Performance Evaluation Metrics and Approaches for Target Tracking: A Survey

Yan, Song; Hu, Zheng; Li, Tiancheng; Fan, Hongqi

doi:10.3390/s22030793

Cited by 11 publications

(2 citation statements)

References 67 publications

(92 reference statements)

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“…Because the number of elements of the real set and estimated set of the states of the targets is different, the root-mean-square type of evaluation (such as the root mean square error) is not usable. In this article, optimal sub-pattern assignment (OSPA) error is selected to evaluate the multi-target tracking performance, which is defined as follows [41]:…”

Section: Algorithm Of Vb-gmcphd Filtermentioning

confidence: 99%

Variational Bayesian cardinalized probability hypothesis density filter for robust underwater multi-target direction-of-arrival tracking with uncertain measurement noise

Zhang

Hou²,

Yang³

et al. 2023

Front. Phys.

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The direction-of-arrival (DOA) tracking of underwater targets is an important research topic in sonar signal processing. Considering that the underwater DOA tracking is a typical multi-target problem under unknown underwater environment with missing detection, false alarm, and uncertain measurement noise, a robust underwater multi-target DOA tracking method for uncertain measurement noise is proposed. First, a kinematic model of the multiple underwater targets and bearing angle measurement model with missing detection and false alarms are established. Then, the multi-target DOA tracking algorithm is derived by using the cardinalized probability hypothesis density (CPHD) filter, the performance of which largely depends on the accuracy of the parameter of measurement noise variance. In addition, the variational Bayesian approach is used to adaptively estimate the uncertain measurement of noise variance for each measurement of target in the real time of tracking. Thus, the robust underwater multi-target DOA tracking is carried out. Finally, comprehensive experimental validations and discussions are made to prove that the proposed algorithm can provide robust DOA tracking in the multi-target tracking scenario with uncertain measurement noise.

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Section: Algorithm Of Vb-gmcphd Filtermentioning

confidence: 99%

Variational Bayesian cardinalized probability hypothesis density filter for robust underwater multi-target direction-of-arrival tracking with uncertain measurement noise

Zhang

Hou²,

Yang³

et al. 2023

Front. Phys.

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show abstract

“…Different performance measures can be used to evaluate a tracking process as explained in [19], [20]. These include statistical measures such as accuracy and error measures used to evaluate the statistical performance of a model, and system performance measures such as time and space complexities used to evaluate the computational performance of a model.…”

Section: Performance Measures and Analytical Comparsionsmentioning

confidence: 99%

CoPreMo: A Collaborative Predictive Model in Time Series and its Application to Radar Target Tracking for ADAS/AD Vehicles

Ekolle,

Kohno

2023

Preprint

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Automotive driving assistant systems (ADAS) and Automated driving (AD) technologies are widely used in unmanned vehicle control along a trajectory, and just like many other technologies, they present some risks such as the risk of misdirection and collision of the vehicle with an obstacle along its trajectory. To avoid these, many tracking systems such as the radar tracking system are used to detect and track targets and trajectories of ADAS/AD vehicles. However, the performance of the tracking operation is an important factor in determining the reliability of the tracking systems and ADAS/AD technologies, especially at the edge computing level. In this study, we proposed a tracking technique using a collaborative predictive model in time series, called CoPreMo, to improve the reliability of the tracking operation of a radar system. We carried out three experiments with the model on a simulated radar system to track the range of a target with different speeds in three ADAS/AD scenarios and achieve a range tracking error of 0.21m, 0.26m, and 0.32m, better than those of the baseline models.

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Robust cardinalized probability hypothesis density filter based underwater multi-target direction-of-arrival tracking with uncertain measurement noise

Yang,

Zhang,

Hou

2024

Applied Acoustics

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Performance Evaluation Metrics and Approaches for Target Tracking: A Survey

Cited by 11 publications

References 67 publications

Variational Bayesian cardinalized probability hypothesis density filter for robust underwater multi-target direction-of-arrival tracking with uncertain measurement noise

Variational Bayesian cardinalized probability hypothesis density filter for robust underwater multi-target direction-of-arrival tracking with uncertain measurement noise

CoPreMo: A Collaborative Predictive Model in Time Series and its Application to Radar Target Tracking for ADAS/AD Vehicles

Robust cardinalized probability hypothesis density filter based underwater multi-target direction-of-arrival tracking with uncertain measurement noise

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