The Nonsequential Fusion Method for Localization from Unscented Kalman Filter by Multistation Array Buoys

Abstract

Based on special features of array buoy and the research field of location and tracking of underwater target, the research combines the highly adaptive nonlinear filtering algorithm unscented Kalman filter with the nonlinear programming of multistation array buoy positioning system. In accordance with the model of nonsequential target location, the research utilizes Unscented Transformation to update the measuring error and covariance matrix of state error, aiming at estimating the filtering of state variable and acquiring the object’s current state of motion. The research analyzes the positioning performance of algorithm, pursuit path, astringency, and other performance indexes of target-relevant parameter through numerical simulation experiment. From the result, the conclusion that multistation array buoy can complete the task of tracing target track very well can be reached, which provides theoretical foundation for putting the algorithm into engineering practice.

An Improved Unscented Kalman Filter for Discrete Nonlinear Systems with Random Parameters

“…Extensive studies for the static source localization by TDOA measurements can be found in [19,20]. However, in practice, the emitter source may not be static, which could be mounted on-board of dynamic mobile platforms [21]. In addition, accurate sensor location information may not be available [20].…”