Consensus-based Distributed Target Tracking in the Presence of Active False Targets

Yang, Ye; Da, Kai; Zhu, Yongfeng; Fu, Qiang

doi:10.1109/icbaie52039.2021.9389855

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…where ρ n , α n , β n and υ n are the corresponding true values. ω n is the measurement error vector whose elements are an independently distributed Gaussian distribution with zero mean, ω n ∼N(0 4×1 , R e ) [21,34]. 0 n×1 is an n×1 all-zero vector.…”

Section: N) the Location And Velocity Vectors Of The Physical Target ...mentioning

confidence: 99%

“…Location information association and velocity vector association (VVA) are performed successively to enhance the ability to discriminate the range-velocity joint deception false targets for active radar networks in [19]. Besides the above dot fusion methods for centralized fusion structure, many ECCMs are also studied for distributed fusion architecture within the target tracking process against false targets in [20][21][22]. Compared with the track fusion methods, the dot fusion methods make use of more raw measurement information.…”

Section: Introductionmentioning

confidence: 99%

“…The data-level fusion methods can be the preprocessing of the signal-level fusion methods, providing and lessening the discrimination scope of the latter. Compared with signal-level fusion methods, the data-level fusion methods are generally more concise and intuitive and easy to engineer implementation with [21,22]. Therefore, a data-level fusion method is proposed to counter the active deception jamming in this paper.…”

Section: Introductionmentioning

confidence: 99%

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Deception Velocity-Based Method to Discriminate Physical Targets and Active False Targets in a Multistatic Radar System

Li,

Guo,

Zhang

et al. 2024

Remote Sensing

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Due to the silent operation of the receiver station in a multistatic radar system, it is difficult for the jammer to generate the cooperative active deception target for the multistatic radar system. Making use of the spatial diversity property, a data level fusion method is proposed to counter the active deception jamming in this paper. According to the spatial correlation difference in physical target and active false target motion states, the deception velocity of the physical target, which is obtained by the radial velocity of each receiver, obeys the Gaussian distribution with zero mean, and the one of the active false target obeys the Gaussian distribution with the mean being its true deception velocity. Based on this fact, the active false target and physical target are discriminated by the deception velocity testing. The proposed deception velocity-based (DVB) method can keep a constant misjudgment probability for physical targets and discriminate active false targets effectively, especially in large deception velocity cases. The simulation verifies the feasibility and validity of the proposed discrimination method. Moreover, the proposed method can be combined with the location information association method to enhance the ability to discriminate the range–velocity joint deception of false targets.

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Section: N) the Location And Velocity Vectors Of The Physical Target ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deception Velocity-Based Method to Discriminate Physical Targets and Active False Targets in a Multistatic Radar System

Li,

Guo,

Zhang

et al. 2024

Remote Sensing

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show abstract

“…In ref. [21,22], single and multiple target tracking algorithms against deception jamming are proposed in radar networks respectively. In ref.…”

Section: Introductionmentioning

confidence: 99%

Consensus based target tracking against deception jamming in distributed radar networks

Yang

Zhu

et al. 2023

IET Radar Sonar & Navi

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In this study, an anti-deception jamming target tracking algorithm based on the data-level fusion is proposed in a distributed radar network. First, the consensus covariance intersection (CCI) algorithm is proposed to improve tracking performance by introducing the consensus theory to the field of covariance intersection fusion. Second, unscented Kalman filter is utilised to implement the preprocessing of distributed filtering. Then, two distributed target discrimination schemes are developed for non-collaborative and collaborative false targets respectively. For non-collaborative false targets, according to the characteristics that estimated spatial locations of the false target from different radars are relatively scattered, a false target elimination method based on nearest neighbour algorithm is proposed. For collaborative false targets, after equipped with passive radars that are less vulnerable to interference, the consensus algorithm is introduced to convey the real target information to the unconnected active radars, thus achieving an effect of global anti-deception jamming. Finally, the CCI algorithm is deployed for distributed fusion of physical target tracking, so as to improve the overall estimation performance.

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Event-Triggered Distributed Multisensor Multitarget Tracking with GM-PHD Filter

Zheng

Jin

et al. 2023

Lecture Notes in Electrical Engineering

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Consensus-based Distributed Target Tracking in the Presence of Active False Targets

Cited by 4 publications

References 12 publications

Deception Velocity-Based Method to Discriminate Physical Targets and Active False Targets in a Multistatic Radar System

Deception Velocity-Based Method to Discriminate Physical Targets and Active False Targets in a Multistatic Radar System

Consensus based target tracking against deception jamming in distributed radar networks

Event-Triggered Distributed Multisensor Multitarget Tracking with GM-PHD Filter

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