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朱允民, Qing'an Ren, Yunmin Zhu*, Xiaojing Shen, Enbin Song
Automatica 45(2009)1694-1702,-0001,():
-1年11月30日
In this paper, for general jointly distributed sensor observations, we present optimal sensor rules with channel errors for a given fusion rule. Then, the unified fusion rules problem for multisensor multi-hypothesis network decision systems with channel errors is studied as an extension of our previous results for ideal channels, i.e., people only need to optimize sensor rules under the proposed unified fusion rules to achieve global optimal decision performance. More significantly, the unified fusion rules do not depend on distributions of sensor observations, decision criterion, and the characteristics of fading channels. Finally, several numerical examples support the above analytic results and show some interesting phenomena which can not be seen in ideal channel case.
Distributed decision, Optimal sensor rule, Global optimization, Unified fusion rule, Channel error
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【期刊论文】Optimal Centralized Update With Multiple Local Out-of-Sequence Measurements
朱允民, Xiaojing Shen, Yunmin Zhu, Enbin Song, and Yingting Luo
IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL.57, NO.4, APRIL 2009,-0001,():
-1年11月30日
In a multisensor target tracking system, observations produced by sensors typically arrive at a central processor out of sequence. There have been some update algorithms for single out-of-sequence measurement (OOSM). In this paper, we consider optimal centralized update algorithms with multiple asynchronous (different lag time) OOSMs. First, we generalize the optimal update algorithm with single one-step-lag OOSM in [Y. Bar-Shalom, "Update With Out-of-Sequence Measurements in Tracking: Exact Solution," IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, vol.38, pp.769-778, July 2002] to optimal centralized update algorithm with multiple one-step-lag OOSMs. Then, based on best linear unbiased estimation, we present an optimal centralized update algorithm with multiple arbitrary-step-lag OOSMs. Finally, two suboptimal centralized update algorithms are proposed to reduce the computational complexity. A numerical example shows that performances of two suboptimal centralized algorithms are close to that of the optimal centralized update algorithm.
Kalman filtering, multisensor systems, out-of-sequence measurements
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【期刊论文】Minimum Variance in Biased Estimation With Singular Fisher Information Matrix
朱允民, Enbin Song, Yunmin Zhu, Jie Zhou, and Zhisheng You
IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL.57, NO.1, JANUARY 2009,-0001,():
-1年11月30日
This paper extends the work of Y. C. Eldar, "Minimum variance in biased estimation: Bounds and asymptotically optimal estimators," in IEEE Trans. Signal Process., vol. 52, pp. 1915-1929, Jul. 2004, which deals with only nonsingular Fisher information matrix. In order to guarantee the uniform Cramér–Rao bound to be a finite lower bound and also to have a feasible solution to the optimization problem in the work of Y. C. Eldar, it is proved that the norms of bias gradient matrices of all biased estimators must have a nonzero exact lower bound, which mainly depends on the rank of the singular Fisher information matrix. The smaller the rank of the singular Fisher information matrix is, the larger the lower bound of norms of bias gradient matrices of all biased estimators is. For a specific Frobenius norm, the exact lower bound is simply the difference between the parameter dimension and the rank of the singular Fisher information matrix.
Biased estimation, biased gradient norm, Cram
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【期刊论文】Fusion of Distributed Extended Forgetting Factor RLS State Estimators
朱允民, YUNMIN ZHU, KESHU ZHANG, X. RONG LI
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS VOL.44, NO.2 APRIL 2008,-0001,():
-1年11月30日
For single-target multisensor systems, two fusion methods are presented for distributed recursive state estimation of dynamic systems without knowledge of noise covariances. The estimator at every local sensor embeds the dynamics and the forgetting factor into the recursive least squares (RLS) method to remedy the lack of knowledge of noise statistics, developed before as the extended forgetting factor recursive least squares (EFRLS) estimator. It is proved that the two fusion methods are equivalent to the centralized EFRLS that uses all measurements from local sensors directly and their good performance is shown by simulation examples.
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朱允民, Yingting Luo, Yunmin Zhu*, Dandan Luo, Jie Zhou, Enbin Song and Donghua Wang
Sensors 2008, 8, 8086-8103,-0001,():
-1年11月30日
This paper proposes a new distributed Kalman filtering fusion with random state transition and measurement matrices, i.e., random parameter matrices Kalman filtering. It is proved that under a mild condition the fused state estimate is equivalent to the centralized Kalman filtering using all sensor measurements; therefore, it achieves the best performance. More importantly, this result can be applied to Kalman filtering with uncertain observations including the measurement with a false alarm probability as a special case, as well as, randomly variant dynamic systems with multiple models. Numerical examples are given which support our analysis and show significant performance loss of ignoring the randomness of the parameter matrices.
Random parameters matrices, Kalman filtering, Centralized fusion, Distributed fusion
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