摘要
无源声探测定位与跟踪是一种非线性系统状态估计问题。为了降低系统的复杂度和提高系统硬件的可实现性,将扩维UKF(Unscented Kalman Filter)应用于多传感器无源声探测定位跟踪。用多声传感器所测的方位角进行交叉定位,建立了扩维UKF无源声探测定位跟踪的状态方程和观测方程。为了提高跟踪精度,采用了一种基于子滤波估计值之间支持度的非等权值融合算法,将UKF子滤波的估计值进行融合得到融合估计值。仿真结果表明,扩维UKF和新融合方式结合的多传感器声探测定位与跟踪的精度高,可以较好地跟踪不规则运动的声目标,具有较大的工程应用价值。
Passive acoustic detection location and tracking is a nonlinear system state estimation problem. Augment Unscented Kalman Filter（UKF） method was applied in the multi-sensor passive acoustic detection location and tracking to reduce system complexity and improve system practicability. The acoustic sensor＇s measured azimuths were used to cross locate the target position. The augment UKF passive acoustic detection location and tracking state and observation equations were established. In order to improve the tracking precision, an unequal-weighted fusion algorithm, which based on the support degree among the sub-filtered estimation value, was used to fuse the UKF sub-filtered estimation value and get a fusion estimation value. Simulation result showed that the acoustic detection location and tracking method using augment UKF and the novel fusion can track the irregular movement acoustic target, which has good performance in tracking precision and will play an important role in engineering applications.
出处
《电光与控制》
北大核心
2010年第12期8-12,共5页
Electronics Optics & Control
基金
总装预研基金资助项目(JG2007056)
关键词
声探测
纯方位
交叉定位跟踪
UKF
数据融合
acoustic detection
bearings only
cross location and tracking
UKF
data fusion
