ID 原文 译文
7064 接着,对方位空变信号进行奇异值分解将方位空变与多普勒分离,通过级联的两步奇异值分解和方位非线性变标操作完成方位空变校正,实现整个场景的聚焦。 Then a space-variant signal for the singular value decomposition will be bearing space-variant separated from doppler, by cascading the two steps of singular value decomposition and azimuth nonlinear change standard operation completes azimuth empty correction, the focus of the whole scene.
7065 最后,仿真结果验证了该方法的有效性。 Finally, the simulation results demonstrate the effectiveness of the proposed method.
7066 为了减小方位陀螺漂移对单轴旋转捷联惯性导航系统(strapdown inertial navigation system,SINS)长时间定位精度影响,提出了一种方位陀螺漂移在线估计方法。 In order to reduce the azimuth gyro drift of single shaft rotary strapdown inertial navigation system (strapdown inertial navigation system of SINS) positioning accuracy for a long time, this paper proposes a azimuth gyro drift online estimation method.
7067 对SINS误差参数进行分析,指出东向陀螺漂移和方位失准角精度决定方位陀螺漂移估计值精度。 The SINS error parameters is analyzed, and points out that the east gyro drift and the azimuth misalignment Angle precision determine the azimuth gyro drift estimation precision.
7068 利用优化后的卡尔曼(Kalman)滤波器在线估计SINS失准角并进行补偿,在此基础上进一步使用Kalman滤波器估计惯性测量单元(inertial measurement unit,IMU)误差。 By using the optimized Kalman (Kalman filter online estimate SINS misalignment Angle) and to compensate, on the basis of further using Kalman filter to estimate the inertial measurement unit (inertial measurement unit, IMU) error.
7069 进行了转台三轴摇摆和车载行进间验证实验。 For three axis turntable swing and vehicle moving validation experiments.
7070 车载行进间验证实验中,IMU误差估计完成后转入到纯惯性导航,其12h的定位误差为2.12nmile,系统定位精度满足中等精度单轴旋转SINS长时间导航需求。 Vehicle moving validation experiments, IMU error estimation into pure inertial navigation, after the completion of the 12 h of positioning error of 2.12 nmile system positioning precision can satisfy the uniaxial medium accuracy rotation SINS navigation needs for a long time.
7071 针对贝叶斯网络参数的近似等式约束,提出采用正态分布构建该类约束的数学模型; In view of the approximate equality constraint bayesian network parameters, put forward the normal distribution was used to construct mathematical model of this kind of constraint;
7072 然后用Dirichlet分布逼近正态分布,并通过目标优化计算Dirichlet分布的超参数; Then use Dirichlet distribution approaches the normal distribution, and calculate the Dirichlet distributions of parameters are optimized by the target;
7073 最后采用贝叶斯最大后验概率(maximum a posterior,MAP)估计方法计算网络参数值。 Finally using bayesian maximum a posteriori probability (maximum a posterior, MAP) estimation method to calculate network parameter values.