The Processing of Band-Limited Measurements; Filtering Techniques in the Least Squares Context and in the Presence of Data Gaps

This paper discusses the treatment of correlated measurements in the least squares context. We focus on the processing of band-limited measurements and on long time series with a constant sampling interval. Time domain as well as frequency domain approaches were discussed to offer different ways to integrate the filtering process into the optimization scheme as good as possible. The focus was on long equispaced data sets. The application of discrete filters in the space domain makes it possible to decorrelate the observations during data acquisition. This opens the way to a sequential adjustment procedure, where the design matrix is treated row-by-row. Huge systems with millions of observations can be solved by direct or iterative strategies, and both approaches benefit from well-tailored filter techniques. Because of the sequential access the computational effort of this giant task can be easily distributed to a cluster of parallel processors and offers, in addition, the possibility to treat data gaps in a straightforward way. This revised version was published online in August 2006 with corrections to the Cover Date.     

基于复合优化的航空发动机模型参数估计

本文提出了一种复合优化方法,用于发动机多变量,状态空间模型的时域辨识。优化中粒子群优化算法和最小二乘优化算法按照“串联”方式运行。粒子群优化从一个初始种群出发,通过进化来搜索最优解。然而有些时候,粒子群算法会陷入次优解。那么最小二乘优化算法就可以从粒子群的次优解出发,通过共轭梯度法获得问题的最优解。本方法适用于待估计参数较多,且参数变化范围大的高阶多变量系统。本文将复合优化算法用于4输入4输出状态变量模型参数的估计。仿真结果表明了所提出方法的有效性。     

用于推进剂燃速辨识的最小二乘法最大邻城算法

介绍了非线性参数最小二乘估值最大邻域算法的数学原理，计算方法及其在辨识固体火箭发动机侵蚀燃速、基本燃速等的应用。实践表明该方法用于推进剂燃速辨识是行之有效的。     

航空发动机研制费估算工作的现状与展望

针对在立项论证阶段对航空发动机项目经费进行快速并有效的估算的问题,基于中国航空发动机的研制现状和型号数据的积累情况,分析了发动机研制经费估算工作的重点与难点以及相应的解决途径,并采用偏最小二乘法建立了航空发动机研制经费估算模型。计算结果表明:该发动机模型精度较高,已经应用于国产某型涡扇发动机的经费测算工作中,并可用于中国未来航空发动机型号的研制经费估算。     

Novel passive localization algorithm based on double side matrix-restricted total least squares

In order to solve the bearings-only passive localization problem in the presence of erroneous observer position, a novel algorithm based on double side matrix-restricted total least squares (DSMRTLS) is proposed. First, the aforementioned passive localization problem is transferred to the DSMRTLS problem by deriving a multiplicative structure for both the observation matrix and the observation vector. Second, the corresponding optimization problem of the DSMRTLS problem without constraint is derived, which can be approximated as the generalized Rayleigh quotient minimization problem. Then, the localization solution which is globally optimal and asymptotically unbiased can be got by generalized eigenvalue decomposition. Simulation results verify the rationality of the approximation and the good performance of the proposed algorithm compared with several typical algorithms.     

Receiver-channel based adaptive blind equalization approach for GPS dynamic multipath mitigation

Aiming at mitigating multipath effect in dynamic global positioning system (GPS) satellite navigation applications, an approach based on channel blind equalization and real-time recursive least square (RLS) algorithm is proposed, which is an application of the wireless communication channel equalization theory to GPS receiver tracking loops. The blind equalization mechanism builds upon the detection of the correlation distortion due to multipath channels; therefore an increase in the number of correlator channels is required compared with conventional GPS receivers. An adaptive estimator based on the real-time RLS algorithm is designed for dynamic estimation of multipath channel response. Then, the code and carrier phase receiver tracking errors are compensated by removing the estimated multipath components from the correlators’ outputs. To demonstrate the capabilities of the proposed approach, this technique is integrated into a GPS software receiver connected to a navigation satellite signal simulator, thus simulations under controlled dynamic multipath scenarios can be carried out. Simulation results show that in a dynamic and fairly severe multipath environment, the proposed approach achieves simultaneously instantaneous accurate multipath channel estimation and significant multipath tracking errors reduction in both code delay and carrier phase.     

基于连接点的机载双天线InSAR系统相位偏置估计算法

针对在茂密森林或沙漠地区部署地面控制点(GCP)对其进行测量非常困难的问题,提出了一种基于连接点的机载双天线InSAR系统相位偏置估计新算法。基于InSAR原理建立地面高程反演的几何表达式,并根据重叠区域里的连接点高程在两次干涉测高过程中不变的特性建立相位偏置间的线性关系,在用最小二乘法求解相位偏置时,通过对不同的连接点进行加权,使估计出的相位偏置更加准确。对X波段机载双天线InSAR系统获得的实际数据进行处理,结果表明:与传统的算法相比,该算法无需GCP,具有更低的成本和更高的效率,且产生的数字高程模型(DEM)具有相似的精度。     

Moving target localization in distributed MIMO radar with transmitter and receiver location uncertainties

In this paper, the problem of moving target localization from Bistatic Range(BR) and Bistatic Range Rate(BRR) measurements in a Multiple-Input Multiple-Output(MIMO) radar system having widely separated antennas is investigated. We consider a practically motivated scenario,where the accurate knowledge of transmitter and receiver locations is not known and only the nominal values are available for processing. With the transmitter and receiver location uncertainties,which are usually neglected in MIMO radar systems by prior studies, taken into account in the measurement model, we develop a novel algebraic solution to reduce the estimation error for moving target localization. The proposed algorithm is based on the pseudolinear set of equations and two-step weighted least squares estimation. The Cramer-Rao Lower Bound(CRLB) is derived in the presence of transmitter and receiver location uncertainties. Theoretical accuracy analysis demonstrates that the proposed solution attains the CRLB, and numerical examples show that the proposed solution achieves significant performance improvement over the existing algorithms.     

复杂推力器配置控制能力的性能指标及其应用

针对多个推力器斜装且耦合强的复杂配置情况,首先基于矩阵范数理论提出一种适用于分析复杂推力器配置控制能力的性能指标——最小上界指令,给出其计算公式,并通过算例验证了该性能指标在评价配置控制能力的有效性;然后讨论推力矢量的幅值和几何分布对于该性能指标的影响;基于该性能指标,建立一个含参数配置阵的优化设计非线性规划模型,讨论了以增强配置控制能力为目标进行配置参数优化设计的方法．     

一种改进RLS算法及其在SINS快速对准中的应用

在传统递推最小二乘算法(RLS)中,人为设置的递推初始值将导致状态估计的有偏性,也就丧失了最优性,当量测数据次数较小时尤为严重。摒弃了传统RLS算法"新估计值=旧估计值+修正值"的递推结构,提出了借助中间量进行递推,再由中间量直接作状态估计的改进算法。改进RLS算法状态估计结果与批处理LS算法完全一致,且无需初始状态的任何信息。将改进RLS算法应用于捷联惯导系统(SINS)初始对准。对于一定的初始对准精度要求,理论上改进RLS算法所需的初始对准时间是最短的。最后,SINS初始对准数值仿真结果验证了所提算法的正确性。