A Novel Indoor Intelligent Location Algorithm Based on GA-BFO

The error caused by nonline-of-sight (NLOS) is main factor affecting the indoor wireless positioning accuracy. In order to eliminate the NLOS error and improve the positioning accuracy, genetic algorithm, genetic algorithm-Hill Climbing algorithm and genetic algorithm-Bacteria Foraging Optimization algorithm are applied to time difference of arrival (TDOA) positioning optimization in this paper. Research results show genetic algorithm-Bacteria Foraging Optimization algorithm, combined global search with local search, has the best performance in terms of positioning accuracy and convergence speed. This method is better in eliminating the NLOS error and improving the performance of real-time positioning.

[1]  K. Passino,et al.  Biomimicry of Social Foraging Bacteria for Distributed Optimization: Models, Principles, and Emergent Behaviors , 2002 .

[2]  Yu Yao,et al.  Research on hybrid location algorithm with high accuracy in indoor environment , 2015, 2015 34th Chinese Control Conference (CCC).

[3]  Jium-Ming Lin,et al.  MS Location Estimation with Genetic Algorithm , 2012, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

[4]  Yu Quan.  Low complexity TOA estimation algorithm in the UWB indoor channel , 2009 .

[5]  Yi Shen,et al.  GA-BFO based signal reconstruction for compressive sensing , 2013, 2013 IEEE International Conference on Information and Automation (ICIA).

[6]  Jen-Fa Huang,et al.  Using Memetic Algorithm to optimize location estimate of mobile station in non-line-of-sight environment , 2014, 2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE).

[7]  Lv Xin,et al.  Research on UWB-based location technology applied for hazardous chemicals stacking storage , 2014, The 26th Chinese Control and Decision Conference (2014 CCDC).

[8]  Ali H. Sayed,et al.  A non-line-of-sight equalization scheme for wireless cellular location , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[9]  Yi Shen,et al.  Artificial immune algorithm based signal reconstruction for compressive sensing , 2014, 2014 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings.

[10]  Cha-Hwa Lin,et al.  Mobile location estimation by density-based clustering for NLoS environments , 2006, 20th International Conference on Advanced Information Networking and Applications - Volume 1 (AINA'06).

[11]  Deng Ping,et al.  An NLOS error mitigation scheme based on TDOA reconstruction for cellular location services , 2003 .

[12]  A. Hepbasli,et al.  Electricity estimation using genetic algorithm approach: a case study of Turkey , 2005 .