A Study on Target Positioning and Tracking Algorithm Based on Improved TDOA and Kalman Prediction Model
In this article,a target positioning and tracking algorithm based on an improved time difference of arrival(TDOA)algo-rithm and a Kalman prediction model is proposed,addressing the target tracking problem in the field of passive localization for drone swarms.Firstly,addressing the shortcomings of the existing Chan-based TDOA algorithm in poor time difference precision leading to inaccurate positioning and the robustness issues of the Taylor-based TDOA algorithm due to the significant impact of iter-ative initial values,a Chan-Taylor combined TDOA algorithm is introduced.This algorithm rapidly determines the initial estimate of the target position using the Chan algorithm and then iteratively calculates the target position using the Taylor algorithm.Based on improving positioning efficiency and shortening positioning time,the positioning accuracy has been enhanced.Secondly,utili-zing the Kalman prediction model,the predicted target position values generated by the target motion model are incorporated as pri-or information into the target positioning algorithm.By effectively integrating predictive and positional information,the precision of target positioning is further improved.Finally,through experimental simulations,the proposed algorithm's correctness and effec-tiveness are validated from several aspects,including the cumulative distribution function(CDF),geometrical dilution precision(GDOP)of positioning accuracy,and the root mean square error(RMSE)of positioning tracking.
unmanned aerial vehicle colonytarget positioning and trackingtime difference of arrivalChan-TaylorKalman pre-diction model
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