Quadratic extended Kalman filter approach for GPS/INS integration

GPS/INS integration system has been widely applied for navigation due to their complementary characteristics. And the tightly coupled integration approach has the advantage over the loosely coupled approach by using the raw GPS measurements, but hence introduces the nonlinearity into the measurement equation of the Kalman filter. So the typical method for navigation using measurements of range or pseudorange is by linearizing the measurements in an extended Kalman filter (EKF). However, the modeling errors of the EKF will cause the bias and divergence problems especially under the situation that the low quality inertial devices are included. To solve this problem, a quadratic EKF approach by adding the second-order derivative information to retain some nonlinearities is proposed in this paper. Simulation results indicate that the nonlinear terms included in the filtering process have the great influence on the performance of integration, especially in the case that the low quality INS is used in the integrated system. Furthermore, a two-stage cascaded estimation method is used, which circumvents the difficulty of solving nonlinear equations and greatly decreases the computational complexity of the proposed approach, so the quadratic EKF approach proposed in this paper is of great value in practice.     

相控阵雷达导引头波束稳定技术研究

 相控阵雷达导引头已成为新型空空导弹制导系统的首选,相控阵雷达导引头应用在空空导弹上需要首先解决导引头波束稳定的技术难题。本文基于波束指向在惯性空间不变性原理提出一种相控阵雷达导引头波束稳定算法,对影响相控阵雷达导引头隔离度性能的主要因素进行了仿真分析。仿真结果表明:该算法可以实现相控阵雷达导引头波束惯性空间指向稳定并且满足工程应用要求;通过减小速率陀螺数据周期和波束控制周期,及引入速率陀螺传输延时补偿算法可以提高导引头的隔离度性能。     

高超声速飞行器惯性/天文鲁棒组合导航方法

对于高超声速飞行而言,惯性/天文组合导航的应用将会面临异常值干扰和噪声特性变化等问题,需要控制天文量测中异常值扰动的影响,提高对于可靠量测信息的利用效率。为此,本文研究了一种基于平衡因子的组合导航方法,采用鲁棒滤波方法进行惯性/天文组合导航系统的量测更新,将自适应滤波技术引入到鲁棒滤波中,考虑到天文量测信息不同的噪声特性和水平,将自适应因子分解为姿态自适应因子和位置自适应因子,从而平衡状态预测信息和天文测量信息的贡献。仿真结果表明,本文方法可以显著提高高动态环境影响下的组合导航性能。     

Novel attitude determination method by integration of electronic level meter,INS, and low-cost turntable for level attitude evaluation and calibration of INS

Attitude references are greatly needed for the evaluation and calibration of Inertial Navigation Systems (INSs), which are widely used in gravimeter, marine, and aeronautical navigation. High-accuracy turntable, INS, and Global Navigation Satellite System have been utilized to verify the performance of relatively low-accuracy INS. The accuracy requirement of the attitude reference continuously increases with the rapid improvement of inertial sensors and navigation algorithms. However, the cost of attitude determination system increases rapidly with the increase of attitude accuracy requirement. To solve this limitation, the integration of level meter, INS, and low-cost turntable is proposed to provide level attitude, such as roll and pitch. The turntable is utilized to rotate the INS. An integration model of the level meter and INS is built to estimate the level attitude and reduce the cost of the turntable. The proposed method successfully avoids the dependence on high-accuracy turntables. An observability degree analysis is conducted to improve the level attitude accuracy further. The simulation and turntable test results indicate that the proposed method can provide high-accuracy level attitude without high-accuracy INS or turntable and is applicable to error calibration and attitude evaluation of INS.     

一种无引北三轴转台标定捷联惯组方法

用三轴转台标定捷联惯组时，需要对三轴转台引北，用于精准测量捷联惯组相对地球北向的角度。为了减少引北带来的繁重工作量，在研究传统六位置标定方法的基础上，设计了十二位置标定方法。给出了十二位置翻转编排，建立了捷联惯组器件误差参数模型，详细推导了求解误差参数的方程式，并在高精度三轴转台上进行了试验验证。结果表明，在对三轴转台无引北的情况下，十二位置标定方法仍能标定出捷联惯组的24个器件误差参数，且精度与六位置标定方法相当，达到了预期目的。     

基于场景检测的城市环境GNSS/INS组合定位方法研究

城市复杂环境下,采用GNSS/INS组合定位方法能有效提升GNSS信号部分或全部被遮挡情况下的车辆连续定位能力。GNSS信号受遮挡的程度具有随机性和突变性,给GNSS/INS组合算法的架构设计和参数设置带来了挑战。基于此,设计了一套基于场景检测的GNSS/INS组合定位策略与方法,提出了以多星GNSS观测量的八类特征为输入,利用支持向量机分类思想将定位场景分为室外开阔、室外遮挡和室内三种类型,并建立了与之相适应的组合滤波量测误差估计模型,进而实现了GNSS/INS组合定位。实测数据验证表明,提出的方法能够有效提升车辆组合定位精度。     

基于DKF的IMU误差预测算法

作为导航领域常用的组合导航方式,全球导航卫星系统(GNSS)/惯性导航系统(INS)组合导航在GNSS信号失锁后,由于惯性测量单元(IMU)误差随时间迅速积累,其定位结果会偏离载体真实位置,导航精度下降.针对此问题,提出了一种长短期记忆网络(LSTM)辅助的算法,称之为深度卡尔曼滤波(DKF)算法.DKF算法的核心思想是使用LSTM训练IMU误差模型,然后通过训练出的模型预测IMU误差,最后将预测的IMU误差代入IMU数据以校正导航结果.仿真结果表明:在200s测试数据上,DKF算法将误差从1.1537m/s降低到0.3746m/s.与平均预测、卡尔曼预测和最小二乘估计等方法相比,DKF算法的误差最小,具有更优越的导航性能.     

基于联邦滤波的多传感器管道定位方法CSCD

由低成本微惯性测量单元(MIMU)和里程轮组合的管道定位系统在里程轮出现打滑情况下,组合定位误差将快速发散。为解决里程轮打滑时量测信息失效的问题,采用光电测速传感器作为冗余测速传感器,利用联邦滤波对MIMU、里程轮和光电测速传感器多源信息进行融合,并基于预测残差设计了自适应信息分配因子,实现了不同特性传感器的最优融合。仿真实验结果表明,提出的多传感器管道定位方法可以降低里程轮打滑导致的管道定位误差,对于120m长的仿真管道,定位误差为管线长度的0.015%。     

惯导系统结构拓扑优化方法研究

针对惯导系统结构拓扑优化问题,提出了一种考虑多工况组合和等效边界处理的优化方法.以惯导系统结构件为研究对象,梳理了不同结构件的载荷工况;以刚度最大为 目标函数,以体积分数、结构频率和最大变形等作为约束条件,研究了变密度拓扑优化方法;以某惯导系统的盖板、惯性台体和外壳体3个结构件为例,进行了优化方法验证.结果表明,与传统设计相比,在满足系统刚度、频率及动态特性的前提下,优化后的3个结构件质量分别减小18.3％、22.6％和18.0％,整体质量减小18.7％,实现了惯导系统结构的轻量化设计.