scholarly journals The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation

Sensors ◽  
2016 ◽  
Vol 16 (7) ◽  
pp. 1103 ◽  
Author(s):  
Siwei Gao ◽  
Yanheng Liu ◽  
Jian Wang ◽  
Weiwen Deng ◽  
Heekuck Oh
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Adaptive Kalman Filter ◽  
Motion State ◽  
Vehicle Motion

scholarly journals A Study on Maneuvering Obstacle Motion State Estimation for Intelligent Vehicle Using Adaptive Kalman Filter Based on Current Statistical Model

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Bao Han ◽  
Guan Xin ◽  
Jia Xin ◽  
Liu Fan
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Rayleigh Distribution ◽  
Intelligent Vehicle ◽  
Adaptive Kalman Filter ◽  
The Road ◽  
Motion State ◽  
Ca Model ◽  
On The Road ◽  
Cv Model

The obstacle motion state estimation is an essential task in intelligent vehicle. The ASCL group has developed such a system that uses a radar and GPS/INS. When running on the road, the acceleration of the vehicle is always changing, so it is hard for constant velocity (CV) model and constant acceleration (CA) model to describe the motion state of the vehicle. This paper introduced Current Statistical (CS) model from military field, which uses the modified Rayleigh distribution to describe acceleration. The adaptive Kalman filter based on CS model was used to estimate the motion state of the target. We conducted simulation experiments and real vehicle tests, and the results showed that the estimation of position, velocity, and acceleration can be precise.

scholarly journals A Strong Tracking Mixed-Degree Cubature Kalman Filter Method and Its Application in a Quadruped Robot

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2251 ◽  
Author(s):  
Jikai Liu ◽  
Pengfei Wang ◽  
Fusheng Zha ◽  
Wei Guo ◽  
Zhenyu Jiang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Real Time ◽  
Quadruped Robot ◽  
The State ◽  
Estimation Accuracy ◽  
Robot System ◽  
Calculation Time ◽  
Motion State ◽  
Cubature Kalman Filter

The motion state of a quadruped robot in operation changes constantly. Due to the drift caused by the accumulative error, the function of the inertial measurement unit (IMU) will be limited. Even though multi-sensor fusion technology is adopted, the quadruped robot will lose its ability to respond to state changes after a while because the gain tends to be constant. To solve this problem, this paper proposes a strong tracking mixed-degree cubature Kalman filter (STMCKF) method. According to system characteristics of the quadruped robot, this method makes fusion estimation of forward kinematics and IMU track. The combination mode of traditional strong tracking cubature Kalman filter (TSTCKF) and strong tracking is improved through demonstration. A new method for calculating fading factor matrix is proposed, which reduces sampling times from three to one, saving significantly calculation time. At the same time, the state estimation accuracy is improved from the third-degree accuracy of Taylor series expansion to fifth-degree accuracy. The proposed algorithm can automatically switch the working mode according to real-time supervision of the motion state and greatly improve the state estimation performance of quadruped robot system, exhibiting strong robustness and excellent real-time performance. Finally, a comparative study of STMCKF and the extended Kalman filter (EKF) that is commonly used in quadruped robot system is carried out. Results show that the method of STMCKF has high estimation accuracy and reliable ability to cope with sudden changes, without significantly increasing the calculation time, indicating the correctness of the algorithm and its great application value in quadruped robot system.

State estimation of external neutron source driven sub-critical core using adaptive Kalman filter

2020 ◽  
Vol 141 ◽  
pp. 107313
Author(s):  
Wenhuai Li ◽  
Ruoxiang Qiu ◽  
Jiejin Cai ◽  
Peng Ding ◽  
Chengjie Duan ◽  
...  
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Neutron Source ◽  
Adaptive Kalman Filter

scholarly journals On the Relation Between Smooth Variable Structure and Adaptive Kalman Filter

