TDOA Passive Location Based on Cuckoo Search Algorithm

This paper formulates a new framework to estimate the target position by adopting cuckoo search (CS) positioning algorithm. Addressing the nonlinear optimization problem is a crucial spot in the location system of time difference of arrival (TDOA). With the application of the Levy flight mechanism, the preferential selection mechanism and the elimination mechanism, the proposed approach prevents positioning results from falling into local optimum. These intelligent mechanisms are useful to ensure the population diversity and improve the convergence speed. Simulation results demonstrate that the cuckoo localization algorithm has higher locating precision and better performance than the conventional methods. Compared with particle swarm optimization (PSO) algorithm and Newton iteration algorithm, the proposed method can obtain the Cram´er-Rao lower bound (CRLB) and quickly achieve the global optimal solutions.

[1]  K. C. Ho,et al.  An Asymptotically Efficient Estimator for TDOA and FDOA Positioning of Multiple Disjoint Sources in the Presence of Sensor Location Uncertainties , 2011, IEEE Transactions on Signal Processing.

[2]  Bing He,et al.  The Robust Passive Location Algorithm for Maneuvering Target Tracking , 2015 .

[3]  La-or Kovavisaruch,et al.  Source Localization Using TDOA and FDOA Measurements in the Presence of Receiver Location Errors: Analysis and Solution , 2007, IEEE Transactions on Signal Processing.

[4]  K. C. Ho,et al.  An accurate algebraic solution for moving source location using TDOA and FDOA measurements , 2004, IEEE Transactions on Signal Processing.

[5]  K. C. Ho,et al.  Passive Source Localization Using Time Differences of Arrival and Gain Ratios of Arrival , 2008, IEEE Transactions on Signal Processing.

[6]  Jun Zheng,et al.  Particle swarm optimization for time-difference-of-arrival based localization , 2007, 2007 15th European Signal Processing Conference.

[7]  Hazem N. Nounou,et al.  Genetic Algorithm-based Adaptive Optimization for Target Tracking in Wireless Sensor Networks , 2014, J. Signal Process. Syst..

[8]  Mohamed Abd El Aziz,et al.  Source localization using TDOA and FDOA measurements based on modified cuckoo search algorithm , 2017, Wirel. Networks.

[9]  L. Jaulin,et al.  Robust TDOA passive location using interval analysis and contractor programming , 2009, 2009 International Radar Conference "Surveillance for a Safer World" (RADAR 2009).

[10]  Xiangtao Li,et al.  Modified cuckoo search algorithm with self adaptive parameter method , 2015, Inf. Sci..

[11]  Mi Wen,et al.  TDOA-based Sybil attack detection scheme for wireless sensor networks , 2008 .

[12]  Xin-She Yang,et al.  Multiobjective cuckoo search for design optimization , 2013, Comput. Oper. Res..

[13]  Yan Zhou,et al.  An approximately efficient bi-iterative method for source position and velocity estimation using TDOA and FDOA measurements , 2016, Signal Process..

[14]  Shuai He.  Asynchronous time difference of arrival positioning system and implementation , 2016 .

[15]  Ahmed Wasif Reza,et al.  A Mathematical Algorithm of Locomotive Source Localization Based on Hyperbolic Technique , 2015, Int. J. Distributed Sens. Networks.

[16]  Md. Akhtaruzzaman Adnan,et al.  A comparative study of Particle Swarm Optimization and Cuckoo Search techniques through problem-specific distance function , 2013, 2013 International Conference of Information and Communication Technology (ICoICT).

[17]  Qun Wan,et al.  Multidimensional Scaling Analysis for Passive Moving Target Localization With TDOA and FDOA Measurements , 2010, IEEE Transactions on Signal Processing.

[18]  Steven Kay,et al.  TDOA based direct positioning maximum likelihood estimator and the cramer-rao bound , 2014, IEEE Transactions on Aerospace and Electronic Systems.