Consensus‐based unscented Kalman filtering over sensor networks with communication protocols

Sheng, Li; Huai, Wuxiang; Niu, Yuguang; Gao, Ming

doi:10.1002/rnc.5614

Cited by 12 publications

(5 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The grid spacing in both Xand Y -axes are assumed to be 1, and the element numbers for these two axes are 5 and 7, respectively. Then, we can obtain the center of mesh and the grid points with (15) and (18), respectively. Naturally, the state space is partitioned into 24 rectangular meshes.…”

Section: Mesh-based Approximation Of Pdfmentioning

confidence: 99%

See 1 more Smart Citation

Mesh-Based Consensus Distributed Particle Filtering for Sensor Networks

Liu

Coombes

Liu

2023

IEEE Trans. on Signal and Inf. Process. over Networks

View full text Add to dashboard Cite

Section: Mesh-based Approximation Of Pdfmentioning

confidence: 99%

“…via the information exchange and the consensus process. Thus, consensus-based filtering methods have been proposed in different frameworks such as the minimum variance filter [17], [18] and the robust filter [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Mesh-Based Consensus Distributed Particle Filtering for Sensor Networks

Liu

Coombes

Liu

2023

IEEE Trans. on Signal and Inf. Process. over Networks

View full text Add to dashboard Cite

“…Over the past decade, cooperative multiagent systems have attracted extensive interest owing to their potential applications, and they have examined various aspects such as multiple robots, 1,2 wireless networks 3,4 and aircraft formations 5–8 . The containment consensus is a further extension of leader‐following consensus 9,10 .…”

Section: Introductionmentioning

confidence: 99%

Distributed observer‐based containment control for unknown time‐varying P‐normal nonlinear multiagent systems

Zhang,

Liu,

Hua

et al. 2024

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

This work investigates the containment control for unknown time‐varying p‐normal (TVP) nonlinear multiagent systems (MASs) under directed topology. First, by innovatively constructing the dynamic update law in the distributed observer design process, the corresponding virtual leader trajectory can be accurately estimated at an exponential rate. Subsequently, the containment problem of MASs is transformed into a tracking problem through appropriate state transformation. Then, based on backstepping and power integrator techniques, a recursive algorithm is proposed to construct the adaptive controller to guarantee that the followers are driven into the convex hull consisting of dynamic leaders and the closed‐loop system is stable. Finally, the effectiveness of the containment control scheme is verified through a numerical simulation and a practical example of single‐link manipulators.

show abstract

“…In addition, many existing fusion algorithms require a fusion center to obtain dynamic estimations of the target. Therefore, in recent years, scholars have also proposed Kalman consistency filtering methods such as the Distributed Extended Kalman Consensus Filter (DEKCF) [32] and Distributed Unscented Consensus Kalman Filter (DUKCF) [33,34] algorithms. These filters accomplish distributed target dynamic estimation through local information fusion among adjacent sensors.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Kalman Consensus Filtering for Connected Vehicle Speed Estimation in a Multi-Sensor Network

Ji,

Mei,

Wang

et al. 2023

Symmetry

View full text Add to dashboard Cite

The autonomous traffic system has imposed higher requirements on the speed estimation of connected vehicles, where the speed of connected vehicles, as one of the control conditions for refined traffic management, plays a crucial role in the evaluation and optimization of network performance. In this paper, we propose a multi-source speed measurement sensor network consensus filtering (MSCF) algorithm based on information weight for the problem of optimal speed consistency estimation for connected vehicles. Specifically, we first utilize dynamic linearization techniques and data-driven parameter identification algorithms to handle the derived state equations of connected vehicles. We then establish observation models for four different types of sensors and construct distributed direct and indirect measurement models by dynamically adjusting the information weights of sensor nodes. Following this, we combine the Kalman consistency filtering algorithm to derive the speed state estimation update rate and design a new state estimator to achieve the optimal consistent convergence estimation for connected vehicles’ speed. The MSCF algorithm can solve the problem of consistency filtering for noisy sensor data under observation- and communication-constrained conditions, enabling each sensor node to obtain a consistent convergence estimation value for the speed of the connected vehicle. The convergence of the algorithm is proved using the Lyapunov function. Through numerical simulation, the results are verified, indicating that compared to existing methods, this method can achieve a higher precision speed estimation effect.

show abstract

Consensus‐based unscented Kalman filtering over sensor networks with communication protocols

Cited by 12 publications

References 35 publications

Mesh-Based Consensus Distributed Particle Filtering for Sensor Networks

Mesh-Based Consensus Distributed Particle Filtering for Sensor Networks

Distributed observer‐based containment control for unknown time‐varying P‐normal nonlinear multiagent systems

Data-Driven Kalman Consensus Filtering for Connected Vehicle Speed Estimation in a Multi-Sensor Network

Contact Info

Product

Resources

About