基于MLR的机动平台传感器误差配准算法

 基于固定平台传感器误差极大似然配准(MLR)算法,针对机动平台存在姿态角系统误差的问题,提出了对机动平台传感器系统误差和目标状态进行批处理离线估计的机动极大似然配准(MLRM)算法.该算法利用所有传感器对目标的量测值,通过把传感器量测向目标状态进行投影、对传感器系统误差和目标状态进行期望最大化迭代以及对目标的状态进行融合估计,最终实现量测、姿态角系统误差和目标状态的有效估计.仿真结果表明,该算法迭代收敛速度快,对系统误差估计精度高,对系统误差可观测性较低的配准环境的适应性强并且对传感器姿态角的相关性不敏感,具有很强的工程实用性.     

纬度未知条件下捷联惯导系统晃动基座的初始对准

针对纬度未知条件下捷联惯导系统(SINS)晃动基座的初始对准问题,提出晃动基座下的纬度估计算法和初始对准方法。前者通过惯性坐标系下两个不同时刻的重力加速度向量的夹角来求取纬度;后者利用惯性坐标系下的姿态更新来实时地反映载体在晃动干扰下的姿态变化,结合初始姿态的最优估计实现初始对准。理论分析表明,本文提出的纬度估计算法的误差主要由加速度计误差决定,陀螺误差和晃动干扰对其影响很小。仿真结果表明,本文提出的纬度估计算法和初始对准方法适用于纬度未知条件下晃动基座的初始对准。     

一种基于实时物理闭环校正的空中动基座对准方法

提出了一种基于实时物理闭环校正的平台惯导空中动基座对准方法.该方法利用卡尔曼滤波实现惯导误差的实时估计,根据最优二次型准则设计反馈控制进行惯导平台的实时闭环物理校正,有效保证了对准精度.经机载试验,平台惯导空中动基座对准后,导航精度大幅提高.     

基于CKF的联合扩维误差配准算法

误差配准是多传感器信息融合的基础。为解决机载多平台多传感器的误差配准问题,研究并提出了一种基于容积卡尔曼滤波(CKF)的联合扩维误差配准算法。在算法实现中,首先采用状态矢量维数扩展方法建立非线性滤波框架下的系统误差配准模型,其次根据误差配准模型对各传感器的测量系统误差及各平台的姿态角系统误差进行估计,最后通过CKF滤波实现对状态预测值的修正,改善系统误差对滤波精度的影响。仿真结果表明,所提出的算法能够有效融合利用多传感器的测量信息,实现对多传感器系统误差及目标状态的实时联合精确估计。     

捷联惯导系统动基座初始对准仿真研究

建立了捷联惯导系统动基座初始对准的误差模型，利用分段定常系统可观测性分析理论和方法对系统动基座初始对准时的可观测性进行了全面分析。并采用卡尔曼滤波技术，对系统在各种运动基座初始对准情况下的平台误差角进行了估计，给出了方差仿真曲线，比较了静基座与在这些运动情况下的卡尔曼滤波器的估计效果。仿真结果表明，在捷联惯导系统动基座初始对准过程中，可以通过载体线运动和角运动的改变来提高系统的可观测性和状态变量的可观测度，从而提高估计的精度和速度。这样可以寻求最佳机动方案，有效快速地进行初始对准。     

基于加速度计的轴系垂直度静动基座测量方法

针对目前惯导平台结构垂直度指标在使用过程中难以测量的问题,提出基于加速度计惯性测角原理的静动基座条件下的新的测量算法。其中包括静动基座条件下,由几何模型推导建立的数学模型,相应的实验方法的阐述与论证,以及实验中对上位机采集数据进行离散Kalman滤波处理。最后,建立误差模型,分析了采用该方法测量垂直度的误差来源,通过数值比较,证明了该方法能够在当前技术条件下实现所需测量精度。     

基于卡尔曼滤波器和遗传算法的航空发动机性能诊断

以某型涡扇发动机为研究对象,构建了基于卡尔曼滤波器和遗传算法的航空发动机性能诊断方法。卡尔曼滤波器根据发动机可测参数偏离额定特性时的变化量,对发动机性能参数进行了估计。当传感器存在测量偏差时,会使滤波器估计结果偏离真实情况。遗传算法以机载模型输出与发动机测量参数之间的误差最小为目标,通过优化计算,找出存在测量偏差的传感器,确定其偏差,并最终消除测量偏差对性能诊断的影响。     

旋转式重力梯度仪自身梯度补偿研究

重力梯度仪安装在惯性稳定平台上,忽略载体姿态角改变等条件下的影响,在空间上保持方向不变,因此载体相对于重力梯度仪的旋转会改变其周围空间的质量分布,从而引起自身梯度的变化.这种自身梯度变化影响了重力梯度仪的测量精度,是动基座重力梯度测量误差的重要来源.由于周围环境物体到重力梯度仪的距离很近,采用基于中心引力梯度的方法计算自身梯度具有较大误差.推导了基于加速度计输出的重力梯度仪自身梯度补偿方法.仿真结果表明通过基于中心引力梯度的方法和基于加速度计输出的方法分别计算单位质量的质量点产生的自身梯度时,0.3m位置处自身梯度补偿的偏差超过5E,采用基于加速度计输出的方法进行自身梯度补偿更加精确.     

