Random Forest-Based Optimization Algorithm for Shipborne GNSS Vector Tracking Loop

High−precision and robust localization is critical for intelligent vehicle and transportation systems, while the sensor signal loss or variance could dramatically affect the localization performance. The vehicle localization problem in an environment with Global Navigation Satellite System (GNSS) signal errors is investigated in this study. The error state Kalman filtering (ESKF) and Rauch–Tung–Striebel (RTS) smoother are integrated using the data from Inertial Measurement Unit (IMU) and GNSS sensors. A segmented RTS smoothing algorithm is proposed in order to estimate the error state, which is typically close to zero and mostly linear, which allows more accurate linearization and improved state estimation accuracy. The proposed algorithm is evaluated using simulated GNSS signals with and without signal errors. The simulation results demonstrate its superior accuracy and stability for state estimation. The designed ESKF algorithm yielded an approximate 3% improvement in long straight line and turning scenarios compared to classical EKF algorithm. Additionally, the ESKF−RTS algorithm exhibited a 10% increase in the localization accuracy compared to the ESKF algorithm. In the double turning scenarios, the ESKF algorithm resulted in an improvement of about 50% in comparison to the EKF algorithm, while the ESKF−RTS algorithm improved by about 50% compared to the ESKF algorithm. These results indicated that the proposed ESKF−RTS algorithm is more robust and provides more accurate localization.

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Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter

Commonly, there are two different types of signal tracking methods in Global Navigation Satellite System (GNSS) receivers: Scalar Tracking (ST) and Vector Tracking (VT). In ST, each tracking channel works independently and generates the measurements for Position, Velocity and Time (PVT) estimation. On the contrary, VT accomplishes the signal tracking and PVT solutions estimation together using a Kalman filter (KF). In this manner, mutual aiding between these channels are obtained, which contributes to the superior performance of VT than ST. KF is the method in the manner of weighted average of predicted state and the measurements derived state. Two drawbacks restrict the KF performance, (1) in KF, correlation between current state and all the past states is ignored, only adjacent state is included in the current state estimation through state transformation matrix; (2) linear KF might degrade the estimation due to the nonlinearity of the VT measurement model. In this brief, a graph optimization (GO) method is employed to substitute the KF to estimate the navigation solutions in VT. In the GO-VT, the measurements and the state transformation from past epochs are all regarded as constraints to optimize the states estimation. With the iterations during the optimization, the GO-VT is expected to alleviate the influence of the measurement model nonlinearity on the state estimation. A field test was carried out for assessing the performance of the GO-VT, its superior position accuracy compared with that from the KF supported that the GO could enhance the VT.

GNSS Vector Tracking Method Using Graph Optimization

Multipath error mitigation method considering NLOS signal for high-precision GNSS data processing

An efficient end-to-end EKF-SLAM architecture based on LiDAR, GNSS, and IMU data sensor fusion for autonomous ground vehicles


 In recent years, Unmanned Surface Vehicles (USVs) have increasingly been used for river monitoring and hydrological surveys. USVs rely on global navigation satellite systems (GNSS) for navigation. However, signal blocking can cause the traditional GNSS vector tracking loop to increase the code phase and carrier frequency errors, leading to higher positioning errors that do not meet USVs’ requirements. To address this problem, we propose a vector tracking method based on the minimum error entropy (MEE) in the signal tracking module. The minimum error entropy Kalman filter (MEEKF) is adopted as the loop filter to mitigate code phase and carrier frequency errors, reduce non-Gaussian noise and random errors generated by signal blocking, and enhance the positioning accuracy and robustness of USV navigation. The measurement noise covariance of the loop filter was adjusted adaptively using the signal carrier-to-noise ratio (CNR). A field experiment was conducted using a commercial GNSS receiver as reference. The results demonstrate a 19.3% improvement in positioning accuracy compared with the traditional method in an open environment. Moreover, the proposed method maintains stable operation and achieves a 79.4% improvement in positioning accuracy during signal blocking. This novel algorithm offers a new concept for USV navigation systems to cope with signal blocking.

Enhancing USVs Navigation Based on Minimum Error Entropy of GPS Vector Tracking

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Random Forest-Based Optimization Algorithm for Shipborne GNSS Vector Tracking Loop

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References

Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter

GNSS Vector Tracking Method Using Graph Optimization

Multipath error mitigation method considering NLOS signal for high-precision GNSS data processing

An efficient end-to-end EKF-SLAM architecture based on LiDAR, GNSS, and IMU data sensor fusion for autonomous ground vehicles

Enhancing USVs Navigation Based on Minimum Error Entropy of GPS Vector Tracking