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Heterogeneous Track-to-Track Fusion in 3D Using IRST Sensor and Air MTI Radar
IEEE Transactions on Aerospace and Electronic Systems ( IF 4.4 ) Pub Date : 2019-12-01 , DOI: 10.1109/taes.2019.2898302
Mahendra Mallick , Kuo-Chu Chang , Sanjeev Arulampalam , Yanjun Yan

Only a few publications exist at present on heterogeneous track-to-track fusion (T2TF). A common limitation of the current work on heterogeneous T2TF is that the cross covariance due to common process noise cannot be computed. This is due to the fact that two local trackers use different dynamic models, and hence, it is difficult to account for the common process noise. We consider a heterogeneous T2TF problem in three dimension (3-D) using a passive infrared search and track (IRST) sensor and an active air moving target indicator (AMTI) radar with the nearly constant velocity motion of the target. The active AMTI tracker uses the Cartesian state vector with 3-D position and velocity, and the dynamic model is linear. A passive IRST tracker commonly uses modified spherical coordinates (MSCs) for the state vector, where the dynamic model is nonlinear. In this formulation, the common process noise is explicitly modeled in both dynamic models. Therefore, it is possible to take into account the common process noise. We use the cubature Kalman filter (CKF) in both trackers due to its numerical stability and improved state estimation accuracy over existing nonlinear filters. The passive tracker uses a range-parameterized MSC-based CKF, and the active tracker uses a Cartesian CKF. We perform T2TF using the information filter (IF), where each local tracker sends its information matrix and the corresponding information state estimate to the fusion center. The IF handles the common process noise in an approximate way. Results from Monte Carlo simulations show that the accuracy of the proposed IF-based T2TF is close to that of the centralized fusion with varying levels of process noise and communication data rate.

中文翻译:

使用 IRST 传感器和空中 MTI 雷达的 3D 异构轨道到轨道融合

目前只有少数关于异构轨道到轨道融合(T2TF)的出版物存在。当前关于异构 T2TF 的工作的一个共同限制是无法计算由于共同过程噪声引起的交叉协方差。这是因为两个本地跟踪器使用不同的动态模型,因此很难解释常见的过程噪声。我们使用被动红外搜索和跟踪 (IRST) 传感器和具有目标几乎恒定速度运动的主动空中移动目标指示器 (AMTI) 雷达来考虑三维 (3-D) 中的异构 T2TF 问题。主动式 AMTI 跟踪器使用具有 3-D 位置和速度的笛卡尔状态向量,并且动态模型是线性的。被动 IRST 跟踪器通常使用修正球坐标 (MSC) 作为状态向量,其中动态模型是非线性的。在这个公式中,常见的过程噪声在两个动态模型中都被明确地建模。因此,可以考虑常见的过程噪声。我们在两个跟踪器中都使用了容积卡尔曼滤波器 (CKF),因为它的数值稳定性和改进的状态估计精度优于现有的非线性滤波器。被动跟踪器使用范围参数化的基于 MSC 的 CKF,主动跟踪器使用笛卡尔 CKF。我们使用信息过滤器 (IF) 执行 T2TF,其中每个本地跟踪器将其信息矩​​阵和相应的信息状态估计发送到融合中心。IF 以近似的方式处理常见的过程噪声。
更新日期:2019-12-01
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