Comparison of motion and stereo methods in passive ranging systems

The authors compare the estimates in passive ranging systems using motion and stereo approaches. It is shown that an integrated approach is necessary to provide better range estimates over a field-of-view (FOV) of interest in helicopter flight. The recursive approach for processing a sequence of stereo images, described together with a recursive motion algorithm (RMA), provides the basis for an integrated method to provide more accurate range information. Results based on motion sequences of stereo images are presented     

An Optimally Integrated Track Recovery System for Aerial Bathymetry

An airborne system has been developed for charting shallow coastal and inland waters. The primary components of this system are an aerial survey camera, a profiling laser radar, an analytical stereo plotter, and a multisensor track recovery system (TRS). The TRS comprises a gimbaled inertial navigation system and a number of auxiliary sensors which acquire redundant position and attitude information. The sensor data are combined postmission using a U-D factorized Kalman filter and modified Bryson-Frazier smoother to compute accurate estimates of the orientation parameters of the survey camera at the times of film exposure. These parameters are used to position each overlapping pair of photographs on the analytical plotter to form a stereo image and corresonding analytical stereomodel from which water depth measurements are made. Flight trial results demonstrate that the TRS can achieve radial position and attitude accuracies which exceed 1 m and 2 arcmin root mean square (rms), respectively, and that this level of performance is sufficient to enable water depth measurements to be made to an accuracy of better than 0.65 m (rms).     

A system for obstacle detection during rotorcraft low altitudeflight

An airborne vehicle such as a rotorcraft must avoid obstacles like antennas, towers, poles, fences, tree branches, and wires strung across the flight path. Automatic detection of the obstacles and generation of appropriate guidance and control actions for the vehicle to avoid these obstacles would facilitate autonomous navigation. The requirements of an obstacle detection system for rotorcraft in low-altitude Nap-of-the-Earth (NOE) flight based on various rotorcraft motion constraints is analyzed here in detail. It is argued that an automated obstacle detection system for the rotorcraft scenario should include both passive and active sensors to be effective. Consequently, it introduces a maximally passive system which involves the use of passive sensors (TV, FLIR) as well as the selective use of an active (laser) sensor. The passive component is concerned with estimating range using optical flow-based motion analysis and binocular stereo. The optical flow-based motion analysis that is combined with on-board inertial navigation system (INS) to compute ranges to visible scene points is described. Experimental results obtained using land vehicle data illustrate the particular approach to motion analysis     

卡尔曼/粒子组合滤波在GPS/INS组合导航中的应用研究

在非线性模型和非高斯噪声条件下,粒子滤波在GPS/INS组合导航系统的观测精度较低时能取得较好的滤波结果,但在高观测精度情况下会导致滤波发散。针对这一问题,在分析了基本粒子滤波器算法原理的基础上提出一种卡尔曼/粒子组合滤波方法,将状态向量分为线性部分和非线性部分,分别用卡尔曼滤波和粒子滤波估计,既保证了简化后滤波算法的结果不会变差,又将运算量大大降低,仿真试验表明,组合滤波器能够获得较高的滤波精度,满足实际的导航要求。     

Accurate solution of proportional navigation for maneuveringtargets

An accurate solution is presented of the nonlinear differential equations describing motion under proportional navigation when the target is laterally maneuvering. A quasilinearization (QL) approach is used, followed by a perturbation technique to obtain closed-form solutions for trajectory parameters. An explicit expression for the pursuer lateral acceleration is derived and shown to contain contributions due to initial heading error and target maneuver, with a coupling between the two effects. The solution is shown to be a substantial and consistent generalization or an earlier accurate solution for nonmaneuvering targets and also of classical linear solutions for maneuvering targets. The generalized QL solution presented provides very accurate estimates of pursuer lateral acceleration over a much broader range of engagement geometries and target maneuvers than presently available closed-form solutions     

