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基于期望最大化算法的自适应噪声交互多模型滤波   总被引:1,自引:0,他引:1       下载免费PDF全文
A novel method under the interactive multiple model (IMM) filtering framework is presented in this paper, in which the expectation-maximization (EM) algorithm is used to identify the process noise eovarianee Q online. Fer the existing IMM filtering theory, the matrix Q is determined by means of design experience, but Q is actually changed with the state of the maneuvering target, Meanwhile it is severely influenced by the environment around the target, i,c., it is a variable of time. Therefore, the experiential eovarianee Q can not represent the influence of state noise in the maneuvering process exactly. Firstly, it is assumed that the evolved state and the initial conditions of the system can be modeled by using Gaussian distribution, although the dynamic system is of a nonlinear measurement equation, and furthermore the EM algorithm based on IMM filtering with the Q identification online is proposed. Secondly, the truncated error analysis is performed, Finally, the Monte Carlo simulation results are given to show that the proposed algorithm outperforms the existing algorithms and the tracking precision for the maneuvering targets is improved efficiently.  相似文献
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WSNs下一种自适应多传感器协同目标跟踪策略*   总被引:1,自引:1,他引:0       下载免费PDF全文
对WSNs中机动目标跟踪问题提出一种自适应多传感器协同跟踪策略。该策略能根据目标的移动位置,动态地唤醒无线传感器网络中部分传感器节点形成分簇,并选择合适的簇首和采样间隔进行目标跟踪。簇内节点通过协作感知以及测量信息融合,提高了跟踪精度,同时自适应可变采样间隔节约了通信能量和计算资源,满足了跟踪系统的实时性要求。提出了传感器网络能量均衡分配的指标,提高了网络的可靠性。由于模型的非线性和目标运动的机动性,采用IMM滤波器进行目标状态估计。仿真结果表明,与NSSS和DGSS相比,跟踪精度明显提高;与DCSS相比  相似文献
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An energy-balanced multiple-sensor collaborative scheduling is proposed for maneuvering target tracking in wireless sensor networks (WSNs). According to the position of the maneuvering target, some sensor nodes in WSNs are awakened to form a sensor cluster for target tracking collaboratively. In the cluster, the cluster head node is selected to implement tracking task with changed sampling interval. The distributed interactive multiple model (IMM) filter is employed to estimate the target state. The estimat...  相似文献
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