OPTIMIZATION OF ACOUSTIC IMPEDANCE，GEOMETRIC STRUCTURE AND OPERATING CONDITION OF LINERS MOUNTED IN ENGINE DUCT

ＯＰＴＩＭＩＺＡＴＩＯＮＯＦＡＣＯＵＳＴＩＣＩＭＰＥＤＡＮＣＥ，ＧＥＯＭＥＴＲＩＣＳＴＲＵＣＴＵＲＥＡＮＤＯＰＥＲＡＴＩＮＧＣＯＮＤＩＴＩＯＮＯＦＬＩＮＥＲＳＭＯＵＮＴＥＤＩＮＥＮＧＩＮＥＤＵＣＴＬｕＹａｄｏｎｇ；ＷａｎｇＱｉｎｇｋｕａｎ（Ｉｎｓｔｉ...     

SURGE FORECAST FOR AERONAUTICAL ENGINES WITH EXTENDED KALMAN ESTIMATION

ＳＵＲＧＥＦＯＲＥＣＡＳＴＦＯＲＡＥＲＯＮＡＵＴＩＣＡＬＥＮＧＩＮＥＳＷＩＴＨＥＸＴＥＮＤＥＤＫＡＬＭＡＮＥＳＴＩＭＡＴＩＯＮＳＵＲＧＥＦＯＲＥＣＡＳＴＦＯＲＡＥＲＯＮＡＵＴＩＣＡＬＥＮＧＩＮＥＳＷＩＴＨＥＸＴＥＮＤＥＤＫＡＬＭＡＮＥＳＴＩＭＡＴＩＯ...     

SUPERPLASTICALLY FREE BULGING OFDURA LUMINLY12CZ UNDER AN EXTERNAL STRONG ELECTRIC FIELD

ＳＵＰＥＲＰＬＡＳＴＩＣＡＬＬＹＦＲＥＥＢＵＬＧＩＮＧＯＦＤＵＲＡＬＵＭＩＮＬＹ１２ＣＺＵＮＤＥＲＡＮＥＸＴＥＲＮＡＬＳＴＲＯＮＧＥＬＥＣＴＲＩＣＦＩＥＬＤＬｉＭｉａｏｑｕａｎ；ＷｕＳｈｉｃｈｕｎ（ＤｅｐａｒｔｎｉｅｎｔｏｆＭａｔｅｒｉａｉｓＳｃｉｅ...     

EXPERIMENTAL INVESTIGATION ABOUT THE UNSTEADY AERODYNAMIC CHARACTERISTICS OF WINGS

ＥＸＰＥＲＩＭＥＮＴＡＬＩＮＶＥＳＴＩＧＡＴＩＯＮＡＢＯＵＴＴＨＥＵＮＳＴＥＡＤＹＡＥＲＯＤＹＮＡＭＩＣＣＨＡＲＡＣＴＥＲＩＳＴＩＣＳＯＦＷＩＮＧＳＹｕＸｉｎｚｈｉ；ＹａｎｇＹｏｎｇｎｉａｎ；ＷｕＺｅ（Ａｉｒｃｒａ．ｆｔＥｎｇｉｎｅｅｒｉｎｇＤｅｐａ...     

FATIGUE DAMAGE AND LIFETIME PREDICTION OF AERONAUTIC WELDED STRUCTURES UNDER HIGH TEMPERATURE

ＦＡＴＩＧＵＥＤＡＭＡＧＥＡＮＤＬＩＦＥＴＩＭＥＰＲＥＤＩＣＴＩＯＮＯＦＡＥＲＯＮＡＵＴＩＣＷＥＬＤＥＤＳＴＲＵＣＴＵＲＥＳＵＮＤＥＲＨＩＧＨＴＥＭＰＥＲＡＴＵＲＥＺｕｏＪｉａｎｚｈｅｎｇ，ＬｏｕＺｈｉｗｅｎ，ＫｕａｎｇＺｈｅｎｂａｎｇ（ＳｔａｔｅＫ...     

