Gating for multitarget tracking with the Gaussian Mixture PHD and CPHD filters

2011 ◽  
Author(s):  
Davide Macagnano ◽  
Giuseppe Thadeu Freitas de Abreu
Keyword(s):  
Gaussian Mixture ◽  
Multitarget Tracking

scholarly journals Multitarget Tracking Algorithm Using Multiple GMPHD Filter Data Fusion for Sonar Networks

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3193 ◽  
Author(s):  
Xueli Sheng ◽  
Yang Chen ◽  
Longxiang Guo ◽  
Jingwei Yin ◽  
Xiao Han
Keyword(s):  
Data Fusion ◽  
Detection Probability ◽  
Gaussian Mixture ◽  
Tracking Algorithm ◽  
Statistical Characteristics ◽  
Multitarget Tracking ◽  
Fusion Algorithm ◽  
Acoustic Shadow ◽  
Multisensor Data Fusion ◽  
Lower Detection

Multitarget tracking algorithms based on sonar usually run into detection uncertainty, complex channel and more clutters, which cause lower detection probability, single sonar sensors failing to measure when the target is in an acoustic shadow zone, and computational bottlenecks. This paper proposes a novel tracking algorithm based on multisensor data fusion to solve the above problems. Firstly, under more clutters and lower detection probability condition, a Gaussian Mixture Probability Hypothesis Density (GMPHD) filter with computational advantages was used to get local estimations. Secondly, this paper provided a maximum-detection capability multitarget track fusion algorithm to deal with the problems caused by low detection probability and the target being in acoustic shadow zones. Lastly, a novel feedback algorithm was proposed to improve the GMPHD filter tracking performance, which fed the global estimations as a random finite set (RFS). In the end, the statistical characteristics of OSPA were used as evaluation criteria in Monte Carlo simulations, which showed this algorithm’s performance against those sonar tracking problems. When the detection probability is 0.7, compared with the GMPHD filter, the OSPA mean of two sensor and three sensor fusion was decrease almost by 40% and 55%, respectively. Moreover, this algorithm successfully tracks targets in acoustic shadow zones.

scholarly journals On Arithmetic Average Fusion and Its Application for Distributed Multi-Bernoulli Multitarget Tracking

2020 ◽  
Author(s):  
Tiancheng Li ◽  
Xiaoxu Wang ◽  
Yan Liang ◽  
Quan Pan
Keyword(s):  
Literature Review ◽  
Information Fusion ◽  
Large Scale ◽  
Gaussian Mixture ◽  
Multitarget Tracking ◽  
Computationally Efficient ◽  
Simple Arithmetic ◽  
Arithmetic Average ◽  
The Core ◽  
Comprehensive Literature Review

<div>Recently, the simple arithmetic averages (AA) fusion has demonstrated promising, even surprising, performance for multitarget information fusion. In this paper, we first analyze the conservativeness and Frechet mean properties of it, presenting new empirical analysis based on a comprehensive literature review. Then, we propose a target-wise fusion principle for tailoring the AA fusion to accommodate the multi-Bernoulli (MB) process, in which only significant Bernoulli components, each represented by an individual Gaussian mixture, are disseminated and fused in a Bernoulli-to-Bernoulli (B2B) manner. For internode communication, both the consensus and flooding schemes are investigated, respectively. At the core of the proposed fusion algorithms, Bernoulli components obtained at different sensors are associated via either clustering or pairwise assignment so that the MB fusion problem is decomposed to parallel B2B fusion subproblems, each resolved via exact Bernoulli-AA fusion. Two communicatively and computationally efficient cardinality consensus approaches are also presented which merely disseminate and fuse target existence probabilities among local MB filters. The accuracy and computing and communication cost of these four approaches are tested in two large scale scenarios with different sensor networks and target trajectories. </div>

scholarly journals Multisensor RFS Filters for Unknown and Changing Detection Probability

Electronics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 741
Author(s):  
Zhang ◽  
Li ◽  
Sun
Keyword(s):  
Detection Probability ◽  
A Priori ◽  
Gaussian Mixture ◽  
Multitarget Tracking ◽  
Probability Hypothesis Density ◽  
Inverse Gamma ◽  
Motion State ◽  
Performance Deterioration ◽  
Cphd Filter ◽  
Monitoring Area

The detection probability is an important parameter in multisensor multitarget tracking. The existing multisensor multi-Bernoulli (MS-MeMBer) filter and multisensor cardinalized probability hypothesis density (MS-CPHD) filter require that detection probability is a priori. However, in reality, the value of the detection probability is constantly changing due to the influence of sensors, targets, and other environmental characteristics. Therefore, to alleviate the performance deterioration caused by the mismatch of the detection probability, this paper applies the inverse gamma Gaussian mixture (IGGM) distribution to both the MS-MeMBer filter and the MS-CPHD filter. Specifically, the feature used for detection is assumed to obey the inverse gamma distribution and is statistically independent of the target’s spatial position. The feature is then integrated into the target state to iteratively estimate the target detection probability as well as the motion state. The experimental results demonstrate that the proposed methods can achieve a better filtering performance in scenarios with unknown and changing detection probability. It is also shown that the distribution of the sensors has a vital influence on the filtering accuracy, and the filters perform better when sensors are dispersed in the monitoring area.

