Efficient Relative Fingerprinting Based UAV Localization via Tensor Completion

Recently, unmanned Aerial Vehicles (UAVs) localization is becoming a major research focus. In this paper, we propose a novel efficient relative fingerprinting-based passive UAV localization algorithm via a tensor completion approach. We first introduce a new relative fingerprint framework by exploring the correlations between the UAV fingerprint and the fingerprint database, the correction factors can be achieved to apply the fingerprint idea into the passive localization case. Then, we exploit the spatial correlation of the RSS data and propose a new training scheme which utilizes tensor completion. Simulation results highlight the superior performance of the proposed algorithm in terms of reconstruction error and localization accuracy.

[1]  Sergio Montenegro,et al.  Obstacle Detection and Collision Avoidance for a UAV With Complementary Low-Cost Sensors , 2015, IEEE Access.

[2]  Shibo He,et al.  Leveraging Crowdsourcing for Efficient Malicious Users Detection in Large-Scale Social Networks , 2017, IEEE Internet of Things Journal.

[3]  Guang Yang,et al.  Promoting Cooperation by the Social Incentive Mechanism in Mobile Crowdsensing , 2017, IEEE Communications Magazine.

[4]  Bang Wang,et al.  FinCCM: Fingerprint Crowdsourcing, Clustering and Matching for Indoor Subarea Localization , 2015, IEEE Wireless Communications Letters.

[5]  Éric Marchand,et al.  3-D Model-Based Tracking for UAV Indoor Localization , 2015, IEEE Transactions on Cybernetics.

[6]  Alice Buffi,et al.  Numerical Investigation of an UWB Localization Technique for Unmanned Aerial Vehicles in Outdoor Scenarios , 2017, IEEE Sensors Journal.

[7]  Shueng-Han Gary Chan,et al.  Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons , 2016, IEEE Communications Surveys & Tutorials.

[8]  Panagiotis Tsakalides,et al.  Efficient Multi-Channel Signal Strength Based Localization via Matrix Completion and Bayesian Sparse Learning , 2015, IEEE Transactions on Mobile Computing.

[9]  Erik G. Ström,et al.  RSS-Based Sensor Localization in the Presence of Unknown Channel Parameters , 2013, IEEE Transactions on Signal Processing.

[10]  Qihui Wu,et al.  A Joint Tensor Completion and Prediction Scheme for Multi-Dimensional Spectrum Map Construction , 2016, IEEE Access.

[11]  Lu Lu,et al.  Novel energy-based localization technique for multiple sources , 2012, 2012 IEEE International Conference on Communications (ICC).

[12]  T. Aaron Gulliver,et al.  Blind Received Signal Strength Difference Based Source Localization With System Parameter Errors , 2014, IEEE Transactions on Signal Processing.

[13]  Jing Zhang,et al.  Multi-dimensional spectrum map construction: A tensor perspective , 2016, 2016 8th International Conference on Wireless Communications & Signal Processing (WCSP).

[14]  Pradipta De,et al.  A Survey of Fingerprint-Based Outdoor Localization , 2016, IEEE Communications Surveys & Tutorials.

[15]  Dario Petri,et al.  Accuracy of RSS-Based Centroid Localization Algorithms in an Indoor Environment , 2011, IEEE Transactions on Instrumentation and Measurement.

[16]  Shahrokh Valaee,et al.  Joint Indoor Localization and Radio Map Construction with Limited Deployment Load , 2015, IEEE Transactions on Mobile Computing.

[17]  Jiming Chen,et al.  Anti-Drone System with Multiple Surveillance Technologies: Architecture, Implementation, and Challenges , 2018, IEEE Communications Magazine.