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

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%.

[1]  Xianlin Huang,et al.  Adaptive unscented Kalman filter in Inertial Navigation System alignment , 2011, 2011 2nd International Conference on Intelligent Control and Information Processing.

[2]  Jeffrey K. Uhlmann,et al.  Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.

[3]  Hon Keung Kwan,et al.  Distributed sensor network localization using combination and diffusion scheme , 2015, 2015 IEEE International Conference on Digital Signal Processing (DSP).

[4]  Stefano Chessa,et al.  Wireless sensor networks: A survey on the state of the art and the 802.15.4 and ZigBee standards , 2007, Comput. Commun..

[5]  Yunhao Liu,et al.  Localization of Wireless Sensor Networks in the Wild: Pursuit of Ranging Quality , 2013, IEEE/ACM Transactions on Networking.

[6]  Alvin S. Lim,et al.  A distributed middleware for self-configurable wireless sensor networks , 2014, Int. J. Sens. Networks.

[7]  B. Tapley,et al.  Adaptive sequential estimation with unknown noise statistics , 1976 .

[8]  Yi Zhang,et al.  Cooperative Target Localization and Tracking with Incomplete Measurements , 2014, Int. J. Distributed Sens. Networks.

[9]  Kai Bu,et al.  Robust localization against outliers in wireless sensor networks , 2013, TOSN.

[10]  Shoushan Jiang,et al.  Research on Target Tracking Based on Unscented Kalman Filter , 2013 .

[11]  Yang Song,et al.  A robust node localization method to address flip ambiguity in wireless networks , 2016, 2016 International Conference on Information Networking (ICOIN).

[12]  Shigeng Zhang,et al.  A locality-based range-free localization algorithm for anisotropic wireless sensor networks , 2016, Telecommun. Syst..

[13]  Santong Zhang.  An Adaptive Unscented Kalman Filter for Dead Reckoning Systems , 2009, 2009 International Conference on Information Engineering and Computer Science.

[14]  Halil Ersin Soken,et al.  Robust Kalman filtering for small satellite attitude estimation in the presence of measurement faults , 2014, Eur. J. Control.

[15]  Bor-Sen Chen,et al.  Robust Relative Location Estimation in Wireless Sensor Networks with Inexact Position Problems , 2012, IEEE Transactions on Mobile Computing.

[16]  Hari Balakrishnan,et al.  Tracking moving devices with the cricket location system , 2004, MobiSys '04.

[17]  Halil Ersin Soken,et al.  Robust adaptive unscented Kalman filter for attitude estimation of pico satellites , 2014 .

[18]  Yuanqing Xia,et al.  Kalman Filter With Recursive Covariance Estimation—Sequentially Estimating Process Noise Covariance , 2014, IEEE Transactions on Industrial Electronics.

[19]  Liu Xiao,et al.  Location estimation of mobile user in wireless sensor network based on Unscented Kalman Filter , 2008, 2008 International Conference on Microwave and Millimeter Wave Technology.

[20]  Dan Simon,et al.  Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches , 2006 .

[21]  Chin-Liang Wang,et al.  An adaptive location estimator using tracking algorithms for indoor WLANs , 2010, Wirel. Networks.

[22]  Hang Guo,et al.  A weighted combination filter with nonholonomic constrains for integrated navigation systems , 2015 .

[23]  Yan Zhou,et al.  Comparison of filtering techniques for simultaneous localization and tracking , 2015, 2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF).

[24]  Zhang Hai,et al.  Adaptive Kalman filter based on improved second order mutual difference estimation , 2015, 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC).

[25]  Yunhao Liu,et al.  Detecting Outlier Measurements Based on Graph Rigidity for Wireless Sensor Network Localization , 2013, IEEE Transactions on Vehicular Technology.

[26]  Yang Meng,et al.  Covariance matching based adaptive unscented Kalman filter for direct filtering in INS/GNSS integration , 2016 .

[27]  Abdelouahid Lyhyaoui,et al.  Combining Kalman Filtering with ZigBee Protocol to Improve Localization in Wireless Sensor Network , 2013 .

[28]  Yang Zhao,et al.  Robust Estimators for Variance-Based Device-Free Localization and Tracking , 2011, IEEE Transactions on Mobile Computing.

[29]  Tolga Eren,et al.  Graph invariants for unique localizability in cooperative localization of wireless sensor networks: Rigidity index and redundancy index , 2015, Ad Hoc Networks.

[30]  Reza Mahboobi Esfanjani,et al.  Improved robust finite-horizon Kalman filtering for uncertain networked time-varying systems , 2015, Inf. Sci..