Adaptive Gating for Multitarget Tracking With Gaussian Mixture Filters

2012 ◽  
Vol 60 (3) ◽  
pp. 1533-1538 ◽  
Author(s):  
Davide Macagnano ◽  
Giuseppe Thadeu Freitas de Abreu
Keyword(s):  
Gaussian Mixture ◽  
Multitarget Tracking ◽  
Gaussian Mixture Filters

scholarly journals Efficient Approximation of the Labeled Multi-Bernoulli Filter for Online Multitarget Tracking

2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Ping Wang ◽  
Liang Ma ◽  
Kai Xue
Keyword(s):  
Measurement Model ◽  
Gaussian Mixture ◽  
Approximation Technique ◽  
Gaussian Component ◽  
Multitarget Tracking ◽  
Target Number ◽  
Track Maintenance ◽  
Online Tracking ◽  
Tracking Time ◽  
Efficient Approximation

Online tracking time-varying number of targets is a challenging issue due to measurement noise, target birth or death, and association uncertainty, especially when target number is large. In this paper, we propose an efficient approximation of the Labeled Multi-Bernoulli (LMB) filter to perform online multitarget state estimation and track maintenance efficiently. On the basis of the original LMB filer, we propose a target posterior approximation technique to use a weighted single Gaussian component representing each individual target. Moreover, we present the Gaussian mixture implementation of the proposed efficient approximation of the LMB filter under linear, Gaussian assumptions on the target dynamic model and measurement model. Numerical results verify that our proposed efficient approximation of the LMB filer achieves accurate tracking performance and runs several times faster than the original LMB filer.

scholarly journals Tracking Ground Targets with a Road Constraint Using a GMPHD Filter

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2723 ◽  
Author(s):  
Jihong Zheng ◽  
Meiguo Gao
Keyword(s):  
Coordinate Transformation ◽  
Gaussian Mixture ◽  
State Constraint ◽  
Transformation Process ◽  
Multitarget Tracking ◽  
Probability Hypothesis Density ◽  
Target State ◽  
Cluttered Environment ◽  
Predicted Probability ◽  
Track Association

The Gaussian mixture probability hypothesis density (GMPHD) filter is applied to the problem of tracking ground moving targets in clutter due to its excellent multitarget tracking performance, such as avoiding measurement-to-track association, and its easy implementation. For the existing GMPHD-based ground target tracking algorithm (the GMPHD filter incorporating map information using a coordinate transforming method, CT-GMPHD), the predicted probability density of its target state is given in road coordinates, while its target state update needs to be performed in Cartesian ground coordinates. Although the algorithm can improve the filtering performance to a certain extent, the coordinate transformation process increases the complexity of the algorithm and reduces its computational efficiency. To address this issue, this paper proposes two non-coordinate transformation roadmap fusion algorithms: directional process noise fusion (DNP-GMPHD) and state constraint fusion (SC-GMPHD). The simulation results show that, compared with the existing algorithms, the two proposed roadmap fusion algorithms are more accurate and efficient for target estimation performance on straight and curved roads in a cluttered environment. The proposed methods are additionally applied using a cardinalized PHD (CPHD) filter and a labeled multi-Bernoulli (LMB) filter. It is found that the PHD filter performs less well than the CPHD and LMB filters, but that it is also computationally cheaper.

scholarly journals A Labeled GM-PHD Filter for Explicitly Tracking Multiple Targets

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3932
Author(s):  
Yiyue Gao ◽  
Defu Jiang ◽  
Chao Zhang ◽  
Su Guo
Keyword(s):  
Numerical Experiments ◽  
Gaussian Mixture ◽  
Multitarget Tracking ◽  
Prior Density ◽  
Negative Effects ◽  
Density Region ◽  
Probability Hypothesis Density ◽  
Time Performance ◽  
Single State ◽  
The Individual

In this study, an explicit track continuity algorithm is proposed for multitarget tracking (MTT) based on the Gaussian mixture (GM) implementation of the probability hypothesis density (PHD) filter. Trajectory maintenance and multitarget state extraction in the GM-PHD filter have not been effectively integrated to date. To address this problem, we propose an improved GM-PHD filter. In this approach, the Gaussian components are classified and labeled, and multitarget state extraction is converted into multiple single-state extractions. This provides the identity label of the individual target and can shield against the negative effects of clutter in the prior density region on the estimates, thus realizing the integration of trajectory maintenance with state extraction in the GM-PHD filter. As no additional associated procedures are required, the overall real-time performance of the proposed filter is similar to or slightly lower than that of the basic GM-PHD filter. The results of numerical experiments demonstrate that the proposed approach can achieve explicit track continuity.

scholarly journals GM-PHD Filter Combined with Track-Estimate Association and Numerical Interpolation

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Jinguang Chen ◽  
Bugao Xu ◽  
Lili Ma ◽  
Rui Sun
Keyword(s):  
False Alarm ◽  
Detection Rate ◽  
Gaussian Mixture ◽  
Multitarget Tracking ◽  
Probability Hypothesis Density ◽  
Interpolation Technique ◽  
Numerical Interpolation ◽  
Simulation Results

For the standard Gaussian mixture probability hypothesis density (GM-PHD) filter, the number of targets can be overestimated if the clutter rate is too high or underestimated if the detection rate is too low. These problems seriously affect the accuracy of multitarget tracking for the number and the value of measurements and clutters cannot be distinguished and recognized. Therefore, we proposed an improved GM-PHD filter to tackle these problems. Firstly, a track-estimate association was implemented in the filtering process to detect and remove false-alarm targets. Secondly, a numerical interpolation technique was used to compensate the missing targets caused by low detection rate. At the end of this paper, simulation results were presented to demonstrate the proposed GM-PHD algorithm is more effective in estimating the number and state of targets than the previous ones.

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