共查询到18条相似文献,搜索用时 453 毫秒
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针对非线性非Gaussian系统的状态估计问题,提出一种基于信息融合的多传感器分布式粒子滤波算法。该算法首先利用粒子滤波方法分别计算局部传感器的状态估值,再应用分布式标量加权融合准则对状态估值进行信息融合。仿真结果表明和单传感器情形相比可提高滤波的精度。 相似文献
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针对带相关观测噪声和带不同观测函数的多传感器离散非线性系统,利用推广的离散Kalman滤波方法对状态系统和观测系统进行线性化处理,提出了基于岭估计的加权最小二乘(REWLS)分布式融合Kalman滤波算法.以风险函数为评价指标,利用信息滤波器比较了各种观测融合Kalman滤波算法,其中REWLS分布式融合算法精度最高.同时,分布式融合算法减少了计算负担,便于实时应用.仿真例子表明了理论分析的正确性. 相似文献
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分布式导航系统是飞机多传感器导航系统设计的新概念,可以大幅提高系统导航性能和容错水平,并能动态配置传感器功能,但是目前并无完善的信息融合算法与之对应;文章在构建惯性传感器网络的基础上,将多个低成本惯性传感器系统配置在飞机的多个位置以作为网络节点,设计了分阶段处理的分布式信息融合算法,综合利用各节点所测量的惯性信息,最后得到本节点的局部状态估计;通过仿真实验表明,采用此方法,有效降低了导航滤波估计误差,因此,系统导航性能及容错能力得到大幅提高。 相似文献
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多传感器噪声方差未知情况下的异步航迹融合 总被引:1,自引:1,他引:0
针对分布式多传感器数据融合系统,提出了一种多传感器异步航迹融合算法。现有的多传感器信息融合算法大都基于Kalman滤波器,要求噪声方差已知,并且假定各传感器同步采样,不考虑通信延迟。本文在分布式处理的模式下,基于各传感器在扩展记忆因子递推最小平方(EFRLS)估计形成本地航迹的基础上,提出了一种融合误差均方差矩阵的迹最小意义下的异步目标航迹融合算法。仿真实验结果表明,这种融合算法是有效的,算法接近集中式融合算法的精度。 相似文献
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针对粒子滤波(PF)在处理非线性、非高斯复杂动态系统故障诊断过程中,由于样本贫化所导致的故障诊断准确度低的问题,提出了一种改进鸟群算法优化粒子滤波的新算法。针对标准鸟群算法容易陷入局部最优问题,引入动态自适应系数和自适应步长,把每只鸟的位置和全局最优位置信息引入到自适应变化控制中,从而改善陷入局部最优的问题;采用改进后的鸟群算法优化粒子滤波重采样过程,即通过模拟鸟群的觅食、警戒和飞行行为使得粒子移向高似然区域;通过对双馈发电机定子电流传感器故障诊断的仿真分析,验证了算法的有效性。实验结果表明此算法可有效提高故障诊断的准确度。 相似文献
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针对一类带随机丢包的异步多传感器网络化系统,提出了基于网络化异步交互式多模型(Interacting multiple model,IMM)融合滤波的故障诊断方法.考虑不同传感器通道具有不同丢包概率的情况,将未知的故障幅值看作扩维的系统状态,利用提出的网络化异步IMM融合滤波算法对由系统正常模型和各种可能的故障模型构成的模型集进行滤波,根据模型概率进行故障检测和定位,同时得到故障幅值和系统状态的联合估计.提出的方法避免了传统IMM故障诊断方法模型集设计中故障大小难以确定的问题,适用于具有任意采样速率和任意初始采样时刻的异步多传感器网络化系统,并且通过融合多个传感器的信息提高了故障诊断的准确性.仿真实例验证了所提出方法的可行性和有效性. 相似文献
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无线多跳传感器网络下基于粒子滤波的信道容错的目标跟踪方法 总被引:3,自引:1,他引:2
对信道衰落的无线多跳传感器网络下的目标跟踪问题, 提出一种新的信道容错的粒子滤波方法. 传感器观测数据被量化成二元信号, 经非理想无线信道多跳中继通讯到达融合中心. 中继节点采用一种二元中继策略, 中继输出是信道污染的中继信号的估计值. 在粒子滤波器下, 考虑实际的物理信道, 计算粒子的似然度函数. 将信道衰落结合进跟踪算法, 在已知信道衰落包络和信道统计分布下, 分别设计信道容错的粒子滤波算法. 仿真结果表明信道容错的粒子滤波器提高了目标跟踪的精度, 对非完美信道具有鲁棒性. 相似文献
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Optimal distributed Kalman filtering fusion for a linear dynamic system with cross-correlated noises
In this article, we study the distributed Kalman filtering fusion problem for a linear dynamic system with multiple sensors and cross-correlated noises. For the assumed linear dynamic system, based on the newly constructed measurements whose measurement noises are uncorrelated, we derive a distributed Kalman filtering fusion algorithm without feedback, and prove that it is an optimal distributed Kalman filtering fusion algorithm. Then, for the same linear dynamic system, also based on the newly constructed measurements, a distributed Kalman filtering fusion algorithm with feedback is proposed. A rigorous performance analysis is dedicated to the distributed fusion algorithm with feedback, which shows that the distributed fusion algorithm with feedback is also an optimal distributed Kalman filtering fusion algorithm; the P matrices are still the estimate error covariance matrices for local filters; the feedback does reduce the estimate error covariance of each local filter. Simulation results are provided to demonstrate the validity of the newly proposed fusion algorithms and the performance analysis. 相似文献
