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Full-bridge vehicle identification and tracking method for long-span bridges based on deep learning
Published in Airong Chen, Xin Ruan, Dan M. Frangopol, Life-Cycle Civil Engineering: Innovation, Theory and Practice, 2021
Y.L. Ma, Y.Q. Dong, D.L. Wang, Y. Pan, S.Q. Zhang
The Kalman filter algorithm is an algorithm for optimal estimation of the system state. Object tracking based on the Kalman filter algorithm has been used earlier (Comaniciu et al. 2003), and it performs poorly in general scenarios. The tracking of the vehicle trajectory on the bridge under the overlook view is relatively simple , And this method can be more simply optimized for this scenario. Therefore, this research uses an improved Kalman filter algorithm, specifically: Optimized the length extension of the vehicle due to perspective deformation.Optimized the acceleration and deceleration of the vehicle.Added verification module for vehicle driving characteristics.
Estimation from Measurements
Published in Clarence W. de Silva, Sensor Systems, 2016
Kalman filter is an “optimal” estimation method where the unknown variables (the state vector) are estimated by minimizing the error covariance of the estimate, given the measured data, as time goes to infinity (i.e., as we progress in the filter recursion). Specifically, at time step i, suppose that the actual state vector is xi and its estimated value (using the measured data) is x^i. The associated estimation error is () ei=xi−x^i
An Overview of Contact Motion Analysis in the Ocean Environment
Published in Joseph C. Hassab, Underwater Signal and Data Processing, 1989
These classes of problems are especially difficult to solve when there is a mismatch between physical processes and modeled processes or when there are large errors in observed parameters. In practice, the latter classes are more commonly encountered than the former. In analyzing the various CLMA problems likely to be encountered, several general statements hold true: Linear problems lend themselves readily to optimal estimation with resulting minimum mean-square estimation error.Nonlinearity increases the complexity and the issues involved in structuring an algorithmic estimator.Increased contact observability tends to improve the quality of an estimate and speeds estimator convergence.Redundant observations (sampling) are required to reduce the adverse effect of measurement errors.Constraints on observer/contact motion encumber the estimation process by delaying estimator convergence, lowering the quality of estimates, and upgrading the ability of the estimator to adapt to mismatches between modeled and physical processes.
Filtering based multi-sensor data fusion algorithm for a reliable unmanned surface vehicle navigation
Published in Journal of Marine Engineering & Technology, 2023
Wenwen Liu, Yuanchang Liu, Richard Bucknall
From the start of USV operation, the on-board IMU starts to measure the motions of the USV, that is, the accelerometer measures the accelerations and the gyroscope measures the angular velocity of the USV. Normally, acceleration rates provided by the IMU are along the inertial frame, which can be approximated as the body frame; whereas, other navigation information has been presented in the navigation frame. It therefore should convert the IMU data from the inertial frame to the navigation frame by using the rotation matrix: As shown in Figure 2, the heading that can be obtained from the compass is the clockwise angle referenced to North. Therefore, the anti-clockwise rotation angle from inertial frame (i-frame) to navigation frame (n-frame) is equal to the heading: It can be observed that the conversion of the frames generates the non-linearity of the system. Unscented Kalman filtering, uses an unscented transform to propagate designed Sigma points and calculates the mean of the propagated point to compute the optimal estimation of the input data. It has been used increasingly in vehicle navigation in recent years (Ma et al. 2014; Meng et al. 2016). As stated previously, when the frame rotation angle is equal to the heading of the USV, the non-linear dynamic model can then be obtained by combining Equation (6) and Equations (1) to (3) as below:
A portable measurement system for pavement surface macrotexture – a case study in China
Published in International Journal of Pavement Engineering, 2022
Yuexin Lou, Jian John Lu, Shengdi Chen, Hong Lang
After the outlier identification and correction, the elevation values also include some measurement noises caused by the detection equipment and environment. Many researches and standards adopt the sliding filter algorithm to remove these noises (Lou et al. 2018, SAC. Standard No. GB/T 26764-2011 2011). The disadvantage of this method is that the sampling points in window length are all unfiltered in the first filtering, so that the noises can not be filtered well in subsequent processing. Kalman filter algorithm (KF) is a best filtering algorithm for optimising data in time domain filtering, it characterises the change rules of signal with a linear mathematical model, and calculates the minimum mean square error (MSE) between the predicted value and the observed value in real-time by the recursive method to update the observed data, so as to realise filtering and obtain the optimal estimation of measurement value (Kalman 1960, Yang and Zhu 1999, Chen et al. 2016, Wang et al. 2017). However, the KF is limited because the pavement profile is nonlinear that caused by aggregate shape. Through the analysis of the actual data measured in this study, there is a certain linear relationship (the linear correlation coefficient is greater than 0.5) for the sampling points in a short length L (L=2 cm in this paper, it means that there are twenty sampling points in L, N=20), it can be described by a linear model , where a and b are parameter variables (Figure 5). Therefore, the least square method (Khodaparast and Khederzadeh 2017) was used to establish a dynamic linear regression model for prediction, and the limited gain method (Yang et al. 2016) was used to suppress the divergence in the process of the update. The IKF can be divided into two steps as follows.
Improved control strategy of dual star permanent magnet synchronous generator based tidal turbine system using sensorless field oriented control and direct power control techniques
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2021
Elyazid Amirouche, Koussaila Iffouzar, Azeddine Houari, Kaci Ghedamsi, Djamal Aouzellag
The Kalman filter is an optimal estimation algorithm adapted for linear systems, it’s widely used to estimate a system state that cannot be directly measured based on measurable states and the discrete time model of the considered system, while the EKF is employed for non linear systems.