An Improved Localization Method Using Error Probability Distribution for Underwater Sensor Networks

An accurate localization scheme is essential to many underwater sensor applications. However, due to the persistent existence of uncertainties and measurement errors, an accurate localization is very difficult to achieve. To mitigate this problem, multi-iteration measurement and least squares scheme are often adopted in terrestrial applications to find a good estimate. But, in underwater applications the multi-iteration scheme is not practical due to high communication cost. Meanwhile, it has been observed that the errors in distance measurement often follow a certain pattern, which can be utilized to further improve on localization accuracy. In the paper, we analyze and utilize the measurement error distributions to better improve localization accuracy. An analytical model is developed for performance evaluation, along with extensive simulations. Both uniform error distribution and normal error distribution are considered in our research. Our results indicate that our proposed probabilistic localization method can significantly improve the localization accuracy over the commonly adopted least squares estimate (LSE) scheme.