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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Heterogeneous Track-to-Track Fusion in 3-D Using IRST Sensor and Air MTI Radar
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Heterogeneous Track-to-Track Fusion in 3-D Using IRST Sensor and Air MTI Radar

机译：使用IRST传感器和空气MTI雷达3-D中的异质跟踪融合

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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.
机译：在异构跟踪融合（T2TF）上仅存在一些出版物。对异构T2TF的当前工作的常见限制是不能计算由于常见的过程噪声引起的交叉协方差。这是由于两个本地跟踪器使用不同的动态模型，因此，难以考虑共同的过程噪声。我们在三维（3-D）中考虑使用无源红外搜索和轨道（IRST）传感器和有效的空气移动目标指示器（AMTI）雷达，以及具有近似恒定的速度运动的活动空气移动目标指示器（AMTI）雷达的异构T2TF问题。活动AMTI跟踪器使用具有3-D位置和速度的笛卡尔状态向量，动态模型是线性的。被动IRST跟踪器通常使用用于状态向量的修改的球形坐标（MSC），其中动态模型是非线性的。在该制剂中，在动态模型中明确地建模了常见的过程噪声。因此，可以考虑常见的过程噪声。由于其数值稳定性和现有非线性滤波器的数值稳定性和改进的状态估计精度，我们在两个跟踪器中使用Cubature Kalman滤波器（CKF）。被动跟踪器使用基于范围参数化的MSC的CKF，活动跟踪器使用笛卡尔CKF。我们使用信息滤波器（IF）执行T2TF，其中每个本地跟踪器将其信息矩​​阵和对应的信息状态估计发送到融合中心。如果以近似的方式处理常见的过程噪声。来自蒙特卡罗模拟的结果表明，所提出的基于IF的T2TF的准确性接近具有不同水平的过程噪声和通信数据速率的集中融合。

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