Multiple UAV Target Tracking Using Consensus-Based Distributed High Degree Cubature  Information Filter

Sun, Tao; Xin, Ming

doi:10.2514/6.2015-0347

Cited by 15 publications

(7 citation statements)

References 18 publications

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“…It is pointed out that CKF is a special form of CQKF in the selection of first-order Gauss-Laguerre integration points [27]. Jia and Xin [28], [31] from Columbia University -Missouri State University analyzed the volume rule that can obtain the accuracy of arbitrary order estimation, and proposed a high-order Cuban Kalman Filter (HCKF) method with more volume points. Meanwhile, They focus on the fifth-order volume Kalman filter and show the filter accuracy is similar to the Gaussian Hermititian filter (GHQF) [32].…”

Section: Introductionmentioning

confidence: 99%

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An Improved IMM Algorithm Based on STSRCKF for Maneuvering Target Tracking

Han

Huang

Lei³

et al. 2019

IEEE Access

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To overcome the IMM algorithm is easy divergence and low tracking accuracy when dealing with complex maneuvering situations, this paper proposes an improved interactive multiple model strong tracking square room cubature Kalman filter (IIMM-STSRCKF) algorithm under the idea of real-time dynamic adjustment of gain matrix and transition probability matrix. The algorithm has been improved in two aspects: on the one hand, the algorithm uses the idea of a strong tracking filter to deduce a new method for time-varying fading factor and introduce it into the square root of the state error covariance matrix of the SRCKF, which improves the tracking accuracy for strong maneuver; on the other hand, the probability difference between two consecutive time points in the IMM submodel is used to adjust the Markov probability transfer matrix to adaptively improve the switching speed of the submodel and the rationality of the allocation. By comparing with IMM-CKF algorithm by maneuvering target tracking case and results show that the IIMM-STSRCKF algorithm has better tracking performance in nonmaneuvering, weak maneuvering, and strong maneuvering cases.INDEX TERMS Complex maneuvering, IMM, SRCKF, strong tracking filter.

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Section: Introductionmentioning

confidence: 99%

“…Meanwhile, They focus on the fifth-order volume Kalman filter and show the filter accuracy is similar to the Gaussian Hermititian filter (GHQF) [32]. Although the calculation is much smaller that GHQF, but it is much larger that CKF, so it is not conducive to tracking high-speed target [28].…”

Section: Introductionmentioning

confidence: 99%

An Improved IMM Algorithm Based on STSRCKF for Maneuvering Target Tracking

Han

Huang

Lei³

et al. 2019

IEEE Access

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“…Distributed estimation technology has been commonly used in process control, signal processing, and information systems. [15][16][17][18] A subset of these efforts has generally been focused on the integration of measurements from all the sensors into a common estimate without using a centralized processor. For example, the work of Olfati-Saber 19 has contributed a great deal of work toward achieving an average consensus among distributed filters.…”

Section: Introductionmentioning

confidence: 99%

Distributed estimation for spatial rigid motion based on dual quaternions

Lee

Dai

2018

Optim Control Appl Methods

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Summary This paper proposes 2 distributed optimization algorithms for the estimation of spatial rigid motion using multiple image sensors in a connected network. The objective is to increase the estimation precision of translational and rotational motion based on dual quaternion models and the cooperation between connected sensors. The dual decomposition subgradient method and distributed Newton optimization method are applied to decompose the filtering task into a series of suboptimal problems and then solve them individually to achieve the global optimality. Our approach assumes that each sensor can communicate with its neighboring sensors to update the individual estimates. Discussion on converging speed of both methods are provided. Simulation examples are demonstrated to compare the 2 distributed algorithms with the traditional extended Kalman filter in terms of estimation accuracy and converging rate.

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“…12 In practice, a testbed is even constructed to validate the consensus algorithm. 13 Nonetheless, most of the existing literature on consensus recognizes the UAV as a point mass, 14,15 and/or it is assumed that an automatic pilot operates in the inner-loop with infinite executive capability. 16 It is easy to see that the above assumption is too idealistic to be used in practice.…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional multiple unmanned aerial vehicles formation control strategy based on second-order consensus

Zhu

Zhang

Yan

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part G:

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Considering three-dimensional formation control for multiple unmanned aerial vehicles, this paper proposes a secondorder consensus strategy by utilizing the position and velocity coordinate variables. To maintain the specified geometric configuration, a cooperative guidance algorithm and a cooperative control algorithm are proposed together to manage the position and attitude, respectively. The cooperative guidance law, which is designed as a second-order consensus algorithm, provides the desired pitch rate, heading rate and acceleration. In addition, a synchronization technology is put forward to reduce the influence of the measurement errors for the cooperative guidance law. The cooperative control law, regarding the output of the cooperative guidance law as its input, is designed by deducing the state-space expression of both the longitudinal and lateral motions. The formation stability is analyzed to give a sufficient and necessary condition. Finally, the simulations for the three-dimensional formation control demonstrate the feasibility and effectiveness of the second-order consensus strategy.

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Multiple UAV Target Tracking Using Consensus-Based Distributed High Degree Cubature Information Filter

Cited by 15 publications

References 18 publications

An Improved IMM Algorithm Based on STSRCKF for Maneuvering Target Tracking

An Improved IMM Algorithm Based on STSRCKF for Maneuvering Target Tracking

Distributed estimation for spatial rigid motion based on dual quaternions

Three-dimensional multiple unmanned aerial vehicles formation control strategy based on second-order consensus

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