In this paper, a novel distributed tracking method is proposed for the problem of manoeuvring target tracking in sensor networks. Firstly, an adaptive adjustment tracking model is established by extended state observer (ESO) theory. Then, the consensus-based square-root cubature Kalman filter (SCKF) algorithm is proposed in order to improve the global accuracy and stability. In addition, the integrated model could reduce the influence of measurement noise. Finally, simulation is performed to verify the effectiveness of the scheme, whereby comparison results show that the estimation accuracy of the method proposed is higher than that of the traditional ESO and SCKF.

中文翻译：

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基于共识的平方根容积卡尔曼滤波器用于传感器网络中的机动目标跟踪

针对传感器网络中的机动目标跟踪问题，本文提出了一种新的分布式跟踪方法。首先，利用扩展状态观测器（ESO）理论建立了自适应调整跟踪模型。然后，为了提高全局精度和稳定性，提出了基于共识的平方根容积卡尔曼滤波器（SCKF）算法。此外，集成模型可以减少测量噪声的影响。最后，通过仿真验证了该方案的有效性，对比结果表明，该方法的估计精度高于传统的ESO和SCKF。