A relative navigation system for formation flight

A relative navigation system based on both the Inertial Navigation System (INS) and the Global Positioning System (GPS) is developed to support situational awareness during formation flight. The architecture of the system requires an INS/GPS integration across two aircraft via a data link. A fault-tolerant federated filter is used to estimate the relative INS errors based on relative GPS measurements and a range measurement obtained from the data link. The filter is constructed based on a reduced-order model of the relative INS error process. A method for analyzing the filter performance is presented. A case involving two helicopters in formation flight is studied under three different night trajectories to account for the effect of vehicle motion on the INS state transition matrix. The results of the covariance analysis are compared with actual night results over an instrumented test range.     

基于改进容积卡尔曼滤波的惯性/光流组合自主测速方法

针对容积卡尔曼滤波算法在惯性/光流组合测速数据融合时出现由于各系统输出数据频率不一致导致融合精度有限的问题,提出了一种基于多速率残差校正的改进容积卡尔曼滤波算法.通过当前时刻误差估算组合导航系统残差,再使用估算后的残差对速度估计值进行补偿,最终实现惯性/光流组合系统速度测量值的数据融合.实验结果表明,通过提出的改进容积卡尔曼滤波对惯性/光流数据进行融合后,东向速度均方根误差为0.2964m/s,北向速度均方根误差为0.06m/s,与现有其他卡尔曼滤波算法相比,此方法可显著提高惯性/光流组合系统的速度测量精度.     

Recursive estimation of radar biases using electronic charts

This work describes the application of the extended Kalman filter (EKF) to the estimation of range and bearing biases in marine radars by performing a map-matching between the data from hydrographic charts and the radar images. By defining at least two corresponding points from the radar image and the electronic charts, the technique provides a rapid and accurate calibration in range and bearing, giving also estimates for the speed, heading, latitude, and longitude of the ship. The method is tested with simulated data to check convergence and later with real data obtained from a navigation console installed on a patrol boat The technique does not require GPS nor speed information from the ship log unit, however it is shown that their inclusion can improve the estimation.     

惯性/双目视觉里程计深组合导航方法

针对现有视觉里程计测量噪声大、匹配精度低、实时性差的问题,研究一种基于Kalman滤波器的惯性/双目视觉里程计组合导航方法.在视觉里程计中引入惯性导航信息,辅助完成实时图截取、搜索区预测、输出速度校正等功能,提高视觉里程计的测量精度与计算速度.利用Kalman滤波器,实现视觉里程计对惯导累计误差的修正,提升组合导航系统的导航精度.车载试验结果表明,惯性/双目视觉里程计深组合导航的实时定位精度优于0.5％D (CEP),具备工程应用条件.     

A Tracking Filter in Spherical Coordinates Enhanced by De-noising of Converted Doppler Measurements

Tracking problem in spherical coordinates with range rate (Doppler) measurements, which would have errors correlated to the range measurement errors, is investigated in this paper. The converted Doppler measurements, constructed by the product of the Doppler measurements and range measurements, are used to replace the original Doppler measurements. A de-noising method based on an unbiased Kalman filter (KF) is proposed to reduce the converted Doppler measurement errors before updating the target states for the constant velocity (CV) model. The states from the de-noising filter are then combined with the Cartesian states from the converted measurement Kalman filter (CMKF) to produce final state estimates. The nonlinearity of the de-noising filter states are handled by expanding them around the Cartesian states from the CMKF in a Taylor series up to the second order term. In the mean time, the correlation between the two filters caused by the common range measurements is handled by a minimum mean squared error (MMSE) estimation-based method. These result in a new tracking filter, CMDN-EKF2. Monte Carlo simulations demonstrate that the proposed tracking filter can provide efficient and robust performance with a modest computational cost.     

