被动多传感器自适应曲线模型跟踪新算法  

Adaptive curvilinear model based target tracking algorithm for multiple passive sensors

作　　者：杨金龙[1] 姬红兵[1] 刘娟丽[1] 

机构地区：[1]西安电子科技大学电子工程学院,西安710071

出　　处：《控制与决策》2011年第8期1126-1130,共5页Control and Decision

基　　金：国家自然科学基金项目(60871074)

摘　　要：针对被动多传感器机动目标跟踪系统中,由于目标机动性能的不确定以及存在的非线性而导致系统模型与目标实际运动模式难以匹配的问题,提出一种新的自适应曲线模型跟踪算法.该算法通过建立新的方向角模型,设计一种自适应的转弯角速度估计方法,实时计算每个采样时刻目标的切向加速度,以获得与目标实际运动模式相匹配的运动模型,并与扩展卡尔曼滤波相结合,有效提高了被动多传感器下机动目标的跟踪精度.When tracking a maneuvering target by multiple passive sensors, it is difficult to match a tracking model with the actual motion pattern due to the uncertain changes of target states and the existence of nonlinearity. Therefore, a new maneuvering target tracking algorithm is proposed based on adaptive curvilinear model. A new model of direction angle is introduced to designe the estimation method of the adaptive turning angular velocity and calculating tangential acceleration at each sampling time, through which the curvilinear model matching well with the actual motion pattern can be obtained. Then, the classical EKF is combined to tracking the maneuvering target, which effectively improves the target tracking accuracy for multiple passive sensors.

关 键 词：机动目标跟踪 被动多传感器 集中式融合 曲线模型 

分 类 号：TN953[电子电信—信号与信息处理]