Self-tuning Distributed Fusion Estimation for Systems With Unknown Model Parameters and Fading Measurement Rates
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摘要: 研究了带未知模型参数和衰减观测率多传感器线性离散随机系统的信息融合估计问题.在模型参数和衰减观测率未知的情形下, 应用递推增广最小二乘(Recursive extend least squares, RELS)算法和加权融合估计算法提出了分布式融合未知模型参数辨识器; 应用相关函数对描述衰减观测现象的随机变量的数学期望和方差进行在线辨识.将辨识后的模型参数、数学期望和方差代入到最优分布式融合状态滤波器中, 获得了相应的自校正融合状态滤波算法.应用动态误差系统分析(Dynamic error system analysis, DESA)方法证明了算法的收敛性.仿真例子验证了算法的有效性.Abstract: This paper is concerned with the information fusion estimation problem for multi-sensor linear discrete-time stochastic systems with unknown model parameters and fading measurement rates. When the model parameters and fading measurement rates are unknown, a distributed weighted fusion identifier for the unknown model parameters is presented based on the recursive extend least squares (RELS) algorithm and weighted fusion estimation algorithm. Both the mathematical expectations and variances of random variables which describe the phenomena of fading measurements are identified by using the correlation functions. The corresponding self-tuning distributed fusion state filtering algorithm is obtained by substituting the identified model parameters, the mathematical expectations and variances into the optimal distributed fusion state filter. The convergence of the proposed algorithms is proven by using a dynamic error system analysis (DESA) method. A simulation example shows the effectiveness of the proposed algorithms.
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Key words:
- Recursive extend least squares (RELS) /
- correlation function /
- unknown model parameter /
- unknown fading measurement rate /
- self-tuning fusion estimation
1) 本文责任编委 张俊 -
图 4 $\mu_{i}(t)$的数学期望辨识
Fig. 4 Identification of Mathematical expectation of $\mu_{i}(t)$
图 8 局部、融合最优与自校正状态分量1的滤波误差方差
Fig. 8 Variance of the first state component of local, fusion optimal and self-tuning filters
图 9 局部、融合最优与自校正状态分量2的滤波误差方差
Fig. 9 Variance of the second state component of local, fusion optimal and self-tuning filters
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