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Robust node position estimation algorithms for wireless sensor networks based on improved adaptive Kalman filters

机译：基于改进的自适应卡尔曼滤波器的无线传感器网络鲁棒节点位置估计算法

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摘要

The Kalman filter (KF) is an optimal state estimator for position observation systems with noise; therefore, it is typically used to estimate the location of a node in a wireless sensor network (WSN) in a noisy environment. The precision of noise statistics largely determines the localization accuracy of the KF, and the statistics of noise are often unknown or time-varying in a real WSN. Therefore, the adaptive Kalman filter (AKF) is utilized for awareness of the statistical parameters of noise. However, the node position estimation (NPE) algorithm based on the state-of-the-art AKF typically lacks robustness and becomes inaccurate in the case of simultaneously perceiving the statistics of process noise and measurement noise. This study proposes a robust NPE algorithm based on an improved adaptive extended Kalman filter (RNPE-IAEKF) and another robust NPE algorithm based on an improved adaptive unscented Kalman filter (RNPE-IAUKF). The RNPE-IAEKF algorithm has low computing complexity, while the RNPE-IAUKF algorithm has high positioning accuracy. Our proposed algorithms solve the problems of poor robustness and low accuracy of the NPE algorithm based on the adaptive extended Kalman filter (NPE-AEKF) and the NPE algorithm based on the adaptive unscented Kalman filter (NPE-AUKF). In addition, the RNPE-IAEKF and the RNPE-IAUKF do not lose robustness upon simultaneous perception of the statistics of process noise and measurement noise, which is strictly proven in theory. The results of practical experiments and numerical simulations demonstrate that regardless of the placement of a static target node, the mobility of a mobile target node, and the number of anchor nodes, the RNPE-IAEKF improves upon the positioning accuracy and convergence speed of the NPE-AEKF by at least 28% and 29%, respectively and that the RNPE-IAUKF increases the localization accuracy and convergence rate of the NPE-AUKF by at least 32% and 37%. (C) 2016 Elsevier B.V. All rights reserved.
机译：卡尔曼滤波器（KF）是具有噪声的位置观测系统的最佳状态估计器。因此，它通常用于估计嘈杂环境中无线传感器网络（WSN）中节点的位置。噪声统计信息的精度在很大程度上决定了KF的定位精度，而噪声统计信息在实际WSN中通常是未知的或随时间变化的。因此，自适应卡尔曼滤波器（AKF）用于了解噪声的统计参数。但是，基于最新AKF的节点位置估计（NPE）算法通常缺乏鲁棒性，并且在同时感知过程噪声和测量噪声的统计信息时变得不准确。本研究提出了一种基于改进的自适应扩展卡尔曼滤波器（RNPE-IAEKF）的鲁棒NPE算法，以及另一种基于改进的自适应无味卡尔曼滤波器（RNPE-IAUKF）的鲁棒NPE算法。 RNPE-IAEKF算法的计算复杂度较低，而RNPE-IAUKF算法的定位精度较高。我们提出的算法解决了基于自适应扩展卡尔曼滤波器的NPE算法（NPE-AEKF）和基于自适应无味卡尔曼滤波器的NPE算法（NPE-AUKF）的鲁棒性和准确性低的问题。此外，在同时感知过程噪声和测量噪声的统计数据时，RNPE-IAEKF和RNPE-IAUKF不会失去鲁棒性，这在理论上已得到严格证明。实际实验和数值模拟的结果表明，无论静态目标节点的位置，移动目标节点的移动性以及锚节点的数量如何，RNPE-IAEKF均可提高NPE的定位精度和收敛速度-AEKF至少分别增加了28％和29％，并且RNPE-IAUKF将NPE-AUKF的定位精度和收敛速度提高了至少32％和37％。 （C）2016 Elsevier B.V.保留所有权利。

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