The high-degree cubature Kalman filter

Jia, Bin; Xin, Ming; Cheng, Yang

doi:10.1109/cdc.2012.6426413

Cited by 93 publications

(179 citation statements)

References 9 publications

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“…The accuracy and efficiency of the target location have witnessed significant improvement in recent years due to the improvement of advanced filtering techniques [4][5][6] and UAV (unmanned aerial vehicle) platform. Many researchers focus on location problems with static target or tracking problems under conditions of observer's specific trajectories, while the target could be located more accurately if the observer implements maneuverings based on certain rules [7].…”

Section: Introductionmentioning

confidence: 99%

Optimal Maneuver Strategy of Observer for Bearing-Only Tracking in Threat Environment

Chen

et al. 2018

International Journal of Aerospace Engineering

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The optimal maneuver of observer for bearing-only tracking (BOT) in a threat environment is a complex problem which involves nonlinear filtering, threat avoidance, and optimal maneuver strategy. Under comprehensive consideration, the reward function comprised of the lower bound on detFIM and threat cost was established; the finite-horizon MDP principle was applied to obtain the optimal strategy. The quantization method was used to discretize the BOT process and calculate the transition matrix of Markov chain; to achieve quantization in the beginning of each period, CKF was applied to provide the initial state estimate and the corresponding error covariance. The numerical simulations illustrated the applicability and superior performance for static and dynamic target tracking in several scenarios in the threat environment.

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Section: Introductionmentioning

confidence: 99%

Optimal Maneuver Strategy of Observer for Bearing-Only Tracking in Threat Environment

Chen

et al. 2018

International Journal of Aerospace Engineering

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“…Over the past decades, the filtering problem of nonlinear systems has been an active field of research on account of its widespread applications, for example, dynamic target tracking [1,2], signal processing [3], and integrated navigation [4]. As a general rule, in view of the fact that the Bayesian estimator of nonlinear systems in minimum mean square error (MMSE) sense is usually faced with intractable computation [5], consequently using approximation methods to design cost-efficient estimators has received much attention.…”

Section: Introductionmentioning

confidence: 99%

Strong Tracking Filter for Nonlinear Systems with Randomly Delayed Measurements and Correlated Noises

Yang

Meng

et al. 2018

Mathematical Problems in Engineering

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This paper proposes a novel strong tracking filter (STF), which is suitable for dealing with the filtering problem of nonlinear systems when the following cases occur: that is, the constructed model does not match the actual system, the measurements have the onestep random delay, and the process and measurement noises are correlated at the same epoch. Firstly, a framework of decoupling filter (DF) based on equivalent model transformation is derived. Further, according to the framework of DF, a new extended Kalman filtering (EKF) algorithm via using first-order linearization approximation is developed. Secondly, the computational process of the suboptimal fading factor is derived on the basis of the extended orthogonality principle (EOP). Thirdly, the ultimate form of the proposed STF is obtained by introducing the suboptimal fading factor into the above EKF algorithm. The proposed STF can automatically tune the suboptimal fading factor on the basis of the residuals between available and predicted measurements and further the gain matrices of the proposed STF tune online to improve the filtering performance. Finally, the effectiveness of the proposed STF has been proved through numerical simulation experiments.

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“…By using the spherical simplex-radial cubature rule, the 5thSSRCKF method can get more accurate estimation than CKF. In the fifth-degree spherical simplex-radial cubature rule, the following integral is considered [24]:…”

Section: Review Of the Fifth-degree Spherical Simplex-radial Cubaturementioning

confidence: 99%

“…To further improve the estimation accuracy of CKF the fifth-degree CKF (5thCKF) is proposed in [24]. However, the computational cost of the 5thCKF increases rapidly with the increasing of the state dimension.…”

Section: Introductionmentioning

confidence: 99%

IMM Fifth-Degree Spherical Simplex-Radial Cubature Filter for Maneuvering Target Tracking

Liu¹,

Wu²

2017

Preprint

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For improving the tracking accuracy and model switching speed of maneuvering target tracking in nonlinear systems, a new algorithm named interacting multiple model fifth-degree spherical simplex-radial cubature filter (IMM5thSSRCKF) is proposed in this paper. The new algorithm is a combination of the interacting multiple model (IMM) filter and fifth-degree spherical simplex-radial cubature filter (5thSSRCKF). The proposed algorithm makes use of Markov process to describe the switching probability among the models, and uses 5thSSRCKF to deal with the state estimation of each model. The 5thSSRCKF is an improved filter algorithm, which utilizes the fifthdegree spherical simplex-radial rule to improve the filtering accuracy. Finally, the tracking performance of the IMM5thSSRCKF is evaluated by simulation in a typical maneuvering target tracking scenario. Simulation results show that the proposed algorithm has better tracking performance and quicker model switching speed when disposing maneuver models compared with IMMUKF, IMMCKF and IMM5thCKF.

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The high-degree cubature Kalman filter

Cited by 93 publications

References 9 publications

Optimal Maneuver Strategy of Observer for Bearing-Only Tracking in Threat Environment

Optimal Maneuver Strategy of Observer for Bearing-Only Tracking in Threat Environment

Strong Tracking Filter for Nonlinear Systems with Randomly Delayed Measurements and Correlated Noises

IMM Fifth-Degree Spherical Simplex-Radial Cubature Filter for Maneuvering Target Tracking

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