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.     

多站可变弧段直接最佳弹道估计

本文首先给出了较全面的外测多站处理计算模型,然后,选取实用的M估计方法估算参数;在解算线性模型过程中,采用了避免待估参数系数矩阵求逆的方法,它在工程应用上,是简捷、有效的。     

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.     

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.     

嵌入维数自适应最小二乘支持向量机状态时间序列预测方法

 针对航空发动机状态时间序列预测中嵌入维数难于有效选取的问题,提出一种基于嵌入维数自适应最小二乘支持向量机(LSSVM)的预测方法。该方法将嵌入维数作为影响状态时间序列预测精度的重?问?以交叉验证误差为评价准则,利用粒子群优化（PSO）进化搜索LSSVM预测模型的最优超参数与嵌入维数,同时通过矩阵变换原理提高交叉验证过程的计算效率,并最终建立优化后的LSSVM预测模型。航空发动机排气温度（EGT）预测实例表明,该方法可自适应选取适用于状态时间序列预测的最优嵌入维数且预测精度高,适用于航空发动机状态时间序列预测。     

A Novel PF-LSSVR-based Framework for Failure Prognosis of Nonlinear Systems with Time-varying Parameters

Particle filtering (PF) is being applied successfully in nonlinear and/or non-Gaussian system failure prognosis. However, for failure prediction of many complex systems whose dynamic state evolution models involve time-varying parameters, the traditional PF-based prognosis framework will probably generate serious deviations in results since it implements prediction through iterative calculation using the state models. To address the problem, this paper develops a novel integrated PF-LSSVR framework based on PF and least squares support vector regression (LSSVR) for nonlinear system failure prognosis. This approach employs LSSVR for long-term observation series prediction and applies PF-based dual estimation to collaboratively estimate the values of system states and parameters of the corresponding future time instances. Meantime, the propagation of prediction uncertainty is emphatically taken into account. Therefore, PF-LSSVR avoids over-dependency on system state models in prediction phase. With a two-sided failure definition, the probability distribution of system remaining useful life (RUL) is accessed and the corresponding methods of calculating performance evaluation metrics are put forward. The PF-LSSVR framework is applied to a three-vessel water tank system failure prognosis and it has much higher prediction accuracy and confidence level than traditional PF-based framework.     

基于迭代变加权最小二乘的宽带波束赋形方法

针对常规加权最小二乘宽带波束形成中存在较大主瓣增益损失和旁瓣电平偏高的问题,提出了一种基于迭代变加权最小二乘的宽带波束赋形方法。首先通过合理设计加权函数和参考波束同步迭代优化波束主瓣区域和旁瓣区域对应的加权函数值;然后对波束最小旁瓣电平进行调整并更新加权值,以获得低旁瓣宽带近似频率不变波束形成器。与常规加权最小二乘法相比,有效减小了波束主瓣增益损失,改善了波束的频率不变特性,并降低了宽带波束最高旁瓣电平。仿真结果表明该方法的有效性。     

一种新的基于TDOA与GROA的无源定位算法

针对基于到达时间差(TDOA)与到达增益比(GROA)的辐射源无源定位问题,提出了一种新的定位算法。首先通过引入一个中间变量,根据TDOA和GROA测量模型,构造一个约束加权最小二乘(CWLS)估计。由于这个CWLS问题是非凸的优化问题,现有的方法不能很好地求解。为此,提出了一种有效的方法可以求解到其全局最优解。最后,所提算法被推广到观测站存在自定位误差时的定位求解。计算机仿真结果验证了所提算法能够获得优于传统两步加权最小二乘法(2WLS)的定位性能,能够在更大的噪声条件下达到克拉美罗下界(CRLB)。