AERODYNAMIC DESIGN PROBLEM SOLVED BY THE THREE－DIMENSIONAL STREAM FUNCTION FORMULATlON

ＡＥＲＯＤＹＮＡＭＩＣＤＥＳＩＧＮＰＲＯＢＬＥＭＳＯＬＶＥＤＢＹＴＨＥＴＨＲＥＥ－ＤＩＭＥＮＳＩＯＮＡＬＳＴＲＥＡＭＦＵＮＣＴＩＯＮＦＯＲＭＵＬＡＴｌＯＮＧｕＣｈｕｎｗｅｉ；ＸｕＪｉａｎｚｈｏｎｇ；ＤｕＪｉａｎｙｉ（ＩｎｓｉｉｔｕｔｅｏｆＥｎｇｉｎｅ...     

NUMERICAL SIMULATION OF THE FLOW PAST WING-BODY USING EULER EQUATIONS

ＮＵＭＥＲＩＣＡＬＳＩＭＵＬＡＴＩＯＮＯＦＴＨＥＦＬＯＷＰＡＳＴＷＩＮＧ－ＢＯＤＹＵＳＩＮＧＥＵＬＥＲＥＱＵＡＴＩＯＮＳＮＵＭＥＲＩＣＡＬＳＩＭＵＬＡＴＩＯＮＯＦＴＨＥＦＬＯＷＰＡＳＴＷＩＮＧ－ＢＯＤＹＵＳＩＮＧＥＵＬＥＲＥＱＵＡＴＩＯＮＳ￥Ｃｈｅｎ...     

NUMERICAL STUDY OF TOPOLOGICAL STRUCTURE OF 3-D TRANSONIC VISCOUS FLOW FIELD (TVFF) INSIDE TURBINE CASCADE

ＮＵＭＥＲＩＣＡＬＳＴＵＤＹＯＦＴＯＰＯＬＯＧＩＣＡＬＳＴＲＵＣＴＵＲＥＯＦ３ＤＴＲＡＮＳＯＮＩＣＶＩＳＣＯＵＳＦＬＯＷＦＩＥＬＤ（ＴＶＦＦ）ＩＮＳＩＤＥＴＵＲＢＩＮＥＣＡＳＣＡＤＥＧｕｏＹａｎｈｕ，ＳｈｅｎＭｅｎｇｙｕ，ＷａｎｇＢａｏｇｕｏ（Ｄｅ...     

EXPERIMENTAL AND NUMERICAL INVESTIGATIONS OF BOTH FLOW INDUCED CAVITY OSCILLATION AND ITS SUPPRESSION BY ACOUSTIC EXCITATION

ＥＸＰＥＲＩＭＥＮＴＡＬＡＮＤＮＵＭＥＲＩＣＡＬＩＮＶＥＳＴＩＧＡＴＩＯＮＳＯＦＢＯＴＨＦＬＯＷＩＮＤＵＣＥＤＣＡＶＩＴＹＯＳＣＩＬＬＡＴＩＯＮＡＮＤＩＴＳＳＵＰＰＲＥＳＳＩＯＮＢＹＡＣＯＵＳＴＩＣＥＸＣＩＴＡＴＩＯＮＬｕｏＢａｉｈｕａ；ＨｕＺｈａｎ...     

高阶各向异性k－ε模型及在直通道湍流流场中的数值研究

ＡＨＩＧＨ－ＯＲＤＥＲＡＮＩＳＯＴＲＯＰＩＣＫ－εＭＯＤＥＬＡＮＤＩＴＳＮＵＭＥＲＩＣＡＬＩＮＶＥＳＴＩＧＡＴＩＯＮＩＮＴＷＯＤＩＭＥＮＳＩＯＮＡＬＴＵＲＢＵＬＥＮＴＦＬＯＷＦＩＥＬＤＳＬｉｕＺｈｅｎｇｘｉａｎ，ＳｏｎｇＢａｏｊｕｎ，ＧｕＣｈｕａｎｇ...     

