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.

Applying GP-EKF and GP-SCKF for non-linear state estimation and fault detection in a continuous stirred-tank reactor system

2016 ◽  
Vol 39 (10) ◽  
pp. 1486-1496 ◽  
Author(s):  
Elham Kowsari ◽  
Behrooz Safarinejadian
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
Gaussian Process ◽  
Stirred Tank ◽  
Square Root ◽  
Continuous Stirred Tank Reactor ◽  
Tank Reactor ◽  
Cubature Kalman Filter ◽  
Non Linear ◽  
Continuous Stirred Tank

This paper proposes two novel methods for fault detection in non-linear processes. These methods apply a Gaussian process (GP) to model the underlying process, and then the extended Kalman filter (EKF) and square root cubature Kalman filter (SCKF) are used to detect faults. Accordingly, two approaches called the Gaussian process–extended Kalman filter (GP-EKF) and Gaussian process–square root cubature Kalman filter (GP-SCKF) are proposed. The most important characteristic of these proposed methods is that there is no need for an accurate model of the system. Therefore, these methods are considered non-parametric approaches of fault detection in non-linear systems. To illustrate the performance of these algorithms in fault detection, they have been used in a continuous stirred-tank reactor system (CSTR). Both proposed methods are able to detect sensor faults at an early stage.

scholarly journals Derivative‐free square‐root cubature Kalman filter for non‐linear brushless DC motors

2016 ◽  
Vol 10 (5) ◽  
pp. 419-429 ◽  
Author(s):  
Devendra Potnuru ◽  
Kumar Pakki Bharani Chandra ◽  
Ienkaran Arasaratnam ◽  
Da‐Wei Gu ◽  
Karlapudy Alice Mary ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Square Root ◽  
Brushless Dc Motors ◽  
Brushless Dc ◽  
Dc Motors ◽  
Cubature Kalman Filter ◽  
Derivative Free ◽  
Non Linear

scholarly journals Time-Varying Noise Statistic Estimator Based Adaptive Simplex Cubature Kalman Filter

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Zhaoming Li ◽  
Wenge Yang ◽  
Dan Ding
Keyword(s):  
Kalman Filter ◽  
Maximum A Posteriori ◽  
Fading Memory ◽  
Time Varying ◽  
A Posteriori ◽  
Noise Statistic ◽  
Cubature Kalman Filter ◽  
Weighted Integral ◽  
Simulation Results ◽  
Posteriori Estimation

To address the problem that filtering accuracy is reduced with the inaccurate time-varying noise statistic in conventional cubature Kalman filter, a noise statistic estimator based adaptive simplex cubature Kalman filter is put forward in this paper. First, the simplex cubature rule is adopted to approximate the intractable nonlinear Gaussian weighted integral in the filter. Secondly, a suboptimal unbiased constant noise statistic estimator is derived based on the maximum a posteriori estimation criterion. For the time-varying noise, the above estimator is modified using an exponential weighted attenuation method to realize the oblivion of stale data which results in a fading memory estimator, which has the ability to estimate the time-varying noise statistic to revise the filter online. The simulation results indicate that the proposed filter can achieve higher accuracy than conventional filters with inaccurate noise statistic.

Design of adaptive robust square-root cubature Kalman filter with noise statistic estimator

2015 ◽  
Vol 256 ◽  
pp. 352-367 ◽  
Author(s):  
Liqiang Zhao ◽  
Jianlin Wang ◽  
Tao Yu ◽  
Huan Jian ◽  
Tangjiang Liu
Keyword(s):  
Kalman Filter ◽  
Square Root ◽  
Noise Statistic ◽  
Cubature Kalman Filter

Square-Root Cubature Kalman Filter and Its Application to SLAM of an Mobile Robot

ROBOT ◽  
2013 ◽  
Vol 35 (2) ◽  
pp. 186 ◽  
Author(s):  
Yifei KANG ◽  
Yongduan SONG ◽  
Yu SONG ◽  
Deli YAN ◽  
Danyong LI
Keyword(s):  
Kalman Filter ◽  
Mobile Robot ◽  
Square Root ◽  
Cubature Kalman Filter

scholarly journals Adaptively Robust Square-root Cubature Kalman Filter Based on Amending

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Chunhui Li ◽  
Jian Ma ◽  
Yongjian Yang ◽  
Bingsong Xiao
Keyword(s):  
Kalman Filter ◽  
Square Root ◽  
Cubature Kalman Filter
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