In recent years, unmanned autonomous driving technology has attracted increasing attention from people, and become a research hotspot. Currently, the integration of strapdown inertial navigation system (SINS) and global navigation satellite systems (GNSSs) is the most common and effective navigation and positioning scheme for unmanned ground vehicles (UGVs) and unmanned aerial vehicles. However, this integrated system is unable to maintain a reliable positioning solution in challenging environments due to the inherent weakness of GNSS signals and the accumulation of SINS positioning errors over time. To address this issue, this paper proposes an integrated scheme based on an asynchronous Kalman filter for SINS, GNSS and two-dimensional (2D) laser Doppler velocimeter (LDV). In the proposed scheme, the SINS and 2D-LDV are tightly coupled to improve the robustness of the integrated system, and the error parameters between the 2D-LDV and the SINS are calibrated in real time during the validity of the GNSS signal. In addition, the designed asynchronous Kalman filter method evaluates the validity of the GNSS and 2D-LDV measurements in real time based on Mahalanobis distance of innovation vector and statistical property principle. Two groups of long-distance, high-mobility vehicle experiments conducted in challenging environments verify the validity of the proposed scheme. The experimental results show that the proposed SINS/GNSS/2D-LDV integrated navigation scheme has good environmental adaptability and reliability, and can maintain high horizontal and vertical positioning accuracy despite frequent GNSS signal failures, which can meet the needs of UGVs.

As the final stage of power system restoration, the critical task of load restoration is to restore the remaining load as quickly as possible. With the continuous increase of the temperature-controlled load and the proportion of electric vehicle load in the urban power grid, the complexity of the load side in the restoration process gradually increases. Therefore, based on the existing grid environment, this paper considers the sudden increase in load recovery caused by cold load pick-up and the auxiliary effect of electric vehicle discharge on load recovery during the load recovery process. From the perspective of economy, safety, and speed, this paper establishes a multi-objective function that includes the amount of load, improved weighted power flow entropy, and the number of recovered lines. The multi-objective evolutionary algorithm based on decomposition is used to optimize the constructed multi-objective load recovery model. Through the IEEE30 node system, it is verified that the method proposed in this paper can effectively establish a fast and safe load recovery plan that meets the actual grid environment.

With ultra-high velocity measurement accuracy, laser Doppler velocimeter (LDV) is promising to replace the odometer to provide vehicle velocity information in the process of land integrated navigation. This paper investigates the fast in-motion initial alignment for LDV-aided strapdown inertial navigation system (SINS). Aiming at the problem that the installation misalignment angles and scale factor error of LDV will reduce the alignment accuracy and the alignment speed, a Robust Double Gain Square-Root Unscented Quaternion Estimator (RDGSR-USQUE) method with good effect at large misalignment angle is proposed. In RDGSR-USQUE, the influence of unknown parameter error is considered in the construction of process model and measurement model and a double gain structure is proposed to improve the convergence speed based on the usually neglected posterior measurement residual information. RDGSR-USQUE method improves the defects of the traditional Unscented Quaternion Estimator (USQUE) method, such as poor noise resistance, slow convergence speed under large misalignment angle and easy to lead to non-positive definite covariance matrix. This will help to estimate and compensate unknown parameter errors while estimating attitude, so as to improve the accuracy of process model and measurement model, and finally improve the accuracy of attitude estimation. The performance of the proposed scheme is verified by vehicle field test. The results show that the proposed method has higher alignment accuracy, faster convergence speed and stronger robustness than three other compared methods, and the estimated installation misalignment angle and scale factor of LDV are satisfactory in general cases.

The tightly coupled integration of strapdown inertial navigation system (SINS) and 2-D laser Doppler velocimeter (2D-LDV) enhances system robustness by directly using raw 2D-LDV measurements, making it well-suited for land autonomous navigation. However, this approach struggles with long-term failures of individual or both 2D-LDV beams and is sensitive to violations of the vehicle’s lateral zero-velocity constraint, which can degrade performance. To address these limitations, this article proposes a novel approach involving a long short-term memory (LSTM)-assisted SINS/2D-LDV tightly coupled integration, incorporating a local outlier factor (LOF) and an adaptive filter. The LOF is introduced to evaluate the anomaly degree of the system’s measurements, while offline datasets constructed and classified from historical normal data improve detection accuracy and reduce computational load. The LSTM is used to predict the 2D-LDV measurements and the vehicle’s lateral velocity, substituting these predictions for anomalous data to mitigate their impact. Furthermore, an adaptive filter is used to adjust the measurement noise covariance matrix of the navigation filter to avoid the adverse effects of potential errors in LSTM predictions. The effectiveness of the proposed method is validated through two groups of experiments, demonstrating satisfactory performance under both normal conditions and prolonged single- or dual-beam failures in the 2D-LDV.

With its advantages of high velocity measurement accuracy and fast dynamic response, the laser Doppler velocimeter (LDVs) is expected to replace the odometer (OD) in combination with a strapdown inertial navigation system (SINS) to give a higher-precision integrated navigation system. Since a LDV has higher velocity measurement accuracy and data update frequency than an OD and Doppler velocity log, a LDV is used for the first time in this paper to aid a SINS in in-motion alignment. Considering that some approximation is used in the alignment model, the uncertainty noise of the sensors during the motion process and the unknown noise parameters during the filter process, an adaptive unscented quaternion H-infinite estimator (AUSQUHE) is proposed. The proposed AUSQUHE method has high robustness since it combines the advantages of an unscented quaternion estimator and H-infinite filter. The adaptive threshold of the H-infinite filter and the adaptive measurement noise covariance matrix are introduced to make the filter adapt to the changing environment and accelerate the convergence of errors. The performance of the proposed method is verified by a vehicle field test with a normal LDV signal and a vehicle test with the LDV signal disturbed by noise. The results show that the proposed method has higher alignment accuracy, faster convergence speed and stronger robustness than the four other compared methods.

With the advantages of high velocity measurement accuracy and fast dynamic response, the laser Doppler velocimeter (LDV) is expected to replace the odometer to be combined with a strapdown inertial navigation system (SINS) to form a higher precision integrated navigation system. However, LDV scale factor error and misalignment angles between LDV and inertial measurement unit will affect the accuracy of navigation. Considering that not all global navigation satellite system (GNSS) receivers can directly provide velocity information and current mainstream calibration methods are sensitive to the measurement noise and outliers of velocity and position information, a robust calibration method aided by GNSS is proposed in this paper, which is based on position observation. Different from current popular calibration methods, the attitude information of the GNSS/SINS integrated navigation system obtained by an adaptive Kalman filter is used to construct the observation vector together with LDV velocity outputs and GNSS position outputs in this method. The LDV scale factor error and the misalignment angle are determined by the ratio of two observation vector modulus and the Davenport’s q-method method, respectively. The accuracy and robustness of the calibration method are verified by one vehicle test with normal GNSS signals and one vehicle test with GNSS signals with outliers. And the horizontal position error of dead reckoning of the calibrated LDV/SINS integrated system are less than 0.0314% and 0.1033% of the mileage, respectively.