多传感器数据融合中的数据配准研究

数据配准是多传感器数据融合的先期处理过程。本文在研究了传统的最小二乘算法的基础上，提出了基于三维坐标系中的极大似然配准算法，解决了最小二乘配准算法对于传感器相距较远时无法解决的问题。最后通过计算机仿真证明了此算法的有效性。     

Observability of an integrated GPS/INS during maneuvers

An observability analysis of a GPS/INS system during manoeuvers is presented based upon a perturbation model with respect to the Earth-centered-Earth-fixed (ECEF) coordinate system. Analysis is performed on two types of manoeuvers, linear acceleration and steady turn. These manoeuvers could be used for in-flight INS alignment using GPS. During the constant linear acceleration without rotation relative to the Earth, the linear system model is shown to be time-invariant. The observability analysis for the time invariant linear system model shows that linear acceleration does not change the number of observable modes but rather the structure of the observable space. For a nonconstant linear acceleration or a steady turn, the perturbation linear system becomes time varying. For this time-varying system, three types of observability are considered, complete, differential, and instantaneous observability. Instantaneous observability is the strongest properties and means that the state of the system at any time may be determined instantaneously from observation of the output and its derivatives. Instantaneous observability is important for fast in-flight INS alignment. It is shown that the number of instantaneously observable moded is increased by at least 2 during a maneuver. Hence, some linear combinations of the attitude angles become instantaneously observable.     

基于模糊奇偶方程的非线性系统传感器故障诊断

 给出了一种非线性系统传感器的故障诊断方法。该方法将T-S 模糊模型、全解耦奇偶方程和参数估计相结合,同时对非线性系统的多个传感器的故障进行检测、隔离与识别。设计出用于产生残差的线性系统全解耦奇偶方程,并给出了全解耦奇偶向量的存在条件,全解耦奇偶方程产生的残差仅对一个传感器故障敏感,而对系统状态、扰动输入和其它传感器输出解耦。引入T-S 模型将全解耦奇偶方程推广到非线性系统中得到了模糊奇偶方程。传感器的故障模型表示为刻度因子和偏差的形式,根据残差信息应用卡尔曼估计方法可识别出故障模型的参数。最后给出了某型号飞机控制系统传感器的故障诊断仿真实例。     

A Polynomial Prediction Filter Method for Estimating Multisensor Dynamically Varying Biases

The estimation of the sensor measurement biases in a multisensor system is vital for the sensor data fusion. A solution is provided for the estimation of dynamically varying multiple sensor biases without any knowledge of the dynamic bias model pa- rameters. It is shown that the sensor bias pseudomeasurement can be dynamically obtained via a parity vector. This is accom- plished by multiplying the sensor uncalibrated measurement equations by a projection matrix so that the measured variable is eliminated from the equations. Once the state equations of the dynamically varying sensor biases are modeled by a polynomial prediction filter, the dynamically varying multisensor biases can be obtained by Kalman filter. Simulation results validate that the proposed method can estimate the constant biases and dynamic biases of multisensors and outperforms the methods reported in literature.     

Exact multisensor dynamic bias estimation with local tracks

An exact solution is provided for the multiple sensor bias estimation problem based on local tracks. It is shown that the sensor bias estimates can be obtained dynamically using the outputs of the local (biased) state estimators. This is accomplished by manipulating the local state estimates such that they yield pseudomeasurements of the sensor biases with additive noises that are zero-mean, white, and with easily calculated covariances. These results allow evaluation of the Cramer-Rao lower bound (CRLB) on the covariance of the sensor bias estimates, i.e., a quantification of the available information about the sensor biases in any scenario. Monte Carlo simulations show that this method has significant improvement in performance with reduced rms errors of 70% compared with commonly used decoupled Kalman filter. Furthermore, the new method is shown to be statistically efficient, i.e., it meets the CRLB. The extension of the new technique for dynamically varying sensor biases is also presented.     