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A fault detection and correction methodology for personal positioning systems for outdoor environments is presented. We demonstrate its successful use in a system consisting of a global positioning system receiver and an inertial measurement unit. Localization is based on the dead reckoning algorithm. In order to obtain more reliable information from data fusion, which is carried out with Kalman filtering, the proposed methodology involves: (1) evaluation of the information provided by the sensors and (2) adaptability of the filtering. By carefully analyzing these factors we accomplish fault detection in different sources of information and in filtering. This allows us to apply corrections whenever the system requires it. Hence, our methodology consists of two stages. In the first stage, the evaluation is conducted. We apply the principles of causal diagnosis using possibility theory by defining states for normal behavior and for fault states. When a fault occurs, corrective measures are applied according to empirical knowledge. In the second stage, the consistency test of the filtering is performed. If this is inconsistent, principles of adaptive Kalman filtering are applied, which means the process and measurement noise matrices are tuned. Our results indicate a reasonable improvement of the trajectory obtained. At the same time, we can achieve consistent filtering, to obtain a more robust system and reliable information. 相似文献
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针对分布式传感网络系统中存在互协方差未知的情形, 融合系数的科学设计对于融合性能至关重要. 本文以各节点估计方差矩阵逆的迹的倒数作为计算融合系数的中间变量, 设计了一种序贯快速协方差交叉融合算法, 可以显著减少各个融合节点的计算量, 能够保证各融合节点融合结果相同. 在给定系统的误差方差上界约束与优化指标前提下, 该融合算法结合粒子群优化算法, 能够给出对分布式系统中各个节点的传感器精度要求. 工程实践中, 可为传感器的选型提供理论依据. 最后, 给出了一个分布式网络传感器精度选型的算例及快速协方差交叉融合算法在雷达网中的应用实例. 相似文献
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基于集成智能算法的发动机滑油系统融合诊断 总被引:1,自引:0,他引:1
为确保飞行训练安全、预防发动机维修事故发生,针对当前航空发动机磨损故障诊断存在的问题,提出基于集成智能算法的发动机滑油系统故障检测模型.从铁谱诊断、光谱诊断、颗粒计数诊断、理化指标诊断和试车台数据诊断等方面研究了运用集成神经网络方法对多源信息进行融合诊断.结果表明:融合诊断结果的故障模式比单项诊断结果的故障模式要多;当单项诊断出现矛盾时,融合诊断结果能很好地解决诊断冲突问题.基于集成神经网络的发动机油液系统磨损故障的融合诊断能充分利用多种方法的互补性和有效解决诊断冲突问题,从而使诊断结果更为可靠和准确. 相似文献
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This paper addresses the distributed fusion filtering problem for multi-sensor systems with finite-step correlated noises. The process noise and observation noises of different sensors are finite-step auto- and cross-correlated, respectively. Based on the optimal local filtering algorithms that we presented before, the filtering error cross-covariance matrices between any two local filters are derived based on an innovation analysis approach. A distributed fusion filter is put forward by using matrix-weighted fusion estimation algorithm in the linear unbiased minimum variance sense. Finally, the proposed algorithms are extended to systems with random parameter matrices. Two simulation examples are given to show the effectiveness of the proposed algorithms. 相似文献
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基于模糊评判的决策级信息融合算法的研究 总被引:9,自引:1,他引:9
文章针对水电故障诊断系统中普遍采用的传感器阀值判断方法引起的信息损失问题,将决策级信息融合技术应用于故障诊断系统中。在模糊综合评判技术和软判决融合结构下,提出了一种新的决策级信息融合算法。该算法以合成运算和全局决策融合来自多传感器的局部判决以获取所处理对象的综合决策分析,并通过在丰满水电仿真系统的故障诊断系统中的实际应用表明该算法优于传统的故障检测方法。 相似文献