Two-Layer IMM Tracker with Variable Structure for Curvilinear Maneuvering Targets

Cai, Xibiao; Sun, Fuming

doi:10.1007/s11277-018-5473-3

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…With respect to the rapid CT motion, the filter cannot detect it on the 55th second. Although the referenced filter 25 adopts the jump Markov model, it cannot well distinguish the actual target from the random clutters based on the embedded UKF-PF algorithm. Then, the over-estimated cardinality occurs.…”

Section: Resultsmentioning

confidence: 99%

“…24 Recently, a robust and fast-tracking algorithm based on two-layer jump Markov model for curvilinear maneuvering target tracking was presented in Cai and Sun. 25 Inspired by the technical specifications for multitarget tracking, this note presents an innovative UTbased PCPHD filter with its derivation. It mainly addresses the double questions: How to design an effective CPHD filter for drawing the required particles from modified proposal distribution?…”

Section: Introductionmentioning

confidence: 99%

“…24 Recently, a robust and fast-tracking algorithm based on two-layer jump Markov model for curvilinear maneuvering target tracking was presented in Cai and Sun. 25…”

Section: Introductionmentioning

confidence: 99%

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Innovative unscented transform–based particle cardinalized probability hypothesis density filter for multi-target tracking

2019

Measurement and Control

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Multi-target tracking is widely applied in video surveillance systems. As we know, although the standard particle cardinalized probability hypothesis density filter can estimate state of targets, it is difficult to define the proposal distribution function in prediction stage. Since the robust particles cannot be effectively drawn, the actual tracking accuracy should be enhanced. In this paper, an innovative unscented transform–based particle cardinalized probability hypothesis density filter is derived. Considering the different state spaces, we use the auxiliary particle method and then draw robust particles from the modified distributions in order to estimate the position of targets. Simultaneously, we present the recursion of the optimized Kalman gain to improve the general unscented transform for the velocity estimates. Using the track label, we further integrate them in the framework of the jump Markov model. The simulation results show that the proposed filter has advances in the multi-target tracking scenes. Moreover, the experiments indicate that the filter can track mobile targets with satisfactory results.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Innovative unscented transform–based particle cardinalized probability hypothesis density filter for multi-target tracking

2019

Measurement and Control

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“…This paper deals with angles only target tracking problem in 2D and 3D. The literature presents with many variations of this tracking problem such as when the target is a curvilinear manoeuvring target [ 28 , 29 ]. However, as is common in passive sonar target tracking applications, the objective here is to estimate the states of a moving constant velocity target with the help of angles-only measurements, but corrupted with non Gaussian noise.…”

Section: Introductionmentioning

confidence: 99%

2D and 3D Angles-Only Target Tracking Based on Maximum Correntropy Kalman Filters

Urooj

Dak

Ristić

et al. 2022

Sensors

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In this paper, angles-only target tracking (AoT) problem is investigated in the non Gaussian environment. Since the conventional minimum mean square error criterion based estimators tend to give poor accuracy in the presence of large outliers or impulsive noises in measurement, a maximum correntropy criterion (MCC) based framework is presented. Accordingly, three new estimation algorithms are developed for AoT problems based on the conventional sigma point filters, termed as MC-UKF-CK, MC-NSKF-GK and MC-NSKF-CK. Here MC-NSKF-GK represents the maximum correntropy new sigma point Kalman filter realized using Gaussian kernel and MC-NSKF-CK represents realization using Cauchy kernel. Similarly, based on the unscented Kalman filter, MC-UKF-CK has been developed. The performance of all these estimators is evaluated in terms of root-mean-square error (RMSE) in position and % track loss. The simulations were carried out for 2D as well as 3D AoT scenarios and it was inferred that, the developed algorithms performed with improved estimation accuracy than the conventional ones, in the presence of non Gaussian measurement noise.

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Generalized interacting multiple model Kalman filtering algorithm for maneuvering target tracking under non-Gaussian noises

Wang,

He,

Peng

et al. 2024

ISA Transactions

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Two-Layer IMM Tracker with Variable Structure for Curvilinear Maneuvering Targets

Cited by 5 publications

References 14 publications

Innovative unscented transform–based particle cardinalized probability hypothesis density filter for multi-target tracking

Innovative unscented transform–based particle cardinalized probability hypothesis density filter for multi-target tracking

2D and 3D Angles-Only Target Tracking Based on Maximum Correntropy Kalman Filters

Generalized interacting multiple model Kalman filtering algorithm for maneuvering target tracking under non-Gaussian noises

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