Model-based robust estimation and fault detection for MEMS-INS/GPS integrated navigation systems

In micro-electro-mechanical system based inertial navigation system（MEMS-INS）/global position system（GPS） integrated navigation systems, there exist unknown disturbances and abnormal measurements. In order to obtain high estimation accuracy and enhance detection sensitivity to faults in measurements, this paper deals with the problem of model-based robust estimation（RE） and fault detection（FD）. A filter gain matrix and a post-filter are designed to obtain a RE and FD algorithm with current measurements, which is different from most of the existing priori filters using measurements in one-step delay. With the designed filter gain matrix, the H-infinity norm of the transfer function from noise inputs to estimation error outputs is limited within a certain range; with the designed post-filter, the residual signal is robust to disturbances but sensitive to faults. Therefore, the algorithm can guarantee small estimation errors in the presence of disturbances and have high sensitivity to faults. The proposed method is evaluated in an integrated navigation system, and the simulation results show that it is more effective in position estimation and fault signal detection than priori RE and FD algorithms.     

基于对偶数的航天器多特征融合相对导航算法

针对基于视觉的航天器相对导航问题,利用对偶数推导并给出了航天器相对耦合动力学方程,该方程一体化描述了追踪航天器相对于目标航天器的姿态运动和轨道运动,且考虑了由非质心点引起的相对姿态与相对轨道之间的耦合影响。在对偶代数的框架内,统一描述了目标航天器上的特征点和特征线,并基于特征点、线在像平面的投影建立了多特征融合的单目视觉测量模型。最后通过对系统状态方程以及测量方程的线性化,应用迭代扩展卡尔曼滤波(IEKF)算法对非质心点的相对运动状态进行了估计。仿真结果表明,本文的算法能够对航天器非质心点的相对运动状态进行较高精度的估计。     

导航星座自主导航技术--自主星历更新

自主星历更新是导航星座自主导航的关键技术之一,包括卫星轨道精密确定和轨道短时预报两个方面的内容。本文在系统地论述导航星座自主导航的信息处理流程的基础上,重点提出星座卫星轨道精密确定的自适应Kalman滤波算法。系统仿真结果表明：通过星间双向测量数据的滤波处理,能够获得高精度的卫星星历和较小的用户测距误差（URE）,满足高精度导航定位需求,初步证明导航星座自主导航信息处理流程及其星历更新算法的合理性和可行性。     

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.     

用于SAR运动补偿的DGPS/SINS组合系统研究

使用考虑位置误差相关项的伪距率观测模型 ,研究了用于合成孔径雷达运动补偿的差分 GPS/ SINS伪距率组合系统。结果表明 ,组合系统的长期位置精度能达到 1 m左右。 GPS数据更新率低于 INS,在 GPS测量时间间隔内 ,组合系统的性能仅由 INS决定。虽然 INS误差随时间积累 ,在 GPS数据更新率为 1 s的情况下 ,即使采用中等精度的惯性仪表 ,其相对位置精度为厘米级 (这里相对位置精度指组合系统在 GPS测量时间间隔内位置误差的变化范围)。     

基于平面模板自由拍摄的双目立体测量系统的现场标定

 摄像机标定是基于光学摄像的立体测量技术中的一个十分重要的步骤，标定精度直接影响系统测量的精度和稳定性。本文提出并实现了一种简便易行的双目立体测量系统现场标定方法：利用一块特殊设计的具有不同大小圆形特征点的平面标定板，在无需控制任何运动参数的情况下，双目测量系统只需对标定板在不同角度自由拍摄一组图像即可方便地实现系统标定。该方法在已有单摄像机标定算法的基础上，加入对双摄像机相对位置和姿态的优化，同时考虑了镜头的非线性畸变，达到了较好的标定结果。另外，提出的图像点与其空间点的对应算法具有良好的稳定性，即使由于环境或摄像机摆放位置等因素的影响，一些标定板上的特征点不能被摄像机拍摄到，或者不能被正确识别时，仍然能进行标定工作。标定实验和系统标定后的三维重建结果验证了该方法的有效性。     

