Toward Emergency Indoor Localization: Maximum Correntropy Criterion Based Direction Estimation Algorithm for Mobile TOA Rotation Anchor

To find the relative positional relationship accurately and efficiently without pre-installed infrastructure in emergent scenarios like firefighting, we proposed a rotating-anchor based indoor geolocation system using time of arrival (TOA) technique in this paper. In this system, the positioning problem is regarded as a template-matching problem to estimate direction of the target. Based on preliminary results, further measurements that the tester held the locator and turned around continuously without any stop. The TOA ranging error for continuous rotation measurement was analyzed and proved to follow non-Guassian with impulsive characteristic in this paper. According to such error characteristics, a direction estimation algorithm of maximum correntropy criterion-based matching algorithm was proposed in this paper. The performance of algorithm was validated by comparing with the least mean square and least mean p-norm with empirical measurements.

[1]  Peter Biber,et al.  Wireless node localization based on RSSI using a rotating antenna on a mobile robot , 2009, 2009 6th Workshop on Positioning, Navigation and Communication.

[2]  Juan Chóliz,et al.  UWB in Heterogeneous Access Networks: location-aware services , 2010, 2010 7th Workshop on Positioning, Navigation and Communication.

[3]  Juan Manuel Górriz,et al.  Intensity normalization in the analysis of functional DaTSCAN SPECT images: The α-stable distribution-based normalization method vs other approaches , 2015, Neurocomputing.

[4]  C. L. Nikias,et al.  Signal processing with fractional lower order moments: stable processes and their applications , 1993, Proc. IEEE.

[5]  Kaveh Pahlavan,et al.  Modeling the Effect of Human Body on TOA Based Indoor Human Tracking , 2013, Int. J. Wirel. Inf. Networks.

[6]  Kaveh Pahlavan,et al.  Toward Accurate Human Tracking: Modeling Time-of-Arrival for Wireless Wearable Sensors in Multipath Environment , 2014, IEEE Sensors Journal.

[7]  Ran He,et al.  Maximum Correntropy Criterion for Robust Face Recognition , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  J. Nolan,et al.  Modeling financial data with stable distributions , 2003 .

[9]  Juha-Pekka Makela,et al.  Indoor geolocation science and technology , 2002, IEEE Commun. Mag..

[10]  Fei Liu,et al.  CC-KF: Enhanced TOA Performance in Multipath and NLOS Indoor Extreme Environment , 2014, IEEE Sensors Journal.

[11]  Jong Ok Ha,et al.  A fully integrated 3–5 GHz UWB RF transceiver for WBAN applications , 2013, 2013 IEEE MTT-S International Microwave Workshop Series on RF and Wireless Technologies for Biomedical and Healthcare Applications (IMWS-BIO).

[12]  André P. Catarino,et al.  Performance Analysis of ToA-Based Positioning Algorithms for Static and Dynamic Targets with Low Ranging Measurements , 2017, Sensors.

[13]  Benoît Champagne,et al.  Time of arrival and power delay profile estimation for IR-UWB systems , 2013, Signal Process..

[14]  Xiaotong Zhang,et al.  Adaptive time delay estimation algorithm for indoor near‐field electromagnetic ranging , 2017, Int. J. Commun. Syst..

[15]  Liyuan Xu,et al.  The effect of multipath and NLOS on TOA ranging error and energy based on UWB , 2016, 2016 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW).

[16]  Chin-Der Wann,et al.  Hybrid TOA/RSSI Wireless Location with Unconstrained Nonlinear Optimization for Indoor UWB Channels , 2007, 2007 IEEE Wireless Communications and Networking Conference.

[17]  Jerald B. Johnson,et al.  Model selection in ecology and evolution. , 2004, Trends in ecology & evolution.

[18]  B. Alavi,et al.  Distance Measurement Error Modeling for Time-of-Arrival Based Indoor Geolocation , 2006 .

[19]  Duncan Graham-Rowe,et al.  Indoor ‘sat-nav’ could save firefighters , 2007 .

[20]  E. Wagenmakers,et al.  AIC model selection using Akaike weights , 2004, Psychonomic bulletin & review.

