A Kalman tracker with a turning acceleration estimator

Guo

et al. 2019

Sensors

For the laser tracking and positioning system of a moving target using a four-quadrant detector, the accuracy of laser spot position detection has a serious impact on the tracking performance of the system. For moving target tracking, the traditional spot position detection method of a four-quadrant detector cannot give better consideration to both detection accuracy and operation speed. In view of this, an improved method based on piecewise low-order polynomial least squares fitting and a Kalman filter is proposed. Firstly, the tracking and positioning mathematical model of the system is created, and the experimental device is established. Then, the shortcomings of traditional methods are analyzed, and the improved method and the real-time tracking and positioning algorithm of the system are studied. Finally, through the experiment, the system operation effects are compared and analyzed before and after the improvement. The experimental results of system dynamic tracking show that, the least squares fitting of the experimental data using a 5-segment and quadratic polynomial can achieve better results. By using the improved method, the maximum tracking distance of a moving object is increased from 12 m to more than 30 m. At a distance of 7.5 m, the maximum tracking speed can reach 2.11 m/s, and the root mean square error (RMSE) of the position is less than 4.59 mm. At 15.5 m, the maximum tracking speed is 2.04 m/s and the RMSE is less than 5.42 mm. Additionally, at 23.5 m, it is 1.13 m/s and 5.71 mm.

Section: Improved Methods For Spot Position Detectionmentioning

confidence: 99%

An Improved Method for Spot Position Detection of a Laser Tracking and Positioning System Based on a Four-Quadrant Detector

Guo

et al. 2019

Sensors

“…In linear systems, the Kalman filter is the optimal filter [ 1 ]. With the development of computer technology, the calculation requirements and complexity of Kalman filtering no longer become obstacles to its application [ 2 ]. At present, the Kalman filtering theory has been widely used in tracking, navigation, guidance, and other areas [ 3 , 4 , 5 , 6 , 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Fading Factor and Updated Monitoring Strategy Adaptive Kalman Filter-Based Variational Bayesian

Shan

Zhou

Yang

et al. 2020

Sensors

Aiming at the problem that the performance of adaptive Kalman filter estimation will be affected when the statistical characteristics of the process and measurement of the noise matrices are inaccurate and time-varying in the linear Gaussian state-space model, an algorithm of multi-fading factor and an updated monitoring strategy adaptive Kalman filter-based variational Bayesian is proposed. Inverse Wishart distribution is selected as the measurement noise model and the system state vector and measurement noise covariance matrix are estimated with the variational Bayesian method. The process noise covariance matrix is estimated by the maximum a posteriori principle, and the updated monitoring strategy with adjustment factors is used to maintain the positive semi-definite of the updated matrix. The above optimal estimation results are introduced as time-varying parameters into the multiple fading factors to improve the estimation accuracy of the one-step state predicted covariance matrix. The application of the proposed algorithm in target tracking is simulated. The results show that compared with the current filters, the proposed filtering algorithm has better accuracy and convergence performance, and realizes the simultaneous estimation of inaccurate time-varying process and measurement noise covariance matrices.

Electron. Comm. Jpn. Pt. I

“…On the other hand, a method that estimates the acceleration from the target's trajectory is considered to be effective. One method [9] that searches for the centripetal acceleration of the circle when the trajectory of the tracking target is regarded as an arc has been proposed as an estimation method that directly uses past position information. Based on its properties, this method is effective since the correct arc is easily formed when the sampling interval is long, but may have drawbacks because discrepancies easily arise in the arc structure for a short sampling interval.…”

Section: Introductionmentioning

confidence: 99%

Maneuver target tracking with an acceleration estimator using target past positions

Hashirao

Kawase

Sasase

2002

Self Cite

SUMMARYTarget tracking filters with a low computational load are desired in air traffic control systems in order to simultaneously search and track multiple aircraft. The applications of the α-β filter and the Kalman filter have been widely studied. Although excellent tracking accuracy can be obtained for linear motion by the α-β filter, the tracking accuracy degrades for a maneuvering target, the difference between the predicted position and the actual target position becomes large, and mistracking where the target is lost may occur. While there has been research on filters that estimate the acceleration, these filters could only be used when there is little maneuvering and the computational load is huge. In this research, we propose a method that adds an acceleration term during turning motion as the tracking filter for maneuvering motion and not just linear motion and estimates the turning acceleration from past estimated positions of the target. We also reduce the tracking errors for a maneuvering target. We use simple trajectories of the target in computer simulations and compare the proposed method to conventional filters to demonstrate its effectiveness. And we evaluate the performance of the proposed acceleration estimation method.