[31]  D. Simon.  Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches , 2006 .

[32]  Wheeler Ruml,et al.  Improved MDS-based localization , 2004, IEEE INFOCOM 2004.

[33]  Francesco Sottile,et al.  Adaptive Location Tracking by Kalman Filter in Wireless Sensor Networks , 2009, 2009 IEEE International Conference on Wireless and Mobile Computing, Networking and Communications.

[34]  徐定杰,et al.  Online Estimation of Allan Variance Coefficients Based on a Neural-Extended Kalman Filter , 2015 .

[35]  Peng Zhang,et al.  Robust weighted fusion Kalman filters for multisensor time-varying systems with uncertain noise variances , 2014, Signal Process..

[36]  Yan Yu,et al.  Lightweight Robust Device-Free Localization in Wireless Networks , 2014, IEEE Transactions on Industrial Electronics.

[37]  M. Salazar-Palma,et al.  A survey of various propagation models for mobile communication , 2003 .

[38]  Lian Liu,et al.  Time difference localization algorithm based on modified unscented Kalman filter , 2015, 2015 Chinese Automation Congress (CAC).

[39]  Shirshu Varma,et al.  A Range Based Localization System in Multihop Wireless Sensor Networks: A Distributed Cooperative Approach , 2016, Wirel. Pers. Commun..

[40]  Li-Guo Zhang,et al.  Square-Root Unscented Kalman Filter for Vehicle Integrated Navigation , 2007, 2007 International Conference on Machine Learning and Cybernetics.

[41]  Jiannong Cao,et al.  Secure localization and location verification in wireless sensor networks: a survey , 2010, The Journal of Supercomputing.

[42]  Ai Jun Zhang,et al.  Improved adaptive Kalman filtering algorithm for vehicular positioning , 2015, 2015 34th Chinese Control Conference (CCC).

[43]  Wei Huang,et al.  Finite-Horizon Robust Kalman Filter for Uncertain Attitude Estimation System with Star Sensor Measurement Delays , 2014 .

[44]  Hugh F. Durrant-Whyte,et al.  A new method for the nonlinear transformation of means and covariances in filters and estimators , 2000, IEEE Trans. Autom. Control..

[45]  Siying Zhang,et al.  Study on Multi-Source Localization of Wireless Sensor Network Based on Quantitative Information , 2016 .

[46]  Lei Ren,et al.  Target Tracking Based on Amendatory Sage-Husa Adaptive Kalman Filtering , 2015 .

[47]  Jiannong Cao,et al.  Accurate Range-Free Localization for Anisotropic Wireless Sensor Networks , 2015, ACM Trans. Sens. Networks.

[48]  Pramod K. Varshney,et al.  Localization in Wireless Sensor Networks: Byzantines and Mitigation Techniques , 2013, IEEE Transactions on Signal Processing.

[49]  H. Jin Kim,et al.  Target Localization in Wireless Sensor Networks Using Online Semi-Supervised Support Vector Regression , 2015, Sensors.

[50]  Weidong Wang,et al.  Performance analysis based on least squares and extended Kalman filter for localization of static target in wireless sensor networks , 2015, Ad Hoc Networks.

[51]  Jian Huang,et al.  Indoor localization system based on wearable posture sensors with incomplete observations , 2014, Proceedings of 2014 International Conference on Modelling, Identification & Control.

[52]  Yu Hen Hu,et al.  Generalised Kalman filter tracking with multiplicative measurement noise in a wireless sensor network , 2014, IET Signal Process..

[53]  Yongmin Zhong,et al.  Windowing and random weighting‐based adaptive unscented Kalman filter , 2015 .

[54]  Arno Solin,et al.  Optimal Filtering with Kalman Filters and Smoothers , 2011 .

[55]  Toru Namerikawa,et al.  Extended Kalman filter-based mobile robot localization with intermittent measurements , 2013 .

[56]  R. Cherkaoui,et al.  Probabilistic assessment of the process-noise covariance matrix of discrete Kalman filter state estimation of active distribution networks , 2014, 2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS).

[57]  Jiannong Cao,et al.  Iterative Localization of Wireless Sensor Networks: An Accurate and Robust Approach , 2014, IEEE/ACM Transactions on Networking.

[58]  K. Soundararajan,et al.  Enhanced mechanism for localization in Wireless Sensor Networks using PSO assisted Extended Kalman Filter Algorithm (PSO-EKF) , 2015, 2015 International Conference on Communication, Information & Computing Technology (ICCICT).