Variance estimation and ranking of target tracking position errors modeled using Gaussian mixture distributions

Automatica ◽  
2005 ◽  
Vol 41 (8) ◽  
pp. 1433-1438 ◽  
Author(s):  
Lidija Trailović ◽  
Lucy Y. Pao
Keyword(s):  
Target Tracking ◽  
Variance Estimation ◽  
Gaussian Mixture ◽  
Mixture Distributions ◽  
Position Errors

scholarly journals Regression with Gaussian Mixture ModelsApplied to Track Fitting

Instruments ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 25
Author(s):  
Rudolf Frühwirth
Keyword(s):  
Linear Regression ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Mixture Component ◽  
Mixture Distributions ◽  
Narrow Component ◽  
Mixture Regression ◽  
Weighted Linear Regression ◽  
Position Errors ◽  
Wide Range

This note describes the application of Gaussian mixture regression to track fitting with a Gaussian mixture model of the position errors. The mixture model is assumed to have two components with identical component means. Under the premise that the association of each measurement to a specific mixture component is known, the Gaussian mixture regression is shown to have consistently better resolution than weighted linear regression with equivalent homoskedastic errors. The improvement that can be achieved is systematically investigated over a wide range of mixture distributions. The results confirm that with constant homoskedastic variance the gain is larger for larger mixture weight of the narrow component and for smaller ratio of the width of the narrow component and the width of the wide component.

A Gaussian mixture filter for target tracking with Doppler ambiguity

2012 ◽  
Author(s):  
Gongjian Zhou ◽  
Michel Pelletier ◽  
Thiagalingam Kirubarajan ◽  
Taifan Quan
Keyword(s):  
Target Tracking ◽  
Gaussian Mixture

Non-linear automatic target tracking in clutter using dynamic Gaussian mixture

2012 ◽  
Vol 6 (9) ◽  
pp. 937-944 ◽  
Author(s):  
D. Mušicki ◽  
T.L. Song ◽  
W.C. Kim ◽  
D. Nešić
Keyword(s):  
Target Tracking ◽  
Gaussian Mixture ◽  
Non Linear

scholarly journals Sensor Selection for Decentralized Large-Scale Multi-Target Tracking Network

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4115 ◽  
Author(s):  
Feng Lian ◽  
Liming Hou ◽  
Bo Wei ◽  
Chongzhao Han
Keyword(s):  
Target Tracking ◽  
Large Scale ◽  
Sequential Monte Carlo ◽  
Signal To Noise Ratio ◽  
Computational Cost ◽  
Gaussian Mixture ◽  
Descent Method ◽  
Sensor Selection ◽  
Multi Target Tracking ◽  
Finite Set

A new optimization algorithm of sensor selection is proposed in this paper for decentralized large-scale multi-target tracking (MTT) network within a labeled random finite set (RFS) framework. The method is performed based on a marginalized δ-generalized labeled multi-Bernoulli RFS. The rule of weighted Kullback-Leibler average (KLA) is used to fuse local multi-target densities. A new metric, named as the label assignment (LA) metric, is proposed to measure the distance for two labeled sets. The lower bound of LA metric based mean square error between the labeled multi-target state set and its estimate is taken as the optimized objective function of sensor selection. The proposed bound is obtained by the information inequality to RFS measurement. Then, we present the sequential Monte Carlo and Gaussian mixture implementations for the bound. Another advantage of the bound is that it provides a basis for setting the weights of KLA. The coordinate descent method is proposed to compromise the computational cost of sensor selection and the accuracy of MTT. Simulations verify the effectiveness of our method under different signal-to- noise ratio scenarios.

scholarly journals Time-Matching Random Finite Set-Based Filter for Radar Multi-Target Tracking

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4416 ◽  
Author(s):  
Defu Jiang ◽  
Ming Liu ◽  
Yiyue Gao ◽  
Yang Gao ◽  
Wei Fu ◽  
...  
Keyword(s):  
Target Tracking ◽  
Gaussian Mixture ◽  
Bayesian Filtering ◽  
Probability Hypothesis Density ◽  
Random Finite Set ◽  
Bayesian Formulation ◽  
Multi Target Tracking ◽  
Finite Set ◽  
Density Filter ◽  
Bernoulli Filter

The random finite set (RFS) approach provides an elegant Bayesian formulation of the multi-target tracking (MTT) problem without the requirement of explicit data association. In order to improve the performance of the RFS-based filter in radar MTT applications, this paper proposes a time-matching Bayesian filtering framework to deal with the problem caused by the diversity of target sampling times. Based on this framework, we develop a time-matching joint generalized labeled multi-Bernoulli filter and a time-matching probability hypothesis density filter. Simulations are performed by their Gaussian mixture implementations. The results show that the proposed approach can improve the accuracy of target state estimation, as well as the robustness.

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