【摘要】 为提高小目标集群界面下复杂目标环境的光标选择任务性能，提出小目标集群界面下基于卡尔曼滤波的光标快速定位优化算法。综合考虑目标大小、目标初始距离、目标密度等因素对其目标选择效率和准确率的影响，通过对光标位置信息和光标运动信息的估计预测修正，实时计算光标观测值和估计值之间的可信度，以动态预测光标运行轨迹，隐式改善光标的运动稳定性，基于贝叶斯估计更新潜在目标的概率分布，并在不同条件下进行对照实验。结果表明：在进行目标选择时，采用基于卡尔曼滤波的动态光标快速定位优化算法有助于实现目标的精准选择及快速交互。更多还原

【Abstract】 In order to improve the performance of cursor selection task in complex target environment under small target cluster interface, an optimization algorithm for fast cursor positioning based on Kalman filter under small target cluster interface is proposed. The influence of factors such as target size, target initial distance, target density and the like on the target selection efficiency and accuracy is comprehensively considered, and the reliability between an observed value and an estimated value of a cursor is calculated in real time by estimating, predicting and correcting the position information and the motion information of the cursor to dynamically predict the motion track of the cursor and implicitly improve the motion stability of the cursor. The probability distribution of potential targets is updated based on Bayesian estimation, and controlled experiments are conducted under different conditions. The results show that the dynamic cursor fast positioning optimization algorithm based on Kalman filter is helpful to achieve accurate target selection and fast interaction.更多还原