Spaceborne global navigation satellite system reflectometry (GNSS-R) techniques have been developed for sea surface wind speed retrieval in recent years. In order to utilize both the leading edge slope (LES) and normalized bistatic radar cross-section (NBRCS), the minimum variance estimator (MVE) is used in the cyclone GNSS (CYGNSS) algorithm for wind retrieval. However, due to the high correlation of two observables, the root mean square error (RMSE) of the MVE estimated winds is not improved significantly. In this article, a new method by combining retrievals from delay-Doppler map (DDM) observables based on particle swarm optimization (PSO) is proposed. LES and NBRCS observables from CYGNSS V2.1 products are used, and then wind retrievals from them are combined by PSO. In order to validate the performance, European Center for Medium-Range Weather Forecasts (ECMWF) and cross-calibrated multi-platform (CCMP) ocean surface wind vector analysis product 10-m ocean surface wind products are used as ground truth. The results show that, when using ECMWF winds, the RMSE of MVE retrievals is 2.21 m/s, while that of PSO is 1.95 m/s: an improvement of 12%; when using CCMP winds, the RMSE of MVE retrievals is 2.15 m/s, while that of PSO is 1.92 m/s: an improvement of 11%. Therefore, we conclude that the PSO algorithm is an improvement on the state-of-the-art MVE GNSS-R-based wind speed retrieval techniques. However, the PSO-based wind retrievals show the dependence on the GPS block type and the CYGNSS satellite identifier that the MVE-based techniques suffer from.

As the global navigation satellite system (GNSS) faces the problems of signals blocked by various objects and absorbed by many materials in indoor environments, many indoor positioning methods based on smartphones’ inertial measurement unit (IMU) become increasingly applicable. Pedestrian dead reckoning (PDR) as one of the most universal methods, several algorithms with high accuracy have been proposed based on it. However, many algorithms are only applicable to scenes where pedestrians are in the "going forward" state. When the pedestrian’s motions include complex patterns such as sidestepping and walking backward, PDR will not work normally as the estimated result is far from the real trajectory. In this paper, we design a deep learning model assisted PDR algorithm to recognize the pedestrian’s complex motion patterns (going forward, walking backward, left sidestepping, right sidestepping) from the smartphones’ IMU. We firstly employ one combined model of trained convolution neural network (CNN) and long short-term memory (LSTM) neural network to determine the correction angle for different motion patterns. Then, we estimate the pedestrian’s motion direction by adding the correction angle to the attitude estimated by the inertial navigation system (INS) mechanization. Finally, the position can be computed by the estimated stride length and the corrected motion direction. Compared with different algorithms, the experimental results have proved that our algorithm can effectively process data in complex motion patterns.

Due to the low signal power, the Global Navigation Satellite System (GNSS) signal is vulnerable to interference and even cannot be captured or tracked in harsh environments. As an alternative, the Low Earth Orbit (LEO) satellite has been widely used in the navigation field due to the advantages of low cost and strong signals. It is becoming a significant component of the new combined navigation system with GNSS. The combination of an LEO Doppler signal and GNSS observables can improve the positioning accuracy and high-precision positioning convergence time of the GNSS receiver. However, the GNSS signal receiving capability cannot be improved from this data fusion level. We propose a novel assisted structure where GNSS signal acquisition and Doppler tracking are assisted by LEO Doppler positioning. The receiver uses the LEO signal to achieve Doppler positioning firstly. Then, the coarse position with the GNSS navigation messages received from LEO, as well as the estimated clock information, is used to assist in the acquisition and tracking of GNSS. In this way, the GNSS receiver’s sensitivity can get the benefit from this integrated system. The paper presents the structure of the assisted receiver and analyzes the assisted GNSS signal acquisition and carrier tracking performance in detail. Simulation experiments of this assisted structure are carried out to verify its superiority of acquisition and tracking sensitivity in comparison with standalone GNSS receivers. Theoretical analysis and experimental results show that the proposed acquisition method can achieve 90% detection probability at a carrier-to-noise ratio (C/N₀) of 15 dB-Hz, which is about 8 dB higher than the conventional acquisition method without assistance; the proposed tracking method can track weak signals of 5 dB-Hz, which is about 4 dB higher than the conventional method. Therefore, this novel LEO-assisted receiver has significantly improved weak signal acquisition and tracking sensitivity.

The phase locked loop (PLL) bandwidth suffers a dilemma on carrier phase accuracy and dynamic stress tolerance in stand-alone global navigation satellite systems (GNSS) receivers. With inertial navigation system (INS) aiding, PLLs only need to tolerate aiding information error, instead of dynamic stress. To obtain accurate carrier phase under high dynamics, INS-aided PLLs need be optimally designed to reduce the impact of aiding information error. Typical micro-electro-mechanical systems (MEMS) INS-aided PLLs are implemented and tested under high dynamics. Tests using simulation show there is a step change in the aiding information at each integer second, which deteriorates the carrier phase accuracy. An improved structure of INS-aided PLLs is proposed to eliminate the step change impact. Even when the jerk is 2000 m/s³, the tracking error of the proposed INS-aided PLL is no more than 3°. Finally, the performances of stand-alone PLLs and INS-aided PLLs are compared using field tests. When the antenna jerk is 300 m/s³, the carrier phase error from the stand-alone PLLs significantly increased, while the carrier phase error from the MEMS INS-aided PLLs almost remained the same. Therefore, the proposed INS-aided PLLs can suppress tracking errors caused by noise and dynamic stress simultaneously under high dynamics.