有源雷达和无源雷达系统误差估计是实现有源无源雷达信息高精度融合的关键环节之一。
System error estimation for active radar and passive radar is the key for realizing high precision Information fusion of passive radar and active radar.
系统误差估计是雷达组网融合跟踪系统的重要环节，而可观测度又是决定误差估计效果的关键因素。
Systematic error estimation is an important process for data fusion and target tracking in radar networking system, and observability is the key factor which determines the estimation accuracy.
由仿真结果可以看出，它能较精确地估计陀螺常值漂移和航向角误差，为系统误差的校正提供了可信信息。
The simulation results show that the constant gyro drifts and the heading errors, which provide information for calibrating the system errors, can be estimated exactly.
运用该方法，只要一套MISTRAM系统的跟踪数据，就能给出自由飞行段常值系统误差的估计。
Specially, by use of this method, we can give high precision estimation of system error only using the tracking data of a set of MISTRAM.
连续波雷达是目前外火箭轨道测量的主要高精度设备，用数学方法估计和修正其系统误差有特别重要的意义。
The continuous wave radar is the main tracking equipment for trajectory observing at present. Estimation and calibration of system errors have important significance.
在估计并扣除系统误差之后，进行三通道最小二乘多项式滤波以更有效地抑制随机误差。
After the system error is estimated and deducted, the random error is effectively reduced by three channels polynomial least square filter.
在GPS信号不可用时，利用SINS和GPS得到数据所形成的误差值来驱动卡尔曼滤波器估计系统误差。
Once the GPS signal is effective, the data gained from SINS and GPS can calculate error value which drives Kalman filter and estimates system error.
仿真表明两种算法能有效地对雷达系统误差进行估计，尤其是当系统偏差发生变化时，配准效果明显优于实时质量控制(RTQC)配准算法。
Simulation results show that the proposed algorithms are effective and are superior that of the real time quality control (RTQC) algorithm for the varied system errors.
同时提出了一种成像系统误差的修正模型，并利用系统辨识方法正确估计了该模型的各参数；
Secondly, a camera system error model is put forward, and all coefficients are computed accurately by using system identification theory, and the test result is reported.
仿真计算结果表明该方法能够有效地发现和估计系统误差，同时指出在积分域进行匹配诊断和估计的精度要优于微分域匹配诊断。
Simulation results show that this method can detect and estimate the system error notably. Meanwhile the precision of matching diagnose in differential domain is higher than that in integral domain.
运用卡尔曼滤波对系统误差进行最优估计，将校正后的SINS数据作为综合定位系统的输出。
The optimal filter technology (Kalman Filter) was used to estimate the error and the SINS was corrected by the optimal error.
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.
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.
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