Sequential covariance intersection fusion Kalman filter

Deng, Zili; Zhang, Peng; Qi, Wenjuan; Liu, Jinfang; Gao, Yuan

doi:10.1016/j.ins.2011.11.038

Cited by 216 publications

(113 citation statements)

References 32 publications

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“…For each case, different noise and kinematic models of transition equation are applied. Finally, 100 Monte Carlo simulations have been run for each case, and the fusion performance of nonlinear tracking is compared between our MCB algorithm and the UKF-SCI algorithm [12] with feedback structure. Note that, in this paper, we concentrate on Gaussian tracking scenarios, in which the UKF-SCI outperforms the DPF-ICI [13].…”

Section: Simulation Resultsmentioning

confidence: 99%

“…To our best knowledge, the most effective algorithms for fusion are based on covariance intersection (CI) [10,11]. These CI-based algorithms can be easily combined with nonlinear filers to form the state-of-the-art solutions to nonlinear problems, such as UKF-SCI [12] and DPF-ICI [13]. The former is more accurate because the unscented Kalman filter (UKF) performs better in normally nonlinear tracking, while the latter has an advantage in non-Gaussian scenarios benefitting from the particle filter (PF).…”

Section: Introductionmentioning

confidence: 99%

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A novel distributed fusion algorithm for multi-sensor nonlinear tracking

Liu

Wang

2016

EURASIP J. Adv. Signal Process.

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The covariance intersection (CI), especially with feedback structure, can be easily combined with nonlinear filters to solve the distributed fusion problem of multi-sensor nonlinear tracking. However, this paper proves that the CI algorithm is sub-optimal, thus degrading the fusion accuracy. To avoid such an issue, a novel distributed fusion algorithm, namely Monte Carlo Bayesian (MCB) algorithm, is proposed. First, it builds a distributed fusion architecture based on the Bayesian tracking framework. Then, the Monte Carlo sampling is incorporated into this architecture to form a feasible solution to nonlinear tracking. Finally, the simulation results verify that our MCB algorithm advances the state-of-the-art distributed fusion of nonlinear tracking.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel distributed fusion algorithm for multi-sensor nonlinear tracking

Liu

Wang

2016

EURASIP J. Adv. Signal Process.

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“…Generally, we select c ¼ 1. It has been proved in [9] that P 1 P 2 is equivalent to that the covariance ellipse of P 1 is enclosed in that of P 2 .…”

Section: Simulation Examplementioning

confidence: 99%

“…The ellipse of actual CI fused variance " P CI is enclosed in that of P CI , which verifies the robustness of the Eq. (95.23), and the ellipse of P CI encloses the intersection of the variance ellipses formed by P 1 and P 2 , and passes through the four points of intersection of the local ellipses for P 1 and P 2 [9].…”

Section: Simulation Examplementioning

confidence: 99%

Covariance Intersection Fusion Robust Steady-State Kalman Filter for Multi-Sensor Systems with Unknown Noise Variances

Zhang

Feng

et al. 2013

Lecture Notes in Electrical Engineering

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For multi-sensor systems with uncertainties of noise variances, a local robust steady-state Kalman filter with conservative upper bounds of unknown noise variances is presented. Based on the Lyapunov equation, its robustness is proved. Further, the covariance intersection (CI) fusion robust steady-state Kalman filter is presented. It is proved that its robust accuracy is higher than that of each local robust Kalman filter. A Monte-Carlo simulation example shows its correctness and effectiveness.Keywords Multi-sensor data fusion Á Covariance intersection fusion Á Robust Kalman filter Á Uncertain noise variances IntroductionThe multi-sensor information fusion has received great attentions and has been widely applied in many high-technology fields, such as tracking, signal proceeding, GPS position, robotics and so on. There are three optimal distributed weighted state fusers [1] which have the limitation to compute the optimal weights, the computation of the variances and cross-covariances of the local estimators are required. However, in many application problems, the systems have the uncertainty of model parameters or noise variances, so that the local filtering error variances and cross-covariances are unknown. To solve the filtering problems for

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“…In this paper, distributed fusion Wiener deconvolution estimator is weighted by matrix, diagonal matrices, scalars, covariance intersection fusion for linear stochastic multichannel ARMA signal [10][11][12][13]. The algorithm is under the linear minimum variance sense, and the optimal information fusion criterion includes weighted by matrix, diagonal matrices, scalars, covariance intersection fusion.…”

Section: Introductionmentioning

confidence: 99%