2020 ◽  
Vol 6 ◽  
Author(s):  
Mark Spiller ◽  
Dirk Söffker
Keyword(s):  
Kalman Filter ◽  
Nonlinear Systems ◽  
State Estimation ◽  
Parameter Optimization ◽  
Estimation Error ◽  
Variable Structure ◽  
Uncertain Nonlinear Systems ◽  
Adaptive Kalman Filter ◽  
Model Uncertainties ◽  
Smooth Variable Structure Filter

This article is addressed to the topic of robust state estimation of uncertain nonlinear systems. In particular, the smooth variable structure filter (SVSF) and its relation to the Kalman filter is studied. An adaptive Kalman filter is obtained from the SVSF approach by replacing the gain of the original filter. Boundedness of the estimation error of the adaptive filter is proven. The SVSF approach and the adaptive Kalman filter achieve improved robustness against model uncertainties if filter parameters are suitably optimized. Therefore, a parameter optimization process is developed and the estimation performance is studied.

Maximum Likelihood-Based Fuzzy Adaptive Kalman Filter Applied to State Estimation of Permanent Magnet Synchronous Motors

2020 ◽  
pp. 23-50
Author(s):  
Miriam M. Serrepe Ranno ◽  
Francisco das Chagas de Souza ◽  
Ginalber L. O. Serra
Keyword(s):  
Kalman Filter ◽  
Maximum Likelihood ◽  
State Estimation ◽  
Permanent Magnet ◽  
Permanent Magnet Synchronous Motor ◽  
Synchronous Motor ◽  
Covariance Matrices ◽  
Adaptive Kalman Filter ◽  
The Difference ◽  
Fuzzy Adaptive

In this chapter, a novel fuzzy adaptive Kalman filter for state estimation of a permanent magnet synchronous motor is proposed. The fuzzy set theory is used as a tool to perform on-line modification of the covariance matrices, adjusting the EKF and UKF parameters according to estimation reliability of the currents in the two windings of the rotor, position, and velocity for a two-phase permanent magnet synchronous motor. Also, the methodology uses the maximum likelihood technique, where the difference between the theoretical covariance and the measured covariance is defined as an approximation considering the average of a moving estimation window. This difference is performed continually and used to dynamically update the covariance matrices, aiming to obtain an efficient estimation. The membership functions are optimized to adjust the covariance matrices so that the error variation is minimal. Simulation results illustrate the efficiency and applicability of the proposed methodology.

scholarly journals Motion State Estimation of Target Vehicle under Unknown Time-Varying Noises Based on Improved Square-Root Cubature Kalman Filter

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2620 ◽  
Author(s):  
Shiping Song ◽  
Jian Wu
Keyword(s):  
Kalman Filter ◽  
Time Varying ◽  
Square Root ◽  
Maximum Increase ◽  
Target Vehicle ◽  
Motion State ◽  
Noise Statistic ◽  
Cubature Kalman Filter ◽  
Non Linear ◽  
Vehicle Motion

In the advanced driver assistance system (ADAS), millimeter-wave radar is an important sensor to estimate the motion state of the target-vehicle. In this paper, the estimation of target-vehicle motion state includes two parts: the tracking of the target-vehicle and the identification of the target-vehicle motion state. In the unknown time-varying noise, non-linear target-vehicle tracking faces the problem of low precision. Based on the square-root cubature Kalman filter (SRCKF), the Sage–Husa noise statistic estimator and the fading memory exponential weighting method are combined to derive a time-varying noise statistic estimator for non-linear systems. A method of classifying the motion state of the target vehicle based on the time window is proposed by analyzing the transfer mechanism of the motion state of the target vehicle. The results of the vehicle test show that: (1) Compared with the Sage–Husa extended Kalman filtering (SH-EKF) and SRCKF algorithms, the maximum increase in filtering accuracy of longitudinal distance using the improved square-root cubature Kalman filter (ISRCKF) algorithm is 45.53% and 59.15%, respectively, and the maximum increase in filtering the accuracy of longitudinal speed using the ISRCKF algorithm is 23.53% and 29.09%, respectively. (2) The classification and recognition results of the target-vehicle motion state are consistent with the target-vehicle motion state.

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