Multisensor multitarget bias estimation for general asynchronous sensors

A novel solution is provided for the bias estimation problem in multiple asynchronous sensors using common targets of opportunity. The decoupling between the target state estimation and the sensor bias estimation is achieved without ignoring or approximating the crosscovariance between the state estimate and the bias estimate. The target data reported by the sensors are usually not time-coincident or synchronous due to the different data rates. Since the bias estimation requires time-coincident target data from different sensors, a novel scheme is used to transform the measurements from the different times of the sensors into pseudomeasurements of the sensor biases with additive noises that are zero-mean, white, and with easily calculated covariances. These results allow bias estimation as well as the evaluation of the Cramer-Rao lower bound (CRLB) on the covariance of the bias estimate, i.e., the quantification of the available information about the biases in any scenario. Monte Carlo simulation results show that the new method is statistically efficient, i.e., it meets the CRLB. The use of this technique for scale and sensor location biases in addition to the usual additive biases is also presented.     

星基ADS与雷达误差校准算法的研究

基于星基自动相关监视(ADS)系统和雷达各自的特点,分析了空中交通管制中ADS与雷达数据融合的模型,提出了在传统的地心地固(ECEF)坐标系中星基ADS和雷达系统误差校准的算法。同时,针对ADS数据的特性,又进一步提出了在Geodetic坐标系和以雷达站为极点的极坐标系(以下简称雷达站极坐标系)中的系统误差校准算法。最后,对校准效果进行了仿真分析和比较,并介绍了一种有效应用上述算法的方法。     

ATS-6 Interferometer

The Applications Technology Satellite-6 (ATS-6) RF interferometer is utilized primarily as a precision 3-axis attitude sensor having an unambiguous field of view of 350°. This function requires two separated ground transmitters, each using one of the two available frequency channels or sharing a single channel by time multiplexing. For 3-axis control, one uplink transmitter can provide 2-axis attitude (pitch and roll) with other sensors (e.g., a Polaris tracker) providing yaw attitude. By utilizing two uplink transmitters and the Earth sensor or three time multiplexed uplink transmitters, the interferometer can also provide measurements of ATS-6 spacecraft orbit position. Uplink frequencies are 6.150 and 6.155 GHz. The receiving antennas are spaced at 19.95 wavelengths (?) for the vernier baseline and 1.66 ? for the coarse baseline. Spacecraft system weight is 8.39 kg (18.5 lb) and power requirement is 15.5 W. Flight evaluation results are given for the interferometer including R F link budgets, modulation of uplink carrier, signal-to-noise ratio, and dropout behavior. A hardware calibration model is described, containing major biases in the phase measurements. Techniques for flight calibration as both an attitude and spacecraft position sensor are outlined . Flight testing has shown that on-line calibration of receiver/converter biases must be performed on a short term routine basis. Interferometer resolution was found to be 0.00140 space angle with negligible noise (jitter) at transmitted power levels above 72 dBW. As an attitude sensor, the interferometer has demonstrated the ability to provide stabilization to better than 0.     

基于不敏变换的动基座传感器偏差估计方法

提出了一种新的基于合作目标的动基座传感器误差绝对配准方法。该方法利用所获得的合作目标位置信息,将载体平台姿态角偏差转换为传感器测量偏差中的一部分,并建立偏差状态方程和测量方程。在此基础上,采用广义最小二乘方法以实现传感器测距误差的估计,不敏滤波的方法则用于实现平台载体的姿态偏差和角度测量偏差的实时估计。仿真结果表明,该方法实现简单,收敛速度快,可以实现单部动基座传感器的偏差估计。     

机载雷达目标跟踪与航迹信息提取

针对机动目标跟踪中航迹信息提取精度不高的问题,提出一种ECEF坐标系下基于交互多模型的多机协同跟踪算法。首先,各载机以ECEF坐标系为融合中心对目标量测进行无偏转换处理,以有效减小量测转换误差对目标跟踪的影响;然后,利用交互多模型的方法对目标进行融合跟踪,以进一步提高目标机动时的跟踪精度;最后,通过二次滤波的方法,来有效实现目标航迹信息的精确提取。仿真结果表明,该算法可较好地提高目标机动时的跟踪精度和航迹信息提取精度。     

