Recent advances in multisensor multitarget tracking using random finite set

Da, Kai; Li, Tiancheng; Zhu, Yongfeng; Fan, Hongqi; Fu, Qiang

doi:10.1631/fitee.2000266

Cited by 58 publications

(34 citation statements)

References 119 publications

(184 reference statements)

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“…Nevertheless, one may design the fusing weights for some other purposes, e.g., in the context of seeking consensus over a peer-to-peer network [41,42], they are typically designed for ensuring fast convergence [14,15,16,17]. In the case of point estimate, a smaller variance is usually sought [43,44,14,21].…”

Section: Mixing Weightmentioning

confidence: 99%

Why Mixture?

Li¹

2021

Preprint

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From the most known Gaussian mixture to the cutting-edge multi-Bernoulli mixture of various forms, mixture offers a fundamental means to deal with uncertainties, which has led to a variety of appealing applications in the state estimation realm based on a single sensor or a sensor network. Like noise is often used to model unknown system input, one may use various hypotheses to deal with the uncertain state space model or data association. Meanwhile, consensus may be sought over the cross-correlated sensors. These all drive a need for representing the probability distribution by a mixture of properly weighted component distributions, which fuse the information gained from different models/hypotheses or from different sensors. This technical note presents information-theoretical results which answer how the averaging/mixture approach makes sense and how the fusing weights should be designed.

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Section: Mixing Weightmentioning

confidence: 99%

Why Mixture?

Li¹

2021

Preprint

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“…To substantially increase the robot's adaptability to the environment and to achieve steady climbing and walking in complex terrains and in dynamic environments, future work will include developing obfuscation and senior decision-making. It is also our aim to incorporate the robot control and multitarget tracking tasks in future research [33,34].…”

Section: Discussionmentioning

confidence: 99%

Motion Control of a Gecko-like Robot Based on a Central Pattern Generator

Han

Feixiang

et al. 2021

Sensors

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To solve the problem of the motion control of gecko-like robots in complex environments, a central pattern generator (CPG) network model of motion control was designed. The CPG oscillation model was first constructed using a sinusoidal function, resulting in stable rhythm control signals for each joint of the gecko-like robot. Subsequently, the gecko-like robot successfully walked, crossed obstacles and climbed steps in the vertical plane, based on stable rhythm control signals. Both simulations and experiments validating the feasibility of the proposed CPG motion control model are presented.

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“…The linear arithmetic average (AA) and the log-linear geometric average (GA) are widely used among average consensus. According to the previous works, AA performs better than GA when the detection probability is low, and targets are close [17], [21]. On the opposite, GA performs better than AA in high clutter density and high false alarm rate.…”

Section: Introductionmentioning

confidence: 87%

“…is the covariance generated by the state of the targets, the latter one is much bigger than the former one in the general case. It is the same context in the assumption on pseudo-Chernoff fusion (16,17), where the targets are well-separated. However, the coefficient becomes zero when fusing targets far apart, as shown in (51).…”

Section: Covariance Inflation For Gicimentioning

confidence: 99%

“…Also, it is relatively easy to manage the tracks and tolerant to the fault of the sensor. Especially, average consensus drew attention in the distributed fusion research area, which is flexible and scalable [13], [17]- [20]. The linear arithmetic average (AA) and the log-linear geometric average (GA) are widely used among average consensus.…”

Section: Introductionmentioning

confidence: 99%

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Distributed GM-CPHD Filter Based on Generalized Inverse Covariance Intersection

Park

2021

IEEE Access

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In this paper, we propose a distributed Gaussian mixture cardinalized probability hypothesis density (GM-CPHD) filter based on generalized inverse covariance intersection that fuses multiple node information effectively for multi-target tracking applications. Covariance intersection (CI) is a well-known fusion method that produces a conservative estimate of the joint covariance regardless of the actual correlation between the different nodes. Inverse covariance intersection (ICI) is the updated version to obtain fusion results that guarantee consistency and less conservative than CI. However, the ICI is not extended to multi-sensor multi-target tracking system yet. Since the ICI formula can be re-structured as naïve fusion with covariance inflation in Gaussian pdf, this method was applied to the GM-CPHD with generalization. The formula for random finite set (RFS) fusion was derived in the same way as the conventional generalized covariance intersection (GCI) based fusion. The simulation results for multi-target tracking show that the proposed algorithm has smaller optimal sub-pattern assignment (OSPA) errors than naïve fusion and the GCI-based fusions.

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Recent advances in multisensor multitarget tracking using random finite set

Cited by 58 publications

References 119 publications

Why Mixture?

Why Mixture?

Motion Control of a Gecko-like Robot Based on a Central Pattern Generator

Distributed GM-CPHD Filter Based on Generalized Inverse Covariance Intersection

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