Underwater source localization using time difference of arrival and frequency difference of arrival measurements based on an improved invasive weed optimization algorithm

In this study, we propose an underwater localization method based on an improved invasive weed optimization algorithm to accurately locate moving sources in underwater sensor networks. First, the Levy flight model is introduced into the invasive weed optimization algorithm to enhance its global search ability and avoid falling into local optima. At the same time, under the condition that the observed noise of each observation is Gaussian noise and does not consider the influence of other error factors, the localization error is adopted as the objective function to obtain an initial estimate for the unknown source parameter. Then, the obtained initial estimates of the target position and velocity as well as the target parameter error are utilized to construct a new localization model. Finally, the precise position of the source and its velocity are obtained according to the weighted least square method. The performance of the algorithm is verified by comparing it with the Cramer-Rao Lower Bound (CRLB). Results from simulations indicate that the algorithm proposed in this paper has excellent localization accuracy compared to existing methods and achieves results close to the CRLB.