An Improved Chaos Driven Hybrid Differential Evolution and Butterfly Optimization Algorithm for Passive Target Localization Using TDOA Measurements

Abstract

This paper addresses the problem of time difference of arrival (TDOA) based passive target localizationand proposes an improved chaos-driven hybrid differential evolution (DE) algorithm and butterfly optimization algorithm (BOA), named ICDEBOA, to solve this complex optimization problem. The proposed algorithm consists of a new mutation strategy with the mechanisms of the BOA algorithm incorporated into the DE algorithm. To boost optimization effectiveness, chaos theory is employed to adjust the control parameter values. The considered localization problem is formulated using the maximum likelihood estimator. To perform the accuracy comparison, the convex constrained weighting least squares algorithm is applied to the considered localization problem as the widely used method in literature. The statistical analysis shows that the proposed modifications to the ICDEBOA algorithm improve its optimization performance, as demonstrated by the improved performance on the CEC2014 benchmark problems. The ICDEBOA algorithm is also shown to be more robust than existing algorithms in noisy environments. Numerical simulation results show that the proposed ICDEBOA algorithm meets the CRLB and achieves better performance than the CWLS, DE, and BOA algorithms.