Iterated square root unscented Kalman filter for maneuvering target tracking using TDOA measurements

This paper presents a study involving the prediction of a complicated maneuvering target, with the aim of improving the tracking performance of time difference of arrival (TDOA) tracking system for passive radar. Because of the large error caused by the complicated maneuvers and a high realtime requirement, the TDOA tracking system will take a heavy computational load. In this study, we calculate the initial position of a complicated maneuvering target using the total least square method to decrease the initial tracking error. Based on the current statistical model and the square root unscented Kalman filter, an iterated square root unscented Kalman filter (ISRUKF) is presented and an iterated termination criteria is used to reduce the linearity error for the whole iterated process. Finally, comparative simulation results are provided to demonstrate the effectiveness and applicability of the proposed method.

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