scholarly journals An Improved Unscented Kalman Filter Algorithm for Radar Azimuth Mutation

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Dazhang You ◽  
Pan Liu ◽  
Wei Shang ◽  
Yepeng Zhang ◽  
Yawei Kang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Singular Value Decomposition ◽  
Unscented Kalman Filter ◽  
Tracking System ◽  
Sudden Change ◽  
Singular Value ◽  
Tracking Accuracy ◽  
Kalman Filter Algorithm ◽  
Value Decomposition ◽  
Accuracy And Stability

An improved UKF (Unscented Kalman Filter) algorithm is proposed to solve the problem of radar azimuth mutation. Since the radar azimuth angle will restart to count after each revolution of the radar, and when the aircraft just passes the abrupt angle change, the radar observation measurement will have a sudden change, which has serious consequences and is solved by the proposed novel UKF based on SVD. In order to improve the tracking accuracy and stability of the radar tracking system further, the SVD-MUKF (Singular Value Decomposition-based Memory Unscented Kalman Filter) based on multiple memory fading is constructed. Furthermore, several simulation results show that the SVD-MUKF algorithm proposed in this paper is better than the SVD-UKF (Singular Value Decomposition of Unscented Kalman Filter) algorithm and classical UKF algorithm in accuracy and stability. Last but not the least, the SVD-MUKF can achieve stable tracking of targets even in the case of angle mutation.

scholarly journals Detection of Surface Defects on Steel Strips Based on Singular Value Decomposition of Digital Image

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Qianlai Sun ◽  
Jianghui Cai ◽  
Zhiyi Sun
Keyword(s):  
Image Segmentation ◽  
Singular Value Decomposition ◽  
Digital Image ◽  
Manufacturing Industry ◽  
Surface Defects ◽  
Sudden Change ◽  
Singular Value ◽  
Steel Strips ◽  
Novel Method ◽  
Value Decomposition

Image segmentation technology has been widely used to detect the surface defects in metal industries effectively. In some fields of the manufacturing industry, the determination of defects is more concerned than the accurate location and shape of defects. However, most of current image segmentation algorithms are complex or have difficulty determining the defect. This paper presents a novel method for determining and roughly locating the surface defects of steel strips based on Singular Value Decomposition. The method has no need of image segmentation. The gray level matrix of a digital image is projected on its singular vectors obtained by Singular Value Decomposition. A defect is reflected as a sudden change on the projections. Therefore, the defects can be determined and roughly located according to the sudden changes. The experimental results suggest that this method is valid and convenient for determining the surface defects directly.

scholarly journals Improved kernelized correlation filter algorithm and application in the optoelectronic tracking system

2018 ◽  
Vol 15 (3) ◽  
pp. 172988141877658 ◽  
Author(s):  
Fengfa Yue ◽  
Xingfei Li
Keyword(s):  
Kalman Filter ◽  
Tracking System ◽  
Target Position ◽  
Adaptive Threshold ◽  
Correlation Filter ◽  
Filter Method ◽  
Tracking Accuracy ◽  
Time Performance ◽  
Kalman Filter Algorithm ◽  
Threshold Approach

In order to improve the tracking accuracy and real-time performance of the optoelectronic tracking system, an improved kernelized correlation filter approach is developed to obtain precise tracking of a maneuvering object. The proposed strategy contains merits of adaptive threshold approach, kernelized correlation filter method, and Kalman filter algorithm. The adaptive threshold approach can choose the suitable threshold in accordance with the size of the target in the image to improve the tracking performance of the kernelized correlation filter method. When the change between previous position and current position is larger than the distance threshold, Kalman filter algorithm is used to predict the target position for tracking. The tracking accuracy of the proposed algorithm is improved by updating the prediction of the target position with a trusted algorithm. The experimental results on comparison with some state-of-the-art trackers, such as kernelized correlation filter; Tracking-Learning-Detection; scale adaptive with multiple features; minimum output sum of squared error; and dual correlation filter, demonstrate that the proposed approach has the effectiveness of tracking accuracy and real-time performance in tracking the maneuvering object.

scholarly journals Underwater Bearing-only and Bearing-Doppler Target Tracking Based on Square Root Unscented Kalman Filter

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 740 ◽  
Author(s):  
Li ◽  
Zhao ◽  
Yu ◽  
Wei
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Unscented Kalman Filter ◽  
Tracking System ◽  
Positive Definiteness ◽  
Square Root ◽  
Tracking Accuracy ◽  
Underwater Target ◽  
State Observability ◽  
Passive Measurements

Underwater target tracking system can be kept covert using the bearing-only or the bearing-Doppler measurements (passive measurements), which will reduce the risk of been detected. According to the characteristics of underwater target tracking, the square root unscented Kalman filter (SRUKF) algorithm, which is based on the Bayesian theory, was applied to the underwater bearing-only and bearing-Doppler non-maneuverable target tracking problem. Aiming at the shortcomings of the unscented Kalman filter (UKF), the SRUKF uses the QR decomposition and the Cholesky factor updating, in order to avoid that the process noise covariance matrix loses its positive definiteness during the target tracking period. The SRUKF uses sigma sampling to avoid the linearization of the nonlinear bearing-only and the bearing-Doppler measurements. To ensure the target state observability in underwater target tracking, the paper uses single maneuvering observer to track the single non-maneuverable target. The simulation results show that the SRUKF has better tracking performance than the extended Kalman filter (EKF) and the UKF in tracking accuracy and stability, and the computational complexity of the SRUKF algorithm is low.

scholarly journals Nonlinear Unknown Input Observer Based on Singular Value Decomposition Aided Reduced Dimension Cubature Kalman Filter

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Wei Zhao ◽  
Huiguang Li ◽  
Liying Zou ◽  
Wenjuan Huang
Keyword(s):  
Kalman Filter ◽  
Singular Value Decomposition ◽  
Integral Form ◽  
Singular Value ◽  
Unknown Input ◽  
Unknown Input Observer ◽  
Proposed Model ◽  
Cubature Kalman Filter ◽  
Reduced Dimension ◽  
Value Decomposition

The paper presents a nonlinear unknown input observer (NUIO) based on singular value decomposition aided reduced dimension Cubature Kalman filter (SVDRDCKF) for a special class of nonlinear systems, the nonlinearity of which is only caused by part of its states. Firstly, the algorithm of general NUIO is discussed and the unknown input observer based on singular value decomposition aided Cubature Kalman filter (SVDCKF) given. Then a special nonlinear system model with unknown input is introduced. Based on the proposed model and the corresponding NUIO, the equivalent integral form with partial sampling and all sampling of the state vector in Cubature Kalman filter is analyzed. Finally the nonlinear unknown input observer based on singular value decomposition aided reduced dimension Cubature Kalman filter is obtained. Simulation results show that the proposed algorithm can meet the requirements of the system and is more important to increase the calculating efficiency a lot, although it has a decline in the accuracy of the filter.

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