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1.
Multi-sensor optimal information fusion Kalman filter   总被引:3,自引:0,他引:3  
This paper presents a new multi-sensor optimal information fusion criterion weighted by matrices in the linear minimum variance sense, it is equivalent to the maximum likelihood fusion criterion under the assumption of normal distribution. Based on this optimal fusion criterion, a general multi-sensor optimal information fusion decentralized Kalman filter with a two-layer fusion structure is given for discrete time linear stochastic control systems with multiple sensors and correlated noises. The first fusion layer has a netted parallel structure to determine the cross covariance between every pair of faultless sensors at each time step. The second fusion layer is the fusion center that determines the optimal fusion matrix weights and obtains the optimal fusion filter. Comparing it with the centralized filter, the result shows that the computational burden is reduced, and the precision of the fusion filter is lower than that of the centralized filter when all sensors are faultless, but the fusion filter has fault tolerance and robustness properties when some sensors are faulty. Further, the precision of the fusion filter is higher than that of each local filter. Applying it to a radar tracking system with three sensors demonstrates its effectiveness.  相似文献   

2.
Linear minimum variance estimation fusion   总被引:2,自引:0,他引:2  
This paper shows that a general multisensor unbiased linearly weighted estimation fusion essentially is the linear minimum variance (LMV) estimation with linear equality constraint, and the general estimation fusion formula is developed by extending the Gauss-Markov estimation to the random parameter under estimation. First, we formulate the problem of distributed estimation fusion in the LMV setting. In this setting, the fused estimator is a weighted sum of local estimates with a matrix weight. We show that the set of weights is optimal if and only if it is a solution of a matrix quadratic optimization problem subject to a convex linear equality constraint. Second, we present a unique solution to the above optimization problem, which depends only on the covariance matrix Ck.Third, if a priori information, the expectation and covariance, of the estimated quantity is unknown, a necessary and sufficient condition for the above LMV fusion becoming the best unbiased LMV estimation with known prior informatio  相似文献   

3.
In this paper, we consider the design problem of optimal sensor quantization rules (quantizers) and an optimal linear estimation fusion rule in bandwidth-constrained decentralized random signal estimation fusion systems. First, we derive a fixed-point-type necessary condition for both optimal sensor quantization rules and an optimal linear estimation fusion rule: a fixed point of an integral operation. Then, we can motivate an iterative Gauss–Seidel algorithm to simultaneously search for both optimal sensor quantization rules and an optimal linear estimation fusion rule without Gaussian assumptions on the joint probability density function (pdf) of the estimated parameter and observations. Moreover, we prove that the algorithm converges to a person-by-person optimal solution in the discretized scheme after a finite number of iterations. It is worth noting that the new method can be applied to vector quantization without any modification. Finally, several numerical examples demonstrate the efficiency of our method, and provide some reasonable and meaningful observations how the estimation performance is influenced by the observation noise power and numbers of sensors or quantization levels.  相似文献   

4.
针对期望输出和未来干扰无预见的离散线性系统最优跟踪问题,提出一种基于信息融合最优估计的控制方法.若将当前给定值和可测干扰值分别看作系统未来输出和干扰的预见值,则跟踪控制问题可转化为具有无限预见步数的预见控制问题,并将无限预见信息融合成一步等效预见信息,进而获得近似最优融合控制律.对线性直流电机系统和宏观经济系统的仿真结果均验证了该控制器在提高系统跟踪精度和抑制干扰等方面的有效性.  相似文献   

5.
Shu-Li Sun   《Automatica》2005,41(12):2153-2159
Based on the optimal fusion criterion in the linear minimum variance sense, a distributed optimal fusion fixed-lag Kalman smoother with a three-layer fusion structure is given for the discrete time-varying linear stochastic control systems with multiple sensors and correlated noises. Its components are estimated by scalar weighting fusion, respectively. It only requires in parallel a series of computations of the weighted scalars, and avoids the computations of the weighted matrices, so that the computational burden can obviously be reduced. Further, the steady-state fusion smoother is also given for the discrete time-invariant linear stochastic control systems. The scalar weights can be obtained by fusing once after all local estimations reach steady state. It can reduce the online computational burden. Also, the computation formulas of smoothing error cross-covariance matrices are given. Two simulation examples show the performance.  相似文献   

