An Application of Hybrid Differential Evolution to 3-D Near field Source localization

3-D near field source localization is one of the hot areas of research which has found direct applications in Radar, Sonar and digital communication. In this work, we propose hybrid meta-heuristic based algorithm to estimate jointly and efficiently the range, elevation angle and amplitude of the near field sources impinging on uniform linear array. For this, first the Differential evolution (DE) and Interior Point Algorithm (IPA) are employed independently and then both of them are hybridized with each other to improve the accuracy and convergence rate further. In this hybridization, DE is used as a global optimization method while the IPA is acted as rapid local search optimizer. Mean Square Error is used as an objective evaluation function which requires single snapshot to converge and avoids any ambiguity among the angles that are supplement to each other. The proposed hybrid scheme (DE-IPA) produced better results as compare to DE and IPA alone. Moreover, the DE-IPA is also compared with the other hybrid meta-heuristic technique based on Genetic Algorithm hybridized with Interior point Algorithm (GA-IPA). The comparison is made on the basis of estimation accuracy, robustness against noise, convergence rate and root mean square error. The validity and effectiveness of the proposed hybrid scheme is exploited on the basis of large number of Monte-Carlo simulations.