基于卡尔曼预测采样与空域图描述的稳健红外目标跟踪

Robust infrared target tracking with Kalman prediction sampling and spatiogram representation

目标状态采样策略和观测概率模型是影响粒子滤波理论框架下红外目标跟踪性能的主要因素。为了提高红外目标跟踪性能,介绍了一种基于卡尔曼预测采样与空域图描述的红外目标跟踪方法。目标状态采样采用卡尔曼预测采样策略,通过卡尔曼预测过程将目标的观测信息组合到重要性建议分布;采用空域图技术实现红外目标的稳健描述,通过计算参考目标的空域图与目标样本的空域图之间的Bhattacharyya距离,建立观测概率模型。传感器自运动场景、辐射不稳定场景及海杂波背景下的红外目标跟踪实验证明:该方法是有效的和稳健的。

Target states sampling strategies and observation probabilistic models are main factors on the infrared target tracking performance under the theory framework of particle filters. In order to improve the performance of the infrared target tracking, a novel method was proposed, which was based on Kalman prediction sampling and spatiogram target-representation. Kalman prediction sampling, which could combine target observations into the importance proposal distribution by the Kalman prediction process, was adopted to implement infrared target state sampling for particle filtering. Robust infrared targets were represented by the spatiogram, which could capture the spatial information of infrared targets. With the spatiogram target-representation, observation probability models were constructed by computing the Bhattacharyya distance between the reference target＇s spatiogram and target samples＇ spatiogram. Three infrared target tracking experimental results demonstrate the method is effective and robust for different scenes, such as the sensor ego-motion scene, the unstable radiation scene, and the sea-clutter scene.