中文摘要：

本文使用容积卡尔曼滤波器来处理分布式摄像机网络中的目标跟踪问题.平方根容积信息滤波（Square-Root Cubature Information Filter,SCIF）是容积卡尔曼滤波的一种扩展,其具有有效性和可靠性等方面优势,有利于对多源信息进行信息融合.然而当该算法应用于像摄像机网络这种大规模网络时,如果采用一般的集中式处理,中心节点可能会承受较大的计算压力.针对这个问题,本文首先将平方根容积信息滤波器进行了扩展,提出分布式平方根容积信息滤波器,使其能适应大规模网络.另外在摄像机网络中,由于摄像机装置在一个较大的区域内,由于摄像机观测区域有限,目标可能会出现在观察的盲区,这样就会存在某些摄像机的测量数据无效.针对这个问题,本文提出了平方根容积信息加权一致性滤波器（Square-Root Cubature Information Weighted Consensus Filter,SCIWCF）对状态信息和信息矩阵加权,减小这些无效信息在一致性算法的作用,从而提高整体的滤波性能.仿真实验结果表明,本文提出的算法能够在摄像机网络中对目标进行有效跟踪,在估计精度和滤波器稳定性等方面要优于传统的信息滤波.

英文摘要：

This paper deals with the problem of tracking target in a distributed camera network using the cubature Kalman filter. The square-root cubature Information filter（ SCIF） is an extension of the cubature Kalman filter. It is an efficient and robust non-linear filter for multi-sensor data fusion. However,when this algorithm is applied to large-scale networks such as camera networks,the center node may be imposed on severe computational loads if using centralized multisensor system. In order to solve this problem,a distributed algorithm based on square-root cubature information filter is presented for large-scale networks. In camera networks,because cameras are arranged in a larger region,the target may appear in the blind zone due to the limited field of view（ FOV）. This may produce invalid measurements from some cameras. To overcome this problem,this paper proposes a novel square-root cubature information weighted consensus filter（ SCIWCF）which reduces the effect of these invalid measurements in consensus algorithm via proper weighting on the information vector and information matrix. The simulation results showthat the proposed algorithm can efficiently track the target in camera networks,and is obviously better in terms of its accuracy and stability than the traditional Information filter.