A Fault-tolerance Estimating Method for Ionosphere Corrections in Satellite Navigation System

 Aiming to the reliable estimates of the ionosphere differential corrections for the satellite navigation system in the presence of the ionosphere anomaly, a fault-tolerance estimating method, which is based on the distributed Kalman filtering, is proposed. The method utilizes the parallel sub-filters for estimating the ionosphere differential corrections. Meanwhile, an infinite norm (IN) method is proposed for the detection of the ionosphere irregularity in the filter processing. Once the anomaly is detected, the sub-filter contaminated by the anomaly measurements will be excluded to ensure the reliability of the estimates. The simulation is conducted to validate the method and the results indicate that the anomaly can be found timely due to the novel fault detection method based on the infinite norm. Because of the parallel sub-filter architecture, the measurements are classified by the spatial distribution so that the ionosphere anomaly can be positioned and excluded more easily. Thus, the method can provide the robust and accurate ionosphere differential corrections.     

INS/GNSS组合导航系统空中快速对准方法

针对机载惯性/全球导航卫星系统(INS/GNSS)组合导航系统地面静基座对准时间较长、航向对准精度较低以及惯导空中故障重启后无法快速得到精确姿态信息重新进入导航状态等问题,提出一种快速初始对准方法。该对准方法基于惯性导航比力方程,利用GNSS的定位、测速信息与惯性测量组件(IMU)的输出信息解算载体姿态信息,并结合遗传-牛顿算法与求和自回归滑动平均(ARIMA)模型卡尔曼滤波信号降噪技术提高姿态信息的解算精度。基于实测飞行数据的解算验证了该方法的有效性、对准精度以及在实际工程应用中的优越性。     

Estimation of Aircraft Target Motion Using Orientation Measurements

A new approach to estimating motion of a highly maneuverable aircraft target in an air-to-air tracking scenario is presented. An interactive filter system is developed that provides an improved estimate of target motion states by conditioning kinematic filter estimates on target aspect angle data. Pattern recognition techniques used with an electrooptical tracker are presumed to provide this target aspect information. A target orientation filter processes the aspect angle measurements by statistically weighting measured aspect angles with the current best estimate of target kinematics. The aerodynamic lift equation is used to relate approximate angle of attack to target velocity and acceleration. A novel statistical model for aircraft target normal acceleration is also developed to represent better the unknown target accelerations. Simulation results of realistic three-dimensional scenarios are presented to evaluate the performance of the interactive filter system.     

Interlaced Kalman filtering of 3D angular motion based on Euler'snonlinear equations

A novel Kalman filtering technique is presented that reduces the mean-square-error (MSE) between three-dimensional (3D) actual angular velocity values and estimated ones by an order of magnitude (when compared with the MSE resulting from direct measurements) even under extremely low signal-to-noise ratio conditions. The filtering problem is nonlinear in nature because the dynamics of 3D angular motion are described by Euler's equations. This nonlinear set of differential equations state that the angular acceleration in one axis is proportional to the torque applied to that axis, and to the products of angular velocity components in the other two axes of rotation. Instead of using extended Kalman filtering techniques to solve this complex problem, the authors developed a new approach where the nonlinear Euler's model is decomposed into two pseudolinear models (primary and secondary). The first model describes the time progression of the state vector containing the linear terms, while the other characterizes the propagation of the state vector containing the nonlinearities. This makes it possible to run two interlaced discrete-linear Kalman filters simultaneously. One filter estimates the values of the state vector containing the linear terms, while the other estimates the values of the state vector containing the nonlinear terms in the system. These estimates are then recombined, solving the nonlinear estimation process without linearizing the system. Thus, the new approach takes advantage of the simplicity, computational efficiency and higher convergence speed of the linear Kalman filter form and it overcomes many of the drawbacks typical of conventional extended Kalman filtering techniques. The high performance and effectiveness of this method is demonstrated through a computer simulation case study