Adaptive Localisation for Unmanned Surface Vehicles Using IMU-Interacting Multiple Model

Unscented Kalman Filter (UKF) remains to be a prevalent multi-sensor fusion method in many practices, including navigational tracking for Unmanned Surface Vehicles (USVs). This paper suggests that results from UKF fusion is unsatisfactory for USVs’ relatively smooth path due to UKF’s lack of versatility. Hence, it is proposed here that by replacing the UKF with Interacting Multiple Model (IMM), estimation results will better represent USV’s movement. Furthermore, this paper proposes slight modification to the IMM in order to heighten the algorithm’s confidence in switching modes. By exploiting angular velocity information from Inertial Measurement Unit (IMU), an independent mode probability can be obtained which is then injected into the IMM. Computer simulations based on maritime operations were done to show that the proposed IMU-based IMM is able to react faster to mode changes, giving more reliable outcomes

A hybrid deep learning approach for robust multi-sensor GNSS/INS/VO fusion in urban canyons

This paper addresses the significant challenges of robust autonomous navigation in Unmanned Aerial Vehicles (UAVs) within densely populated environments. The focus is on enhancing the performance of Position, Navigation, and Timing (PNT), as specified by the International Civil Aviation Organization, in terms of accuracy, integrity, continuity, and availability. The novel contribution introduces a Robust Multi-Sensor Fusion Architecture (RMSFA) that utilizes a Bayesian-LSTM machine learning algorithm, fusing GNSS, INS, and monocular odometry. Unlike existing solutions that rely on sensor redundancies or methods such as Receiver Autonomous Integrity Monitoring (RAIM), which have limitations in performance, or adaptability to erroneous signals, the proposed system offers improvements in both positioning accuracy and integrity. Furthermore, GNSS data is preprocessed to remove NoneLine-of-Sight data (NLOS) to improve positioning accuracy. Additionally, INS data errors are corrected using a GRU-based error correction architecture to improve INS positioning and reduce drifting. The addition of these post-processing steps reduced the 95th percentile horizontal error by 97.4% and 71.5% respectively. A CNN-LSTM architecture is used to obtain a Visual Odometer (VO) from the camera sensor. The Bayesian-LSTM architecture fusion performance was then compared to a GNSS/IMU/VO EKF-GRU architecture. The comparison showed a 95th percentile error improvement in the horizontal direction of 30.1% for the BayesianLSTM. The architecture was tested in a realistic simulated environment utilizing Unreal Engine and AirSim for UAV simulation, Spirent GNSS7000 simulator for Hardware-in-the-Loop (HIL) simulation, and OKTAL-SE Sim3D to mimic the effects of multipath on GNSS signals. Overall, this work represents a step toward improving the safety and effectiveness of drone navigation by providing a more robust navigation system suitable for safety-critical situations, without the stated disadvantages in previously mentioned literatures

A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance

The Unscented Kalman filter (UKF) may suffer from performance degradation and even divergence while mismatch between the noise distribution assumed as a priori by users and the actual ones in a real nonlinear system. To resolve this problem, this paper proposes a robust adaptive UKF (RAUKF) to improve the accuracy and robustness of state estimation with uncertain noise covariance. More specifically, at each timestep, a standard UKF will be implemented first to obtain the state estimations using the new acquired measurement data. Then an online fault-detection mechanism is adopted to judge if it is necessary to update current noise covariance. If necessary, innovation-based method and residual-based method are used to calculate the estimations of current noise covariance of process and measurement, respectively. By utilizing a weighting factor, the filter will combine the last noise covariance matrices with the estimations as the new noise covariance matrices. Finally, the state estimations will be corrected according to the new noise covariance matrices and previous state estimations. Compared with the standard UKF and other adaptive UKF algorithms, RAUKF converges faster to the actual noise covariance and thus achieves a better performance in terms of robustness, accuracy, and computation for nonlinear estimation with uncertain noise covariance, which is demonstrated by the simulation results

Regional Frequency Analysis Estimates of Extreme Rainfall Events under Climate Change

Extreme rainfall events have a long history of causing large economic damages in urban areas and even loss of human life. Reliable estimates of extreme rainfall intensities for different rainfall durations are essential for the effective planning of drainage systems under climate change to balance the construction costs and potential damages caused by future extreme rainfall events. The information required for design rainfall events can be obtained through frequency analysis of extreme rainfall. However, extreme rainfall quantiles obtained from the traditional approach of frequency analysis have become increasingly unreliable under climate change. With increasing global temperatures and the uneven distribution of atmosphere moisture, the frequency and magnitude of extreme rainfall events can experience accelerated changes. Thus, urban drainage systems designed based on extreme rainfall quantiles obtained from historical records are becoming increasingly ineffective. Under the impacts of climate change, extreme rainfall events are becoming one of the most destructive natural hazards in the world. Frequency analysis of the extreme rainfall events used to estimate the probability of exceedance of extreme rainfall events of a given magnitude in the future context can generate unreliable estimates under climate change because of two issues. Firstly, there are often insufficient data records available for the quantification of extreme rainfall events of interest from a design perspective. Since extreme rainfall events are rare, there is large uncertainty in quantile estimates obtained from using only the information from the site of interest. Thus, regional frequency analysis, which expands the data records through gathering information from sites sharing similar rainfall patterns, is widely used and is applied in this research. Secondly, the traditional assumption that there is a repetitive pattern in the occurrences of extreme rainfall events has become invalid in a nonstationary environment. Since extreme rainfall patterns can be altered in the future, estimates for rainfall quantiles obtained from using frequency analysis in a historical stationary environment can be unreliable when applied for future conditions. Further research is required into applying the regional frequency analysis approach for the estimation of extreme rainfall quantiles under climate change. To provide reliable regional estimates of rainfall quantiles for different rainfall durations under climate change, this research improves regional frequency analysis through exploring the following issues: 1) An improved procedure for homogeneous group formation for historical stationary periods. Extreme rainfall events have been affected by climate change. A three-layer searching algorithm is proposed for homogeneous group formation in a stationary environment for the consideration of climate change impacts on the spatial distribution of extreme rainfall events. 2) An adjustment procedure for homogeneous group formation in the future stationary environment. Under the assumption that extreme rainfall patterns remain stationary within a 30-year period, a procedure is proposed to adjust the optimal homogeneous group formation from the previous temporal periods to reflect conditions in future 30-year periods. 3) A procedure used for rainfall quantile estimation in a future nonstationary environment. Under the assumption that the extreme rainfall series exhibit nonstationary behavior during the whole future period, a one-step forward procedure is constructed based on the unscented Kalman filter to consider the potential non-monotonic change behavior of extreme rainfall events at different return periods. In this approach, the homogeneous groups are formed using a trend centered pooling approach. The proposed methodology fills the gaps of considering climate change impacts on homogeneous group formation in both historical and future stationary environments and challenges the assumption of monotonic change behavior of extreme rainfall quantiles used in the traditional regional frequency analysis for stations exhibiting nonstationary behavior. The proposed procedures have been extensively tested using large sets of climate data in both historical and future contexts and have been shown to improve the extreme rainfall quantile estimates in both historical and future contexts