TDOA-based target localization method by minimizing module of noise vector in underwater acoustic networks

Given the complexity of underwater environments, network nodes are usually unstable, which leads to inaccurate self-localization. Consequently, time difference of arrival (TDOA) measurements are not accurate, which decreases the precision of the resulting location information. To solve the aforementioned problems, we propose a TDOA-based target localization method that employs the minimizing the module of noise vector in underwater acoustic networks. This algorithm uses the least-squares method to calculate the initial target position. Then, by considering the TDOA measurement error and the self-positioning error, an objective function is obtained through a series of transformations which minimizes the influence of the above errors on location accuracy. According to the initial value and the objective function, a simulated anneal algorithm is used to obtain the exact position of the target. Simulation results demonstrate that MMNV is superior to the weighted least-squares (WLS) and the constrained total least-squares (CTLS) algorithms in terms of positioning accuracy, robustness, and effect of errors on the result.