Robust ToA-Based Localization in a Mixed LOS/NLOS Environment Using Hybrid Mapping Technique

A two-stage hybrid method based on the machine learning approach is proposed for source localization using time of arrival (ToA) measurements in a mixed line of sight (LOS) and non-line of sight (NLOS) environment. The first stage applies an artificial neural network (NN) to detect the NLOS measurements that are outliers and the second stage passes the identified LOS measurements to an inverse weighted self-organizing network (IWSON) for determining the source location. The NN NLOS detector is able to take care of a variable number of NLOS measurements while the IWSON handles naturally a variable number of inputs and yields a solution without explicitly solving the nonlinear estimation problem. Simulations validate the good performance of the system with a different number of NLOS measurements. It provides a solution in reaching the Cramer-Rao‘ lower bound (CRLB) accuracy under a harsh multipath noisy environment, except over the small error region where it can act as an initialization for the iterative MLE to refine accuracy if necessary.

[1]  K. C. Ho,et al.  Improving TOA Localization Through Outlier Detection Using Intersection of Lines of Position , 2018, 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP).

[2]  Hisashi Kobayashi,et al.  On time-of-arrival positioning in a multipath environment , 2004, IEEE 60th Vehicular Technology Conference, 2004. VTC2004-Fall. 2004.

[3]  Simon Haykin,et al.  Neural Networks and Learning Machines , 2010 .

[4]  Trung-Kien Le,et al.  Rank Properties for Matrices Constructed From Time Differences of Arrival , 2018, IEEE Transactions on Signal Processing.

[5]  David J. C. MacKay,et al.  Bayesian Interpolation , 1992, Neural Computation.

[6]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[7]  Dave Winkler,et al.  Bayesian Regularization of Neural Networks , 2009, Artificial Neural Networks.

[8]  Nirvana Meratnia,et al.  Distributed online outlier detection in wireless sensor networks using ellipsoidal support vector machine , 2013, Ad Hoc Networks.

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

[10]  Andreu Urruela,et al.  A non-line-of-sight mitigation technique based on ML-detection , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[11]  Ran Wolff,et al.  In-Network Outlier Detection in Wireless Sensor Networks , 2006, ICDCS.

[12]  Goutam Chakraborty,et al.  Development of Laguerre Neural-Network-Based Intelligent Sensors for Wireless Sensor Networks , 2011, IEEE Transactions on Instrumentation and Measurement.

[13]  Yasuhisa Takizawa,et al.  Cloud-based Self-Organizing Localization for wireless sensor networks in mixture environments of LOS and NLOS , 2015, 2015 International Wireless Communications and Mobile Computing Conference (IWCMC).

[14]  Fredrik Gustafsson,et al.  TOA-Based Robust Wireless Geolocation and Cramér-Rao Lower Bound Analysis in Harsh LOS/NLOS Environments , 2013, IEEE Transactions on Signal Processing.

[15]  Michael Negnevitsky,et al.  Artificial Intelligence: A Guide to Intelligent Systems , 2001 .

[16]  Parmeet Kaur,et al.  Outlier Detection Using Kmeans and Fuzzy Min Max Neural Network in Network Data , 2016, 2016 8th International Conference on Computational Intelligence and Communication Networks (CICN).

[17]  Steven Kay,et al.  Fundamentals Of Statistical Signal Processing , 2001 .

[18]  Yue Yu,et al.  WSN Location Method Based on BP Neural Network in NLOS Environment , 2014, 2014 International Conference on Wireless Communication and Sensor Network.

[19]  Sanaa S. A. Al-Samahi,et al.  Improving Elliptic/Hyperbolic Localization Under Multipath Environment Using Neural Network for Outlier Detection , 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[20]  J. Holtzman,et al.  The non-line of sight problem in mobile location estimation , 1996, Proceedings of ICUPC - 5th International Conference on Universal Personal Communications.

[21]  K. C. Ho.  Bias Reduction for an Explicit Solution of Source Localization Using TDOA , 2012, IEEE Transactions on Signal Processing.

[22]  Pak-Chung Ching,et al.  Time-of-arrival based localization under NLOS conditions , 2006, IEEE Transactions on Vehicular Technology.

[23]  Joseph W. Goodman,et al.  A generalized convergence theorem for neural networks , 1988, IEEE Trans. Inf. Theory.

[24]  Hwee Pink Tan,et al.  LOS and NLOS Classification for Underwater Acoustic Localization , 2014, IEEE Transactions on Mobile Computing.