6.
Designing a state estimator for a linear state-space model requires knowledge of the characteristics of the disturbances entering the states and the measurements. In [Odelson, B. J., Rajamani, M. R., & Rawlings, J. B. (2006). A new autocovariance least squares method for estimating noise covariances. Automatica, 42(2), 303-308], the correlations between the innovations data were used to form a least-squares problem to determine the covariances for the disturbances. In this paper we present new and simpler necessary and sufficient conditions for the uniqueness of the covariance estimates. We also formulate the optimal weighting to be used in the least-squares objective in the covariance estimation problem to ensure minimum variance in the estimates. A modification to the above technique is then presented to estimate the number of independent stochastic disturbances affecting the states. This minimum number of disturbances is usually unknown and must be determined from data. A semidefinite optimization problem is solved to estimate the number of independent disturbances entering the system and their covariances.  相似文献   

7.
8.
This paper presents a significant integrated optimization point of view behind the following three successful decision and estimation fusion results: 1) a unified fusion rule for networked sensor decision systems; 2) optimal sensor data quantization for estimation fusion and 3) integrated multi-target data association tracking systems. More precisely speaking, the integrated optimization method in 1) derives a unified objective function optimizing only sensor rules given a unified fusion rule; the method in 2) derives a unified objective function optimizing both the sensor quantization rule and the final estimation in the MSE sense, and the method in 3) integrates all associated targets and their valid observations into a whole random measurement matrix dynamic system so that the optimal random matrix Kalman filtering can be applied to estimate the states of all associated targets.  相似文献   

9.
In this paper we study the attitude estimation problem for an accelerated rigid body using gyros and accelerometers. The application in mind is that of a walking robot and particular attention is paid to the large and abrupt changes in accelerations that can be expected in such an environment. We propose a state estimation algorithm that fuses data from rate gyros and accelerometers to give long-term drift free attitude estimates. The algorithm does not use any local parameterization of the rigid body kinematics and can thus be used for a rigid body performing any kind of rotations. The algorithm is a combination of two non-standard, but in a sense linear, Kalman filters between which a trigger based switching takes place. The kinematics representation used makes it possible to construct a linear algorithm that can be shown to give convergent estimates for this nonlinear problem. The state estimator is evaluated in simulations demonstrating how the estimates are long-term stable even in the presence of gyro drift.  相似文献   

10.
This paper is concerned with the optimal state estimation for linear systems when the noises of different sensors are cross-correlated and also coupled with the system noise of the previous step. We derive the optimal linear estimation in a sequential form and for distributed fusion. They are both compared with the optimal batch fusion, suboptimal batch fusion, suboptimal sequential fusion, and the suboptimal distributed fusion where the cross-correlation between the noises are neglected. The comparison is in terms of theoretical filter mean square error and the real root mean square error. Simulation on a target tracking example is given to show the effectiveness of the presented algorithms.  相似文献   

11.
In this paper, we address the problem of minimum variance estimation for discrete-time time-varying stochastic systems with unknown inputs. The objective is to construct an optimal filter in the general case where the unknown inputs affect both the stochastic model and the outputs. It extends the results of Darouach and Zasadzinski (Automatica 33 (1997) 717) where the unknown inputs are only present in the model. The main difficulty in treating this problem lies in the fact that the estimation error is correlated with the systems noises, this fact leads generally to suboptimal filters. Necessary and sufficient conditions for the unbiasedness of this filter are established. Then conditions under which the estimation error and the system noises are uncorrelated are presented, and an optimal estimator and a predictor filters are derived. Sufficient conditions for the existence of these filters are given and sufficient conditions for their stability are obtained for the time-invariant case. A numerical example is given in order to illustrate the proposed method.  相似文献   

12.
离散线性信息融合最优跟踪控制   总被引:3,自引:0,他引:3  
提出一种有限时间离散线性最优跟踪控制问题的新解法--信息融合估计解法.基于信息融合估计理论,推导出协状态融合滤波方程和控制量融合估计值,由此获得最优融合控制律及二次性能指标最小值.从理论上证明了信息融合估计解法与传统解法的等同性,从信息融合的角度建立了有限时间离散线性最优跟踪控制系统,从而统一了最优控制问题和最优估计问题,电机系统的控制仿真结果验证了该解法的有效性以及与传统解法的等同性.  相似文献   

