ID 原文 译文
55387 传统的姿态匹配传递对准需要主、子惯性测量单元(inertial measurement unit,IMU)分别惯导解算,得到两个姿态向量或姿态阵构造观测量。 The traditional posture matching transfer alignment to the Lord, the son of inertial measurement unit (inertial measurement unit, IMU) respectively for calculating inertial navigation, vector or get two stance array structure observation.
55388 提出利用主、子IMU输出直接解算主、子IMU之间的相对姿态来构造匹配量的一种新的传递对准方法。 Proposed by the Lord, the son of IMU output directly calculating Lord, son of IMU of relative attitude to construct the matching between a new method of transfer alignment.
55389 首先推导了主、子相对姿态矩阵微分方程,并给出求解方法,得到的相对姿态矩阵计算值与初始相对姿态矩阵相乘作为观测信息;然后推导建立了相对姿态的误差方程,由此给出了传递对准状态空间模型;最后讨论建立了仿真验证环境,仿真结果表明,子陀螺精度为1(°)/h时,子IMU对准精度可以达到3′以内,并在10s内迅速收敛。 First son, god the relative attitude matrix differential equation was deduced, and the solution is given, and the relative attitude matrix calculated value and the initial relative attitude matrix multiplication as observation information;And then establish the relative attitude error equation is derived, which gives the transfer alignment state space model;Finally set up the simulation environment, simulation results show that the child gyro accuracy of 1 °)/h, the son of IMU alignment accuracy can be achieved within 3 ', and rapid convergence within 10 s.
55390 该方法同时适用于精确确定大角度或小角度的子IMU相对姿态,为关键位置实时高精度姿态基准的建立提供了技术参考。 The method also applies to accurately determine the large Angle or small Angle of sub company relative attitude, for the establishment of the key position in real time and high precision attitude benchmark provides technical reference.
55391 方位超分辨是国内外雷达界长期以来持续探索的一项技术难题,针对求解过程中所遇到的病态性,本文深入研究了范数正则化方法。 Bearing super resolution is LeiDaJie both at home and abroad for a long time continuous exploration of a technical problem, in view of the encountered in the process of solving ill-posed, this paper deeply studied the norm regularization method.
55392 利用L2范数正则化方法对方位超分辨模型进行求解,针对L2范数正则化方法的不足并考虑到目标信号的稀疏性质,建立了L1范数正则化模型。 Using the L2 norm regularization method to solve each other a super resolution model, aiming at the shortcomings of the L2 norm regularization method and considering the nature of target signal sparse, L1 norm regularization model was established.
55393 为提高求解的计算效率,将其转化为半定规划模型,用预测校正原对偶路径跟踪法进行求解。 In order to improve the calculation efficiency of solving, turning it into semidefinite programming model, with forecast correction tracking method for solving the dual path.
55394 针对不同信噪比情况进行了计算机仿真,初步结果表明,两种方法都能用于雷达方位超分辨,且在相同条件下L1范数正则化方法分辨性能更好,具有较强的噪声适应能力,在信噪比低至0dB时,分辨力提高1.7倍。 Computer simulation is carried out for different SNR situation, the preliminary results show that both methods can be used in radar azimuth super resolution, and in the same conditions the L1 norm regularization method to distinguish better performance, strong adaptability to noise and the signal to noise ratio as low as 0 db, resolution increased by 1. 7 times.
55395 狼群算法(wolf pack algorithm,WPA)源于狼群在捕食及其猎物分配中所体现的群体智能,已被成功应用于复杂函数求解。 Wolves algorithm (Wolf pack algorithm, WPA) from wolves embodied in the predator and its prey distribution of swarm intelligence, has been successfully applied to solving complex function.
55396 在此基础上,通过定义运动算子,对人工狼位置、步长和智能行为重新进行二进制编码设计,提出了一种解决离散空间组合优化问题的二进制狼群算法(binary wolf pack algorithm,BWPA)。 Operator based on this, by defining the movement, position of artificial Wolf, step length and intelligent behavior to binary code design, this paper proposes a binary wolves to solve the problem of discrete space combination optimization algorithm (binary Wolf pack algorithm, BWPA).