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Heterogeneous Track-to-Track Fusion in 2D Using Sonar and Radar Sensors

机译:使用声纳和雷达传感器2D的异质轨道跟踪融合

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In our previous work we considered heterogeneous track-to-track fusion (T2TF) in 3D using an infrared search and track (IRST) sensor and an air moving target indicator (AMTI) radar. In this paper we consider the 2D counterpart, heterogeneous T2TF in 2D using a passive sonar and an active radar. The target is assumed to move with nearly constant velocity (NCV) motion in 2D. The sonar sensor measures bearing of the target whereas the radar measures range and azimuth of the target. For the sonar and radar trackers, we use range-parametrized modified polar coordinates (RP-MPC) and Cartesian state vector, respectively. Both trackers use the cubature Kalman filter (CKF) as the tracker filter. Local trackers send their information matrices and the corresponding information state estimates to the fusion center (FC). The FC performs T2TF using the information filter (IF). Monte Carlo simulations are used to evaluate the accuracy of the proposed IF based T2TF relative to that of centralized fusion.
机译:在我们以前的工作中,我们考虑使用红外搜索和跟踪(IRST)传感器和空气移动目标指示器(AMTI)雷达在3D异构磁道到磁道融合(T2TF)。在本文中,我们考虑2D对应异构T2TF在2D使用被动声纳和主动雷达。目标被假设为与2D几乎恒定的速度(NCV)运动的方式移动。轴承目标而雷达措施的声纳传感器测量范围和目标的方位角。对于声纳和雷达跟踪器,我们使用范围-参数化改性极坐标(RP-MPC)和笛卡尔状态向量。两个跟踪器使用数值积分卡尔曼滤波器(CKF)作为跟踪滤波器。当地跟踪器发送自己的信息矩阵和相应的信息状态估计值的融合中心(FC)。使用该信息过滤器(IF)的FC执行T2TF。蒙特卡罗模拟可用于评估基于相对于集中式融合的提议IF T2TF的准确性。

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