Target Tracking With Equality/Inequality Constraints Based on Trajectory Function of Time

Zhou, Jinyang; Li, Tiancheng; Wang, Xiaoxu; Zheng, Litao

doi:10.1109/lsp.2021.3090271

Cited by 12 publications

(12 citation statements)

References 19 publications

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“…Four grey categories were established, where K =

denotes “excellent”, “good”, “medium”, and “poor”. Based on the scenario [ 79 ], the following tracking methods were used for the performance evaluation where results were available for 200 Monte Carlo runs: Pseudo-observation (PS) [ 80 ]; Projection (PRO) [ 81 , 82 ]; Karush–Kuhn–Tucker (KKT) [ 83 ]; Karush–Kuhn–Tucker–Kalman filter (KKT_KF) [ 79 ]; Unconstrained Kalman filter (UKF) [ 84 ]; Trajectory function of time (T-FoT) [ 85 , 86 ]. …”

Section: Rating and Overall Performancementioning

confidence: 99%

Performance Evaluation Metrics and Approaches for Target Tracking: A Survey

Yan

et al. 2022

Sensors

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Performance evaluation (PE) plays a key role in the design and validation of any target-tracking algorithms. In fact, it is often closely related to the definition and derivation of the optimality/suboptimality of an algorithm such as that all minimum mean-squared error estimators are based on the minimization of the mean-squared error of the estimation. In this paper, we review both classic and emerging novel PE metrics and approaches in the context of estimation and target tracking. First, we briefly review the evaluation metrics commonly used for target tracking, which are classified into three groups corresponding to the most important three factors of the tracking algorithm, namely correctness, timeliness, and accuracy. Then, comprehensive evaluation (CE) approaches such as cloud barycenter evaluation, fuzzy CE, and grey clustering are reviewed. Finally, we demonstrate the use of these PE metrics and CE approaches in representative target tracking scenarios.

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“…Four grey categories were established, where K =

Section: Rating and Overall Performancementioning

confidence: 99%

Performance Evaluation Metrics and Approaches for Target Tracking: A Survey

Yan

et al. 2022

Sensors

Self Cite

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“…The problem based on discrete points is to regard the track as discrete track points and judge whether the track is correlated through two segments of track points. The continuous track association models the trajectory of the target movement through an engineering-friendly, time trajectory function (T-FoT), and then the smoothing and tracking problem becomes the estimation/fitting T-FoT problem [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Track Segment Association Method Based on Bidirectional Track Prediction and Fuzzy Analysis

Cao

Zhou

2022

Aerospace

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Due to sensor characteristics, geographical environment, electromagnetic interference, electromagnetic silence, information countermeasures, and other reasons, the phenomenon of track breakages occur in the process of aircraft track data processing. It leads to the change in target label attributes. In order to make the track segment association effect better, we studied several existing time series prediction methods, and proposed a track segment association method based on bidirectional Holt-Winters prediction and fuzzy analysis. This algorithm bidirectionally predicts and extrapolates track segments by the Holt-Winters method, and then uses the fuzzy track segment association algorithm to perform segment association and secondary association. The simulation results of this method show that the track segment association method based on Holt-Winters prediction and fuzzy analysis can effectively solve the track association problem where the target label attributes change before and after track breakage, demonstrating better association ability and robustness. Compared with the fuzzy association method without adding track prediction, our method generally improves the association accuracy by 35%.

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“…In real applications, the performance of the filter will be reduced due to uncertainties in the system's model and drift in noise parameters. Newly an alternative method based on trajectory function of time (T-FoT) is introduced in [18], [19]. The method includes state equality and inequality constraints and models target motion by curves to avoid the difficulty of process noise modeling [18].…”

Section: Introductionmentioning

confidence: 99%

“…Newly an alternative method based on trajectory function of time (T-FoT) is introduced in [18], [19]. The method includes state equality and inequality constraints and models target motion by curves to avoid the difficulty of process noise modeling [18].…”

Section: Introductionmentioning

confidence: 99%

Kalman Filtering With Linear State Equality Constraints: A Method Based on Separating Variables

Ghanbarpourasl

Zobar

2022

IEEE Access

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This article considers the minimum variance state estimation of linear dynamic systems with linear state equality constraints. The proposed method uses singular value decomposition to divide the constrained system states into deterministic and stochastic parts. The deterministic part can be independently determined through the constraint equations. The stochastic part consists of random variables which have to be determined through a filtering process. The measurement and dynamic equations of the system are also divided into stochastic and deterministic parts. In order to update the mean and covariance of the state vector's stochastic part, the measurement updating phase of the unconstrained Kalman Filter is used. Then, the deterministic part of the dynamic equation is used as a noisy measurement for the proposed method. Finally, the stochastic part of the dynamic equation is used to predict the mean and covariance of state vector's stochastic part. Simulations show that the proposed method provides superior performance compared to other methods in the literature especially for estimating the constrained unobservable states.

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Target Tracking With Equality/Inequality Constraints Based on Trajectory Function of Time

Cited by 12 publications

References 19 publications

Performance Evaluation Metrics and Approaches for Target Tracking: A Survey

Performance Evaluation Metrics and Approaches for Target Tracking: A Survey

Track Segment Association Method Based on Bidirectional Track Prediction and Fuzzy Analysis

Kalman Filtering With Linear State Equality Constraints: A Method Based on Separating Variables

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