scholarly journals Convergence Results for the Gaussian Mixture Implementation of the Extended-Target PHD Filter and Its Extended Kalman Filtering Approximation

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Feng Lian ◽  
Chongzhao Han ◽  
Jing Liu ◽  
Hui Chen
Keyword(s):  
Uniform Convergence ◽  
Kalman Filtering ◽  
Gaussian Mixture ◽  
Extended Kalman Filtering ◽  
Probability Hypothesis Density ◽  
Target Probability ◽  
Convergence Results ◽  
Extended Target ◽  
Nonlinear Condition

The convergence of the Gaussian mixture extended-target probability hypothesis density (GM-EPHD) filter and its extended Kalman (EK) filtering approximation in mildly nonlinear condition, namely, the EK-GM-EPHD filter, is studied here. This paper proves that both the GM-EPHD filter and the EK-GM-EPHD filter converge uniformly to the true EPHD filter. The significance of this paper is in theory to present the convergence results of the GM-EPHD and EK-GM-EPHD filters and the conditions under which the two filters satisfy uniform convergence.

scholarly journals Adaptive Target Birth Intensity for ET-PHD Filter

2018 ◽  
Vol 176 ◽  
pp. 03010
Author(s):  
Lu Miao ◽  
Xin-xi Feng ◽  
Luo-jia Chi
Keyword(s):  
Target Tracking ◽  
Gaussian Mixture ◽  
Tracking Algorithm ◽  
Tracking Problem ◽  
Probability Hypothesis Density ◽  
Probability Hypothesis Density Filter ◽  
Target Probability ◽  
Extended Target ◽  
Density Filter ◽  
Simulation Results

An adaptive tracking algorithm based on Extended target Probability Hypothesis Density (ETPHD) filter is proposed for extended target tracking problem with priori unknown target birth intensity.The algorithm is implemented by gaussian mixture, where the target birth intensity is generated by measurement-driven, and the persistent and the newborn targets intensity are respectively predicted and updated. The simulation results show that the proposed algorithm improves the performance of the probability hypothesis density filter in the extended target tracking.

scholarly journals Two Measurement Set Partitioning Algorithms for the Extended Target Probability Hypothesis Density Filter

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2665 ◽  
Author(s):  
Yulan Han ◽  
Chongzhao Han
Keyword(s):  
Mahalanobis Distance ◽  
Nearest Neighbors ◽  
Set Partitioning ◽  
Measurement Information ◽  
Probability Hypothesis Density ◽  
Dbscan Algorithm ◽  
Target Probability ◽  
Extended Target ◽  
Density Filter ◽  
Partitioning Algorithms

The extended target probability hypothesis density (ET-PHD) filter cannot work well if the density of measurements varies from target to target, which is based on the measurement set partitioning algorithms employing the Mahalanobis distance between measurements. To tackle the problem, two measurement set partitioning approaches, the shared nearest neighbors similarity partitioning (SNNSP) and SNN density partitioning (SNNDP), are proposed in this paper. In SNNSP, the shared nearest neighbors (SNN) similarity, which incorporates the neighboring measurement information, is introduced to DP instead of the Mahalanobis distance between measurements. Furthermore, the SNNDP is developed by combining the DBSCAN algorithm with the SNN similarity together to enhance the reliability of partitions. Simulation results show that the ET-PHD filters based on the two proposed partitioning algorithms can achieve better tracking performance with less computation than the compared algorithms.

scholarly journals Maintaining Track Continuity for Extended Targets Using Gaussian-Mixture Probability Hypothesis Density Filter

2015 ◽  
Vol 2015 ◽  
pp. 1-16
Author(s):  
Yulan Han ◽  
Hongyan Zhu ◽  
ChongZhao Han
Keyword(s):  
Gaussian Mixture ◽  
Intensity Function ◽  
Probability Hypothesis Density ◽  
Management Scheme ◽  
Extended Target ◽  
Close Proximity ◽  
Density Filter ◽  
Track Management ◽  
Simulation Results ◽  
Track Initiation

A multiextended-target tracker based on the extended target Gaussian-mixture probability hypothesis density (ET-GMPHD) filter, which can provide the tracks of the extended targets, is proposed to maintain the track continuity for the extended targets. To identify the extended targets, each individual Gaussian term of the mixture representing the posterior intensity function will be assigned a label, which is evolved through time. Then a track management scheme, including track initiation, track confirmation, track propagation, and termination, is developed to form the tracks for the extended targets. Furthermore, to improve the performance of the extended target tracker we also propose a mixture partitioning algorithm for resolving the identities of the extended targets in close proximity. The simulation results show that our proposed tracker achieves the less error of the position estimates and decreases the probability of incorrect label assignments from 0.6 to 0.25.

scholarly journals An Improved GM-PHD Approach for Multi Extended Target Tracking with Square-root Cubature Information Kalman Filter

2021 ◽  
Author(s):  
Zhe Liu ◽  
Fengbao Yang ◽  
Linna Ji ◽  
Xiqiang Qu
Keyword(s):  
Target Tracking ◽  
Linear Models ◽  
Nonlinear Models ◽  
Gaussian Mixture ◽  
Tracking Performance ◽  
Square Root ◽  
Probability Hypothesis Density ◽  
Information Filter ◽  
Extended Target ◽  
The Mean

Abstract The conventional target tracking approaches are presented under the assumption of the point target. In multi extended target scenarios, the tracking performance of these approaches may be greatly decreased. The the Gaussian mixture (GM) implementation of the probability hypothesis density (PHD) (named as the ET-GM-PHD approach) has been presented for applying the GM-PHD approach into extended target tracking. However, it has been proposed under the linear models. In fact, most of targets are moving with nonlinear models. Thus, we, in this paper, present a square-root cubature information filter (SCIF) based ET-GM-PHD approach. To be more specific, we, first, employ the cubature points to predict the mean and the square-root factor of covariance. Then, the information forms of the mean and square-root of covariance has been used to update the mean and covariance of GM component. Meanwhile, we integrate the gating method into our method for saving computational complexity. Owing to the significant tracking performance of the SCIF method, our approach can estimate states and number of multi extended targets in nonlinear scenarios. In addition, we also propose an observation driven method to initiate the birth intensity. As for our method, the conditional probability has been adopted to describe the association between the target and its corresponding observations. With such a probability, the most possible partition, where the estimated targets belong to, can be approximated. Thus, the birth intensity can be estimated by removing the cells associated with the estimated targets. Since we use the estimated targets to initiate the birth intensity, our approach can initiate the birth intensity adaptively. The simulation results prove the effectiveness of our approach.

Sign in / Sign up

Export Citation Format

Share Document