13.
14.
The interval estimation fusion method based on sensor interval estimates and their confidence degrees is developed. When sensor estimates are independent of each other, a combination rule to merge sensor estimates and their confidence degrees is proposed. Moreover, two optimization criteria: minimizing interval length with an allowable minimum confidence degree, or maximizing confidence degree with an allowable maximum interval length are suggested. In terms of the two criteria, an optimal interval estimation fusion can be obtained based on the combined intervals and their confidence degrees. Then we can extend the results on the combined interval outputs and their confidence degrees to obtain a conditional combination rule and the corresponding optimal fault-tolerant interval estimation fusion in terms of the two criteria. It is easy to see that Marzullo's fault-tolerant interval estimation fusion [Marzullo, (1990). Tolerating failures of continuous-valued sensors. ACM Transactions on Computer System, 8(4), 284-304] is a special case of our method.  相似文献   

15.
Remote sensing image fusion based on Bayesian linear estimation   总被引:1,自引:0,他引:1  
A new remote sensing image fusion method based on statistical parameter estimation is proposed in this paper. More specially, Bayesian linear estimation (BLE) is applied to observation models between remote sensing images with different spa- tial and spectral resolutions. The proposed method only estimates the mean vector and covariance matrix of the high-resolution multispectral (MS) images, instead of assuming the joint distribution between the panchromatic (PAN) image and low-resolution multispectral image. Furthermore, the proposed method can enhance the spatial resolution of several principal components of MS images, while the traditional Principal Component Analysis (PCA) method is limited to enhance only the first principal component. Experimental results with real MS images and PAN image of Landsat ETM demonstrate that the proposed method performs better than traditional methods based on statistical parameter estimation, PCA-based method and wavelet-based method.  相似文献   

16.
This paper adopts the concept of random weighting estimation to multi-sensor data fusion. It presents a new random weighting estimation methodology for optimal fusion of multi-dimensional position data. A multi-sensor observation model is constructed for multi-dimensional position. Based on this observation model, a random weighting estimation algorithm is developed for estimation of position data from single sensors. Using the random weighting estimations from each single sensor, an optimization theory is established for optimal fusion of multi-sensor position data. Experimental results demonstrate that the proposed methodology can effectively fuse multi-sensor dimensional position data, and the fusion accuracy is much higher than that of the Kalman fusion method.  相似文献   

17.
针对传感器测量噪声变化导致加权数据融合精度下降的问题,提出一种新的方差估计算法.方差估计采用自适应移动数据窗,窗口长度由多元假设检验的结果决定.假设检验环节首先应用信号分段处理方法与中心极限定理,使得检验统计量满足正态分布,简化了后续计算与理论推导.然后根据马尔可夫状态转移理论和最大后验概率准则,实现测量噪声方差变化的快速检测.通过与典型算法的仿真对比,验证所提算法克服了典型方法的局限性,能够保证加权数据融合具有更高精度.  相似文献   

18.
将多速率传感器数据融合技术与传统最小二乘估计方法相结合,可以得到一种基于多速率传感器数据融合技术的最小二乘估计新方法。它通过有效地融合各个传感器的观测数据,最终获得了基于全局测量信息的在极小化估计误差方差的迹的准则下最优的无偏参数估计结果。针对具体应用实例,计算机仿真不仅说明了这种方法的实用性,而且进一步验证了其有效性。  相似文献   

19.
For the multisensor systems with unknown noise variances, using the modern time series analysis method, based on on-line identification of the moving average (MA) innovation models, and based on the solution of the matrix equations for correlation function, the on-line estimators of the noise variances are obtained, and under linear minimum variance optimal information fusion criterion weighted by scalars for state components, a class of self-tuning decoupled fusion Wiener filters is presented. It realizes the self-tuning decoupled local Wiener filters and self-tuning decoupled fused Wiener filters for the state components. A new concept of convergence in a realization is presented, which is weaker than the convergence with probability one. The dynamic error system analysis (DESA) method is presented, by which the problem of convergence in a realization for self-tuning fusers is transformed into the stability problems of non-homogeneous difference equations, and the decision criterions of the stability are also presented. It is strictly proved that if the parameter estimation of the MA innovation models is consistent and if the measurement process is bounded in a realization or with probability one, then the self-tuning fusers will converge to the optimal fusers in a realization or with probability one, so that they have the asymptotic optimality. They can deal with the systems with the non-stationary or Gaussian measurement processes. They can reduce the computational burden, and are suitable for real time applications. A simulation example for a target tracking system with 3-sensor shows their effectiveness.  相似文献   

20.
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