基于多传感器多模型信息，给出了目标状态基于全局信息融合估计的一种新算法，并通过计算机仿真验证了这种算法的有效性。
Based on Multi_sensor Multi_model information, we present a new algorithm based on total information fusion estimation on target state. We prove the validity of this algorithm by computer.
采用卡尔曼滤波器对目标进行跟踪时，目标初始状态估计是影响初始阶段跟踪精度的一个重要原因。
When using Kalman Filter to track a target, estimation of the initial state of the target is an important factor influencing tracking precision in the initial phase.
由于选择了新的机动加速度量，从而得出线性的状态方程，由机动指令的实时估计得到机动目标自适应跟踪卡尔曼滤波器。
A linear state equation is got from selection of maneuvering acceleration. The adaptivity of adaptive tracking Kalman filter is represented by estimation of maneuvering commander at real time.
深入研究了多传感器数据融合的理论方法，并实验研究了数据融合的估计理论在光电经纬仪中的应用，目的是实现对目标状态的预测。
The theory method of multi-sensor data fusion is studied deep. And more, the estimate theory of data fusion is applied concretely to the theodolite with the aim of prognosticating the object state.
该算法可以有效地实现异类传感器之间的误差配准，同时估计目标的运动状态和传感器的系统误差。
Registration for heterogeneous sensor can be realized effectively, track of target and sensors' systematic errors can be estimated simultaneously.
用UK -GMPHDF完成局部传感器的局部状态估计，然后用FCM算法对这些局部状态进行融合处理，产生目标的全局状态估计。
In the algorithm, the UK-GMPHDF is used to complete local state estimation of local sensors, then the FCM algorithm is used to fuse the local state estimation and result global state estimation.
用UK -GMPHDF完成局部传感器的局部状态估计，然后用FCM算法对这些局部状态进行融合处理，产生目标的全局状态估计。
In the algorithm, the UK-GMPHDF is used to complete local state estimation of local sensors, then the FCM algorithm is used to fuse the local state estimation and result global state estimation.
应用推荐