/non-line-of-sight (LOS/NLOS) signal reception classification directly affects its performance [5]. In addition to the 3DMA GNSS positioning, conventional ranging-based least-squares GNSS positioning can also benefit from an accurate signal classification by excluding or down-weighting the identified multipath (MP)/NLOS measurements [11].Various approaches to classifying LOS/NLOS signal have been proposed. As mentioned above, the 3D ...

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Section: Discussionmentioning

confidence: 92%

Section: Resultsmentioning

confidence: 99%

Intelligent GPS L1 LOS/Multipath/NLOS Classifiers Based on Correlator-, RINEX- and NMEA-Level Measurements

Jia

Luo

et al. 2019

Remote Sensing

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“…After successful execution of the primary information processing in NR it is not difficult to carry out secondary information processing which is the solution of navigation task. It is based on the received navigation message and the NS parameters [14]. We can assume that the probability of a successful navigation task solution depends significantly on the probability of successful primary information processing:…”

Section: Methods Developingmentioning

confidence: 99%

Improved Method for Estimating Noise Immunity of Global Navigation Satellite Systems

Жук¹,

Orel²

2019

Proceedings of the 7th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2019)

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The task of wireless data transmission system in electronic conflict is to ensure the secrecy of signal parameters to provide the availability of radio signal in conditions of interference. The task of interference transmitter is to suppress the radio channel with interference. Interference transmitter must explore the parameters of the radio signal to form interference which will be the most effective for its suppression. Increasing structural secrecy of radio signal is aimed at increasing a priori uncertainty of its parameters, and is achieved by manipulating them. An improved method for estimating noise immunity of global navigation satellite systems is proposed in the article. The method allows taking into account the dynamics of the conflict between the navigation system and the interference transmitter: changing signal parameters in time and exploring them. The estimation of noise immunity of navigation signals of global navigation satellite systems allowed to conclude that the use of an increased number of binary quasi-orthogonal code sequences sets will provide high noise immunity of the global navigation satellite system for 15 years.

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“…The vector receiver uses the traditional acquisition and scalar tracking algorithm to decode the ephemeris and to obtain the first navigation solution, which is used to complete the necessary initialization work [13,14]. In vector mode, the receiver uses the predicted position and velocity and the ephemeris to calculate the control command of the numerically controlled oscillator (NCO) to generate the local replica code and carrier.…”

Section: Vector-tracking Loopsmentioning

confidence: 99%

C/N0 Estimator Based on the Adaptive Strong Tracking Kalman Filter for GNSS Vector Receivers

Liu

Zheng

et al. 2020

Sensors

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The carrier-to-noise ratio (C/N0) is an important indicator of the signal quality of global navigation satellite system receivers. In a vector receiver, estimating C/N0 using a signal amplitude Kalman filter is a typical method. However, the classical Kalman filter (CKF) has a significant estimation delay if the signal power levels change suddenly. In a weak signal environment, it is difficult to estimate the measurement noise for CKF correctly. This article proposes the use of the adaptive strong tracking Kalman filter (ASTKF) to estimate C/N0. The estimator was evaluated via simulation experiments and a static field test. The results demonstrate that the ASTKF C/N0 estimator can track abrupt variations in C/N0 and the method can estimate the weak signal C/N0 correctly. When C/N0 jumps, the ASTKF estimation method shows a significant advantage over the adaptive Kalman filter (AKF) method in terms of the time delay. Compared with the popular C/N0 algorithms, the narrow-to-wideband power ratio (NWPR) method, and the variance summing method (VSM), the ASTKF C/N0 estimator can adopt a shorter averaging time, which reduces the hysteresis of the estimation results.

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Open-source MATLAB code for GPS vector tracking on a software-defined receiver

Cited by 58 publications

References 18 publications

Intelligent GPS L1 LOS/Multipath/NLOS Classifiers Based on Correlator-, RINEX- and NMEA-Level Measurements

Intelligent GPS L1 LOS/Multipath/NLOS Classifiers Based on Correlator-, RINEX- and NMEA-Level Measurements

Improved Method for Estimating Noise Immunity of Global Navigation Satellite Systems

C/N0 Estimator Based on the Adaptive Strong Tracking Kalman Filter for GNSS Vector Receivers

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