Axisymmetric extended target tracking using Gaussian process

2020 ◽  
Author(s):  
Qinlei Li ◽  
Liping Song
Keyword(s):  
Gaussian Process ◽  
Target Tracking ◽  
Extended Target

scholarly journals Generalized Labeled Multi-Bernoulli Extended Target Tracking Based on Gaussian Process Regression

2018 ◽  
Vol 176 ◽  
pp. 01017
Author(s):  
Luo-jia Chi ◽  
Xin-xi Feng ◽  
Lu Miao
Keyword(s):  
Gaussian Process ◽  
Target Tracking ◽  
State Space Model ◽  
Gaussian Process Regression ◽  
Tracking Performance ◽  
Probability Hypothesis Density ◽  
Target Shape ◽  
Low Snr ◽  
Extended Target ◽  
Cphd Filter

For the problems that Gamma Gaussian Inverse Wishart Cardinalized Probability Hypothesis Density (GGIW-CPHD) filter cannot accurately estimate the extended target shape and has a bad tracking performance under the condition of low SNR, a new generalized labeled multi-Bernoulli algorithm based on Gaussian process regression is proposed. The algorithm adopts the star convex to model the extended target, and realizes the online learning of the Gaussian process by constructing the state space model to complete the estimation of the extended target shape. At the same time, in the low SNR environment, the target motion state is tracked by the good tracking performance of the generalized label Bernoulli filter. Simulation results show that for any target with unknown shape, the proposed algorithm can well offer its extended shape and in the low SNR environment it can greatly improve the accuracy and stability of target tracking.

scholarly journals Extended Target Tracking and Feature Estimation for Optical Sensors Based on the Gaussian Process

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1704
Author(s):  
Haoyang Yu ◽  
Wei An ◽  
Ran Zhu
Keyword(s):  
Gaussian Process ◽  
Optical Sensors ◽  
Radial Function ◽  
Target Surface ◽  
Amplitude Distribution ◽  
Surface Shape ◽  
Feature Parameters ◽  
Extended Target ◽  
Feature Estimation ◽  
Tracking Surface

A problem of tracking surface shape-shifting extended target by using gray scale pixels on optical image is considered. The measurement with amplitude information (AI) is available to the proposed method. The target is regarded as a convex hemispheric object, and the amplitude distribution of the extended target is represented by a solid radial function. The Gaussian process (GP) is applied and the covariance function of GP is modified to fit the convex hemispheric shape. The points to be estimated on the target surface are selected reasonably in the hemispheric space at the azimuth and pitch directions. Analytical representation of the estimated target extent is provided and the recursive process is implemented by the extended Kalman filter (EKF). In order to demonstrate the algorithm’s ability of tracking complex shaped targets, a trailing target characterized by two feature parameters is simulated and the two feature parameters are extracted with the estimated points. The simulations verify the validity of the proposed method with compared to classical algorithms.

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