Current target tracking algorithms for wireless sensor networks in noise environments have large positioning errors. Owing to the environmental noise, Kalman filters (KFs) are used to estimate the target position. To reduce the adverse effect of unknown or time-varying noise on KFs, adaptive KFs (AKFs) are developed. However, the present AKFs can only achieve second-order estimation accuracy. To improve the existing target tracking algorithm's positioning accuracy under unknown and time-varying noise environments, the authors propose a noise-aware algorithm based on a novel third-order adaptive cubature KF (ACKF) with higher estimation accuracy, which improves the accuracy of the existing algorithm by up to 63%. The innovative ACKF contains a new third-order noise statistic estimator and a traditional cubature KF without noise perception. A large number of numerical simulations and practical experiments show that the proposed noise-aware target tracking algorithm based on the novel ACKF is always more accurate than the target tracking algorithms based on the current KFs, no matter whether the moving target is manoeuvring or not, whether the strength of the noise is small or large, whether the number of anchor nodes is many or few, and whether the noise is time-varying or constant.

中文翻译：

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自适应自适应卡尔曼滤波的无线传感器网络噪声感知机动目标跟踪算法

当前在噪声环境中用于无线传感器网络的目标跟踪算法具有较大的定位误差。由于环境噪声，卡尔曼滤波器（KFs）用于估计目标位置。为了减少未知噪声或随时间变化的噪声对KF的不利影响，开发了自适应KF（AKF）。然而，当前的AKF只能实现二阶估计精度。为了提高未知和时变噪声环境下目标跟踪算法的定位精度，作者提出了一种基于新颖的三阶自适应培养KF（ACKF）的噪声感知算法，该算法具有更高的估计精度，从而提高了目标跟踪算法的定位精度。现有算法最多可提高63％。创新的ACKF包含一个新的三阶噪声统计估计器和一个没有噪声感知的传统库房KF。