基于高斯混合模型的航迹抗差关联算法

针对雷达系统误差时变、上报目标不完全一致等复杂场景下目标航迹关联问题，采用高斯混合模型（GMM）与航迹间拓扑信息相结合的方法实现航迹抗差关联。将航迹关联问题转化为图像匹配中的非刚性点集匹配问题，建立对非同源航迹具有鲁棒性的高斯混合模型，根据航迹间的邻域拓扑信息决定高斯混合模型中各高斯组成部分的权重，利用期望最大值（EM）算法求解高斯混合模型的最优闭合解，在期望步（E-step）阶段求解航迹的对应关系，在最大化步（M-step）阶段求解非同源航迹比例，最后进行航迹关联判决以获得关联结果。仿真结果表明，该算法在不同系统误差、目标分布密度、探测概率等环境下具有较好有效性和鲁棒性。     

基于视觉的惯性导航误差在线修正

陀螺零偏和加速度计零偏是影响惯性测量单元(IMU)积分精度的重要因素。提供一组精确的实时的零偏估计可以提高IMU的积分精度,为视觉导航提供良好的位姿预测,提高整个系统的动态性能。通过合理地建立IMU的噪声模型以及IMU和视觉的组合方程,利用一种基于李群和李代数知识的IMU预积分方法将零偏进行合理的线性化,运用Kalman滤波进行IMU零偏的在线估计。实验结果表明,通过本文的修正方法,惯性导航的平均积累误差由0.034m/s提高到0.0037m/s,精度明显提高。     

地固系GNSS/SINS组合导航正常重力间接算法

相比导航系,GNSS/SINS组合导航在地固系计算,编排效率提高约40％,Kalman滤波效率提高约10％,即使正常重力与姿态矩阵的计算效率偏低,在地固系计算比导航系计算综合效率也提高约30％.针对地固系正常重力计算复杂的问题,提出了地固系正常重力间接算法,首先把三维直角坐标转换为大地坐标,然后在导航系计算正常重力,最后把正常重力转换到地固系,姿态矩阵也在此过程计算完毕.试验结果表明,间接法与传统方法计算精度相当,但间接法计算过程简单直观,计算效率可以提高3％ ～ 26％,尤其对于高速高精度的捷联惯导,效率提高尤为明显,具有重要的工程实用价值.     

基于ECEF的广义最小二乘误差配准技术

 雷达组网数据处理首先要进行误差配准,来准确地估计和消除系统误差。传统的误差配准技术多基于球极投影,当雷达之间距离较远时,给配准结果引入一定的误差。基于地球中心坐标系(ECEF),提出了一种广义最小二乘的ECEF-GLS误差配准技术,较好地解决了远距离误差配准问题,误差分析表明,如果忽略模型线性化引入的误差,配准结果达到了CRLB下限。最后,使用仿真数据验证了算法的性能,并和Zhou提出的基于ECEF坐标系的最小二乘ECEF-LS误差配准算法进行了比较。     

In-Flight Parity Vector Compensation for FDI

The performance of a failure detection and isolation (FDI) algorithm applied to a redundant strapdown inertial measurement unit (IMU) is limited by sensor errors such as input axis misalignment, scale factor errors, and biases. A techique is presented for improving the performance of FDI algorithms applied to redundant strapdown IMUs. A Kalman filter provides estimates of those linear combinations of sensor errors that affect the parity vector. These estimates are used to form a compensated parity vector which does not include the effects of sensor errors. The compensated parity vector is then used in place of the uncompensated parity vector to make FDI decisions. Simulation results are presented in which the algorithm is tested in a realistic flight environment that includes vehicle maneuvers, the effects of turbulence, and sensor failures. The results show that the algorithm can significantly improve FDI performance, especially during vehicle maneuvers.     