[21]  A. Ufuk Şahin,et al.  An area matching process to estimate the hydraulic parameters using transient constant-head test data , 2016 .

[22]  I. A. Koutrouvelis.  An iterative procedure for the estimation of the parameters of stable laws , 1981 .

[23]  Xiaotong Zhang,et al.  Characteristic Modeling of TOA Ranging Error in Rotating Anchor-Based Relative Positioning , 2017, IEEE Sensors Journal.

[24]  Jaime Campos,et al.  UWB in heterogeneous access networks: EUWB proposal for application scenarios , 2009, 2009 6th Workshop on Positioning, Navigation and Communication.

[25]  Miguel Lopez-Guerrero,et al.  On the Use of Alpha-Stable Distributions in Noise Modeling for PLC , 2015, IEEE Transactions on Power Delivery.

[26]  Nanning Zheng,et al.  Steady-State Mean-Square Error Analysis for Adaptive Filtering under the Maximum Correntropy Criterion , 2014, IEEE Signal Processing Letters.

[27]  Kaveh Pahlavan,et al.  Measurement and Modeling of Ultrawideband TOA-Based Ranging in Indoor Multipath Environments , 2009, IEEE Transactions on Vehicular Technology.

[28]  Lawrence Wai-Choong Wong,et al.  A robust dead-reckoning pedestrian tracking system with low cost sensors , 2011, 2011 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[29]  Nanning Zheng,et al.  Convergence of a Fixed-Point Algorithm under Maximum Correntropy Criterion , 2015, IEEE Signal Processing Letters.

[30]  Peng Hao,et al.  RSSI-based Indoor Localization Using RSSI-with-Angle-based Localization Estimation Algorithm , 2015 .

[31]  Badong Chen,et al.  Maximum Correntropy Estimation Is a Smoothed MAP Estimation , 2012, IEEE Signal Processing Letters.

[32]  Kaveh Pahlavan,et al.  UDP identification and error mitigation in toa-based indoor localization systems using neural network architecture , 2009, IEEE Transactions on Wireless Communications.

[33]  Ali Khenchaf,et al.  Features modeling with an α-stable distribution: Application to pattern recognition based on continuous belief functions , 2013, Inf. Fusion.

[34]  Kaveh Pahlavan,et al.  Modeling of the TOA-based distance measurement error using UWB indoor radio measurements , 2006, IEEE Communications Letters.

[35]  David R. Anderson,et al.  Model Selection and Multimodel Inference , 2003 .

[36]  Iain B. Collings,et al.  Integration of IMU in indoor positioning systems with non-Gaussian ranging error distributions , 2016, 2016 IEEE/ION Position, Location and Navigation Symposium (PLANS).

[37]  Ioannis A. Koutrouvelis,et al.  Regression-Type Estimation of the Parameters of Stable Laws , 1980 .

[38]  Xinrong Li,et al.  Indoor Geolocation Distance Error Modeling using UWB Channel Measurements , 2005, 2005 IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communications.

[39]  Deniz Erdogmus,et al.  Information Theoretic Learning , 2005, Encyclopedia of Artificial Intelligence.

[40]  Weifeng Liu,et al.  Correntropy: Properties and Applications in Non-Gaussian Signal Processing , 2007, IEEE Transactions on Signal Processing.

[41]  Fredrik Olsson,et al.  Cooperative localization using a foot-mounted inertial navigation system and ultrawideband ranging , 2014, 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[42]  Ignas G. Niemegeers,et al.  EUROPCOM - An Ultra-WideBand (UWB)-Based Ad Hoc Network for Emergency Applications , 2008, VTC Spring 2008 - IEEE Vehicular Technology Conference.

[43]  Xi Liu,et al.  > Replace This Line with Your Paper Identification Number (double-click Here to Edit) < , 2022 .

[44]  Rui Caseiro,et al.  High-Speed Tracking with Kernelized Correlation Filters , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[45]  Gang Wang,et al.  I am the antenna: accurate outdoor AP location using smartphones , 2011, MobiCom '11.