基于复合地标导航的动平台四旋翼飞行器自主优化降落技术

针对四旋翼飞行器在依靠地标导航完成动平台自主降落任务中存在的目标易丢失、地标定位死区大和降落可靠性差等问题，设计了一种基于复合地标导航的动平台四旋翼飞行器自主优化降落系统。该系统以圆环地标和二维码构成复合地标来解决仅用单一地标定位时存在的定位死区大和定位范围小等问题。针对地标识别丢失、动平台车轮打滑和码盘标定不精确等问题，建立动平台的精确运动模型同时考虑码盘包含未知测量偏差，基于扩展卡尔曼滤波器实现了对动平台连续位姿的在线估计。最终，基于动平台位姿估计结果以最小Jerk指标设计降落轨迹和降落策略，实现了四旋翼飞行器在动平台上高效平稳的降落。为验证所提系统的有效性，设计了仿真和实际降落实验，验证了所提复合地标实现摄像头距离在0.5~6.0 m内的综合完整定位；所设计的动平台状态估计器能在码盘存在未知测量偏差的情况下准确估计出平台的实时位姿，同时所提自主优化降落策略和轨迹规划算法保证了可靠的动平台降落。     

基于卡尔曼滤波-遗传算法的航空发动机性能诊断

以某型涡扇发动机为研究对象,提出了基于卡尔曼滤波器和遗传算法的航空发动机性能诊断方法.根据发动机可测参数偏离额定特性时的变化量,利用卡尔曼滤波器对发动机性能参数进行了估计.当传感器存在测量偏差时,会使滤波器估计结果偏离真实情况.遗传算法以机载模型输出与发动机测量参数之间的误差最小为目标,通过优化计算,找出了存在测量偏差的传感器,确定其偏差,并最终消除了测量偏差对性能诊断的影响.     

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.     

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.     

Adaptive Estimation for a System with Unknown Measu rement Bias

An adaptive state estimator for passive underwater tracking of maneuvering targets is developed. The state estimator is designed specifically for a system containing unknown or randomly switching biased measurements. In modeling the stochastic system, it is assumed that the bias sequence dynamics can be modeled by a semi-Markov process. By incorporating the semi-Markovian concept into a Bayesian estimation technique, an estimator consisting of a bank of parallel, adaptively weighted, Kalman filters has been developed. Despite the large and randomly varying measurement biases, the proposed estimator, provides an accurate estimate of the system states.     

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.     

地空协同防空目标抗差跟踪算法

针对现有系统误差配准算法以已知系统误差变化模型为前提条件、相应的目标状态估计易受系统误差配准结果影响等不足之处,在机载雷达与地基雷达协同防空预警体系下,对系统误差存在情况下的目标跟踪问题进行了研究,并提出了有效的地空协同防空目标抗差跟踪算法。仿真结果表明所提算法可得到无偏、稳定、有效的目标状态估计,并且相对于系统误差目标状态联合估计算法,所提算法计算量小,对系统误差变化有很强的鲁棒性,可适应实际工程应用中可能出现的异常情况,为后续决策提供稳定有效的目标信息。     

Modified input estimation technique using pseudoresiduals

An adaptive tracking filter for maneuvering targets is proposed using modified input estimation technique. Pseudoresiduals are defined using measurements and the velocity estimate at the hypothesized maneuver onset time. With the pseudoresiduals and a new target model representing transitions of nominal accelerations, a new input estimation method for tracking a maneuvering target is derived. Since the proposed detection technique is more sensitive to maneuvers than previous work, the shorter window length can be employed to detect and compensate target maneuvers. Also shown is that the tracking performance of the proposed filter is similar to that of interacting multiple model method (IMM) with 3 models, while computational loads of our method are drastically reduced     

传感器测量偏差下的航空发动机智能性能诊断

以某型涡扇发动机为研究对象, 构建了基于神经网络的航空发动机智能性能诊断方法, 讨论了测量噪声及测量偏差对诊断结果的影响及其处理方法.建立一簇并行的神经网络组和发动机模型, 通过比较各模型输出与发动机测量参数之间的误差, 判断传感器是否存在测量偏差.仿真结果表明, 该方法能有效消除测量噪声, 准确判断并隔离有测量偏差的传感器, 得出正确的发动机性能诊断结果.      

神经网络在发动机自适应建模中的应用研究

提出了一种新的基于神经网络的发动机自适应实时模型的建模方法。建模的思想是认为发动机的任何非额定工作都将导致其输出参数的变化,因而可以把这些参数偏离正常工作参数值的变化量,也就是输出偏离量,用来表征发动机的非额定工作情况。把它们作为增广的状态变量,设计卡尔曼滤波器对其进行最优估计,然后用这些输出偏离量的估计值,通过由BP神经网络训练出来的可测输出偏离量与未测输出偏离量的映射关系来校正机载发动机模型的计算输出,使之与真实发动机的输出一致,从而使实时机载模型获得对任何发动机非额定工况的自适应能力。      

Interacting acceleration compensation algorithm for trackingmaneuvering targets

The two-stage Kalman estimator has been studied for state estimation in the presence of random bias and applied to the tracking of maneuvering targets by treating the target acceleration as a bias vector. Since the target acceleration is considered a bias, the first stage contains a constant velocity motion model and estimates the target position and velocity, while the second stage estimates the target acceleration when a maneuver is detected, the acceleration estimate is used to correct the estimates of the first stage. The interacting acceleration compensation (IAC) algorithm is proposed to overcome the requirement of explicit maneuver detection of the two-stage estimator. The IAC algorithm is viewed as a two-stage estimator having two acceleration models: the zero acceleration of the constant velocity model and a constant acceleration model. The interacting multiple model (IMM) algorithm is used to compute the acceleration estimates that compensate the estimate of the constant velocity filter. Simulation results indicate the tracking performance of the IAC algorithm approaches that of a comparative IMM algorithm while requiring approximately 50% of the computations