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Target position estimation is valuable for sensor detection and weapon attack. However, there is position uncertainty due to sensor detection accuracy and environmental noise. In this paper, the uncertainty region is graphically represented by area of uncertainty (AOU), and the error ellipses represent the error dispersal of 2D target s position. Calculate AOU using Extended Kalman Filter (EKF) algorithm and Dead Reckon (DR) algorithm. EKF covariance matrix is used for calculating AOU while estimating target position; The dead reckon algorithm realizes the update of AOU by calculating the time difference based on the last time’s AOU. Finally, it was implemented and operated in a naval combat system, which verifies the effectiveness of the algorithms. Introduction Battlefield target information is both the basis for command decision and the necessary for the combat unit to perform missions. In combat simulation, target information is acquired by various sensors. Based on this acquired information, the fusion system estimates and predicts the target's location, identification, threat classification, etc. Due to the uncertainty of measurement caused by sensor detection capability and environmental noise and the uncertainty of the target model used in the fusion center of simulation system, the obtained target state data is uncertain. This article focuses on the uncertainty of target location. At present, the research on uncertainty of location estimation is not perfect at home and abroad. Based on the method expressing the positional uncertainty of point entities in surveying and mapping, LU Dai-jun and others have employed uncertainty ellipse to characterize the uncertainty region for the error dispersal of 2D target’s position and uncertainty ellipsoid for the 3D target, and describe the uncertainty of the target position in graphical form [1]. In addition, based on Target Uncertainty Area(TUA) and Weapon Uncertainty Area(WUA), Lu Dai-jun and others have put forward a calculation method of damage probability [2]. Sun Ning in Jiangsu Automation Research Institute has put forward a method of estimating the moving target’s likely position or uncertainty based on the error of initial location vector and displacement vector [3]. In 2004, Joelle J. Mann [4] developed the software program, LosCon, to help the ASW commander regain tactical control in a loss of submarine contact situation. Lines of bearing to the contact with associated standard deviation bearing errors and positions with their standard deviation range errors are the measurements LosCon uses to generate an ellipse of the submarine’s likely position. In 2009, Stephen M. Valerio [5] generated a tactical decision aid (TDA) capable of calculating the probability of kill of a submarine when targeted with a vertical launched(VLA) anti-submarine rocket propelled torpedo (ASROC). In determining the target’s position and AOU, the model employs an extended Kalman Filter that uses MTST movement and measurement models. In 2011, James C. Pabelico [6] examined ASW eFusion, an anti-submarine warfare (ASW) tactical decision aid (TDA) that utilizes Kalman filter-ing to improve battlespace awareness by simplifying and automating the track management process involved in a ASW watch standing operations. ASW eFusion allows the user to select one of two options to estimate how

Research on Uncertainty of Target Position Estimation in Combat Simulation