Fast Global Navigation Satellite System Research Articles

A fast and stable GNSS-LiDAR-inertial state estimator from coarse to fine by iterated error-state Kalman filter

Simultaneous localization and mapping (SLAM) aims to solve the problems of robot localization and mapping in unknown environments. Recent related research usually uses closed-loop correction or integrate GNSS (Global Navigation Satellite System) into the optimization framework to ensure the long-term system accuracy and stability at the cost of huge computational resources. To balance efficiency and accuracy, this paper presents a fast and stable GNSS-LiDAR-inertial state estimator: GNSS, LiDAR and IMU are fused to achieve state estimation from coarse to fine, thereby improving the system accuracy and stability; the overall framework based on iterated error-state Kalman filter makes our system faster than most multi-sensor fusion SLAM. We also design a fast GNSS online initialization method and a multi-layer outlier rejection mechanism for our system. In addition, we apply backward propagation for multi-sensor motion compensation to overcome the limitations of fast motion. Finally, comprehensive experiments demonstrate that our system achieves higher accuracy and computational efficiency than the state-of-the-art navigation systems on the latest challenging public datasets, and perform equally well in the real environment.

Fast GNSS acquisition algorithm based on SFFT with high noise immunity

The Global Navigation Satellite System(GNSS) has been widely used in various fields. To achieve positioning, the receiver must first lock the satellite signal. This is a complicated and expensive process that consumes a lot of resources of the receiver. For this reason, this paper proposes a new fast acquisition algorithm with High Signal-to-noise ratio(SNR) performance based on sparse fast Fourier transform(HSFFT). The algorithm first replaces the IFFT process of the traditional parallel code phase capture algorithm with inverse sparse fast Fourier transform (ISFFT) with better computing performance, and then uses linear search combined with code phase discrimination to replace the positioning loop and the estimation loop with poor noise immunity in ISFFT. Theoretical analysis and simulation results show that, compared with the existing SFFT parallel code phase capture algorithm, the calculation amount of this algorithm is reduced by 19%, and the SNR performance is improved by about 5dB. Compared with the classic FFT parallel code phase capture algorithm, the calculation amount of the algorithm in this paper is reduced by 43%, and when the capture probability is greater than 95%, the SNR performance of the two is approximately the same.

Fast GNSS satellite signal acquisition method based on multiple resamplings

A fast Global Navigation Satellite System (GNSS) satellite signal acquisition method based on resampling is presented. In contrast to traditional approaches, which perform a single-round search with a high data rate, the proposed method introduces a signal acquisition mechanism that uses data resampling. Starting from a resampled data rate slightly above the Nyquist frequency, the proposed method conducts multiple rounds of searches with an increasing sampling rate. After each round of searching, the satellites are sorted according to their relative signal strengths. By removing satellites at the bottom of each sorted list, the search space for satellite acquisition is continuously pruned. If a sufficient number of satellites are not acquired when the original data rate is reached, the method will switch to the weak-signal detection mode and use non-coherent integration for the satellites at the top on the list. The non-coherent integration process continues until either a sufficient number of satellites are acquired or the maximum number of steps is reached. The experimental results show that the proposed method can acquire the same set of satellites as traditional methods but with a considerably lower computational cost. The proposed method was implemented in a software-based GNSS receiver and can also be used in hardware-based receivers.

SDHT for Fast Detection of Weak GNSS Signals

Successful and fast Global Navigation Satellite System (GNSS) positioning in indoor environments can enable many location based services (LBS). However, fast indoor GNSS positioning has been one of the biggest challenges for GNSS receivers due to the huge computational cost. To detect weak GNSS signals in indoor environments, a GNSS receiver should perform numerous correlations with a longer coherent integration interval for a denser Doppler frequency search, which is computationally too expensive. For a fast and low computational weak GNSS signal detection, we propose the synthesized Doppler frequency hypothesis testing (SDHT) technique that, utilizing the test results of only sparse Doppler frequency hypotheses, can estimate the test results of entire Doppler frequency hypotheses with small computations. We provide theoretical performance analysis of the proposed technique and demonstrate that the proposed technique reduces the computational cost for weak GNSS signal acquisition significantly and achieves faster signal acquisition than conventional techniques.

Precise and Fast GNSS Signal Direction of Arrival Estimation

This paper proposes a precise and fast direction of arrival estimation method using Global Navigation Satellite System (GNSS) carrier phase measurements. Single-epoch, single-satellite integer cycle ambiguities are reliably resolved by making use of constraints and taking advantages of antenna arrays. The algorithm shows good robustness in cases where signal interruption or corruption occurs on some antenna elements as long as four antenna elements in a non-planar array have uncorrupted observables. The algorithm is demonstrated by field tests where antenna elements are connected to multiple receivers with an external common clock. The results indicate a high success rate of single-epoch ambiguity resolution and high direction of arrival accuracy.

Fast GNSS ambiguity resolution by ant colony optimisation

Global Navigation Satellite System (GNSS) carrier phase ambiguity resolution (AR) is the key technique to high precision positioning and navigation. Ant colony optimisation (ACO) as a stochastic meta-heuristic method solves combinatorial optimisation problems by construsting solutions iteratively using a colony of ants guided by pheromone trails and heuristic information. This paper seeks to explore the effectiveness of ACO to deal with the AR problem and closest lattice point problem. The performance of this new method is evaluated considering several simulated examples with different dimensions. The results show that the proposed algorithm can compete efficiently with other promising approaches to the problem and provide integer optimal solutions in often simulated scenarios. We hope that this paper provides a starting point for researches in applying ACO algorithm and other stochastic methods in the AR problem and other GNSS problems due to the simplicities involved in algebraic manipulation.