A consensus-based square-root cubature Kalman filter for manoeuvring target tracking in sensor networks

Zhong, Zaimin; Zhao, Enjiao; Xin, Zheng; Zhao, Xinhua

doi:10.1177/0142331220940228

Cited by 8 publications

(5 citation statements)

References 26 publications

(20 reference statements)

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“…In engineering applications, CIF with better effects is applied to locate targets [ 20 ]. CIF is also widely used in the location of passive sensor networks in the fields of aviation [ 21 ], spaceflight [ 22 ] and navigation [ 23 ]. A distributed algorithm leveraging IMM was proposed by [ 24 ] to enhance the reliability of target tracking.…”

Section: Introductionmentioning

confidence: 99%

Distributed Cubature Information Filtering Method for State Estimation in Bearing-Only Sensor Network

Chen,

Fu,

Zhang

et al. 2024

Entropy

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The problem of state estimation based on bearing-only sensors is increasingly important while existing research on distributed filtering solutions is rather limited. Therefore, this paper proposed the novel distributed cubature information filtering (DCIF) method for addressing the state estimation challenge in bearing-only sensor networks. Firstly, the system model of the bearing-only sensor network was constructed, and the observability of the system was analyzed. The sensor nodes are paired to measure relative angle information. Subsequently, the coordinated consistency theory is employed to achieve a unified state estimation of the maneuvering target. The DCIF method enhances the observability of the system, addressing the issues of large accuracy errors and divergence in traditional nonlinear filtering algorithms. Building upon the theoretical proof of consistency convergence in DCIF, four simulation experiments were conducted for comparison. These experiments validate the effectiveness and superiority of the DCIF method in bearing-only sensor networks.

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Section: Introductionmentioning

confidence: 99%

Distributed Cubature Information Filtering Method for State Estimation in Bearing-Only Sensor Network

Chen,

Fu,

Zhang

et al. 2024

Entropy

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“…Because the computer is used for practical industrial control systems to achieve the information collection in general, the effective state estimation and parameter identification are the prerequisite and guarantee to monitor the fractional-order control system under the input and output measurement information disturbed by noises. With regard to integer-order systems, a slew of methods on the state estimation and parameter identification are summarized by scholars such as the neural network method (Ali et al, 2019; Chen and Song, 2019), particle filters (Alyazidi and Mahmoud, 2019), extended Kalman filters (Battistelli and Chisci, 2016), cubature Kalman filters (Zhao et al, 2019; Zhong et al, 2020), unscented Kalman filters (Han et al, 2020; Kulikov and Kulikova, 2020), and H ∞ filters (Yan et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Full adaptive Kalman filters for nonlinear fractional-order systems containing unknown parameters and fractional-orders

Huang

Gao

2022

Transactions of the Institute of Measurement and Control

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In this study, full adaptive Kalman filters are designed for continuous-time nonlinear fractional-order systems (FOSs) containing unknown parameters and fractional-orders. First, the estimated FOS is discretized using the Grünwald–Letnikov difference method to transform the fractional-order differential equation into a difference equation. Then, in terms of the nonlinear function contained in the investigated system, the Taylor expansion formula is adopted to linearize the discretized equation. Based on the method of augmented vector, an augmented state equation is established by state equations, unknown parameter equations, and fractional-order equation to achieve the state estimation. Besides, the sigmoid function is brought to ensure that the estimation of the fractional-order is performed in a suitable range. Because the covariance matrices of noises are difficult to be measured in physical systems, we also concern the problems on the state estimation, parameter estimation, and fractional-order estimation under the cases that the covariance matrix of process noise is unknown or the covariance matrix of measurement noise is unknown. Considering that the initial value can produce an error of state estimation and parameter identification for the FOS defined under the Caputo sense, the augmented vector method is used to achieve the initial value compensation. Finally, the effectiveness of the proposed algorithms is validated by four examples.

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“…However, the first-order Taylor expansion of EKF cannot, at times, satisfy the tracking accuracy. To this end, other nonlinear filter algorithms including unscented Kalman filter (UKF) [ 18 , 19 ] and cubature Kalman filter (CKF) [ 20 ] were proposed. By adopting the unscented transformation and spherical-radial principle, UKF and CKF can achieve second-order accuracy without calculating the Jacobian matrix.…”

Section: Introductionmentioning

confidence: 99%

Noise-Adaption Extended Kalman Filter Based on Deep Deterministic Policy Gradient for Maneuvering Targets

Tang

Guo

2022

Sensors

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Although there have been numerous studies on maneuvering target tracking, few studies have focused on the distinction between unknown maneuvers and inaccurate measurements, leading to low accuracy, poor robustness, or even divergence. To this end, a noise-adaption extended Kalman filter is proposed to track maneuvering targets with multiple synchronous sensors. This filter avoids the simultaneous adjustment of the process model and measurement model without distinction. Instead, the maneuver detection based on the Dempster-Shafer evidence theory is constructed to achieve the reliable distinction between unknown maneuvers and inaccurate measurements by fusing multi-sensor information, which effectively improves the robustness of the filter. Moreover, the adaptive estimation of the process noise covariance is modeled by a Markovian decision process with a proper reward function. Deep deterministic policy gradient is designed to obtain the optimal process noise covariance by taking the innovation as the state and the compensation factor as the action. Furthermore, the recursive estimation of the measurement noise covariance is applied to modify a priori measurement noise covariance of the corresponding sensor. Finally, the fusion algorithm is developed for the global estimation. Simulation experiments are carried out in two scenarios, and simulation results illustrate the feasibility and superiority of the proposed algorithm.

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A consensus-based square-root cubature Kalman filter for manoeuvring target tracking in sensor networks

Cited by 8 publications

References 26 publications

Distributed Cubature Information Filtering Method for State Estimation in Bearing-Only Sensor Network

Distributed Cubature Information Filtering Method for State Estimation in Bearing-Only Sensor Network

Full adaptive Kalman filters for nonlinear fractional-order systems containing unknown parameters and fractional-orders

Noise-Adaption Extended Kalman Filter Based on Deep Deterministic Policy Gradient for Maneuvering Targets

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