DGPS/IMU integration-based geolocation system: Airborne experimental test results

This paper addresses differential global positioning system (DGPS)/inertial measurement unit (IMU) integration-based geolocation system developed for airborne remote sensing cameras. First, we provide a brief review on sensor calibration, alignment and sensor fusion as background material of this research. After presenting those background material, as a main part of this paper we present a geolocation algorithm designed for an airborne imaging system. The geolocation system developed is tested through actual airborne experiments. For the verification of the geolocation system developed, we compare initial stationary states of the airplane before-taking off with states after-landing. From the actual test results, we find that it is critical to do an accurate time synchronization between IMU, DGPS, and airborne images, and to compensate for the data delay occurred during the network transfer.     

基于固定区间最优平滑的传递对准精度评估

惯导系统传递对准是战术导弹发射前必须完成的任务,其对准精度直接影响导弹系统的制导精度。为评估传递对准精度性能,在介绍传递对准精度评估原理的基础上,研究了Kalman固定区间最优平滑算法;并从理论上分析了顺序滤波与逆序平滑的关系,得出平滑依赖于滤波的结论。通过实测数据半物理仿真试验证明了理论分析的正确性,表明固定区间最优平滑算法能有效地评估传递对准精度,并与间接法对比得出间接法与最优平滑算法具有一致性。     

Wavelet de-noising for IMU alignment

Inertial navigation system (INS) is presently used in several applications related to aerospace systems and land vehicle navigation. An INS determines the position, velocity, and attitude of a moving platform by processing the accelerations and angular velocity measurements of an inertial measurement unit (IMU). Accurate estimation of the initial attitude angles of an IMU is essential to ensure precise determination of the position and attitude of the moving platform. These initial attitude angles are usually estimated using alignment techniques. Due to the relatively low signal-to-noise ratio of the sensor measurement (especially for the gyroscopes), the initial attitude angles may not be computed accurately enough. In addition, the estimated initial attitude angles may have relatively large uncertainties that may affect the accuracy of other navigation parameters. This article suggests processing the gyro and accelerometer measurements with multiple levels of wavelet decomposition to remove the high frequency noise components. The proposed wavelet de-noising method was applied on a navigational grade inertial measurement unit (LTN90-100). The results showed that accurate alignment procedure and fast convergence of the estimation algorithm, in addition to reducing the estimation covariance of the three attitude angles, could be obtained.     

Kalman Filter Design Considerations for Space-Stable Inertial Navigation Systems

Terrestrial inertial navigation is typically performed using an instrumented platform stabilized in a ?local-level? configuration for convenient generation of geographic navigation information. The local-level geographic reference must be maintained by torquing the system gyros, a requirement which may be incompatible with high-precision inertial sensors currently under development. Gyro torquing in a gimballed navigation system can be avoided by employing a ?space-stable? mechanization of the platform where an inertial, rather than geographic, reference is used for navigation calculations. The software design problems associated with this concept, especially those related to the application of Kalman filtering, are the principle focus of this paper. Although the space-stable configuration has been used extensively for spacecraft navigation and guidance, it has not been widely applied to terrestrial navigation, either for air or marine applications. The chief problem in this application is to perform navigation in local-level coordinates, using system outputs generated in an inertial reference frame. It can be demonstrated that, although the navigation system error dynamics are identical for the local-level and space-stable configurations, the dynamics of the sensor errors which drive these systems are quite different. These differences in sensor error propagation characteristics impose new requirements for the design of procedures to accomplish system calibration, alignment, and reset. This paper outlines a Kalman filtering approach which is applicable to all of the above procedures, and presents numerical results to demonstrate its effectiveness.     

多个MEMS惯性姿态模块同时标定方法研究

针对MEMS 惯性姿态模块的应用需求, 根据已有的MEMS 三轴加速度计和 三轴陀螺仪的零偏、标度因子和非正交等误差及其随温度的变化模型, 设计了多个 MEMS 惯性姿态模块误差同时标定的方法,该方法可实现多个模块传感器数据的同步采 集,在常温下可对多个MEMS 惯性姿态模块的非正交误差进行批量标定,在全温度范围 内同时标定多个模块的温度漂移误差。试验表明,该方法校正了MEMS 惯性传感器的非 正交误差和温度漂移误差,提高了MEMS 惯性传感器的精度,同时提高了标定的效率, 减少了标定成本,有利于工程实现。