Novel data association schemes for the probability hypothesis density filter

The probability hypothesis density (PHD) filter is a practical alternative to the optimal Bayesian multi-target Alter based on finite set statistics. It propagates the PHD function, a first-order moment of the full multi-target posterior density. The peaks of the PHD function give estimates of target states. However, the PHD filter keeps no record of target identities and hence does not produce track-valued estimates of individual targets. We propose two different schemes according to which PHD filter can provide track-valued estimates of individual targets. Both schemes use the probabilistic data-association functionality albeit in different ways. In the first scheme, the outputs of the PHD filter are partitioned into tracks by performing track-to-estimate association. The second scheme uses the PHD filter as a clutter filter to eliminate some of the clutter from the measurement set before it is subjected to existing data association techniques. In both schemes, the PHD filter effectively reduces the size of the data that would be subject to data association. We consider the use of multiple hypothesis tracking (MHT) for the purpose of data association. The performance of the proposed schemes are discussed and compared with that of MHT.     

Track monitoring when tracking with multiple 2D passive sensors

A fast method of track monitoring is presented which determines what tracks are good and what tracks have had data association problems and should be eliminated. The philosophy of tracking in a dense target environment with limited central processing unit (CPU) time is to acquire the targets, track them with as simple a filter as will meet requirements, and monitor the tracks to determine if they are still tracking a target or are tracking incorrect returns and should be terminated. After termination the true targets are reacquired. However, it is difficult to determine from simple track monitoring the correct interpretation of a poor track. Poor tracks can be a result of a sensor failure, target maneuver, or incorrect data association. The author describes track monitoring and provides a solution to this dilemma when tracking with multiple two-dimensional passive sensors. The method is much faster than other monitoring methods.<>     

Tracking of multiple maneuvering targets using multiscan JPDA and IMM filtering

The paper considers the problem of tracking multiple maneuvering targets in the presence of clutter using switching multiple target motion models. A novel suboptimal filtering algorithm is developed by applying the basic interacting multiple model (IMM) approach and the joint probabilistic data association (JPDA) technique. Unlike the standard single-scan JPDA approach, the authors exploit a multiscan joint probabilistic data association (mscan-JPDA) approach to solve the data association problem. The algorithm is illustrated via a simulation example involving tracking of four maneuvering targets and a multiscan data window of length two     

多目标跟踪的概率假设密度粒子滤波

在多目标跟踪中,当目标数很大时,目标状态的联合分布的计算量会非常大。如果目标独立运动,可用各目标分别滤波来代替,但这要求考虑数据互联问题。文章介绍一种可以解决计算量问题的方法,只需计算联合分布的一阶矩——概率假设密度(PHD),PHD在任意区域S上的积分是S内目标数的期望值。因未记录目标身份,避免了数据互联问题。仿真中,传感器为被动雷达,目标观测值为距离、角度及速度时,对上述的PHD滤波进行了粒子实现,并对观测值是否相关的不同情况进行比较。PHD粒子滤波应用在非线性模型的多目标跟踪,实验结果表明,滤波可以稳健跟踪目标数为变数的情况,得到了接近真实情况的结果。     

残差滤波及其在密集多回波环境下机动多目标跟踪中的应用

 修正概率数据关联滤波（MPDAF）是目前解决密集多回波环境下机动多目标跟踪较为有效的方法,但当回波密度增高时,该方法容易失跟。本文针对此特点,在MPDAF基础上,提出了残差滤波（RF）方法,分别从理论和Monte carlo仿真两方面揭示了RF与数据关联的内在机理,结果表明该方法能大幅度提高跟踪滤波器捕捉目标和剔除关联域内多余回波的能力以及跟踪的性能,是一种解决高密集多回波环境下机动多目标跟踪的有效方法。     

Improved multi-target tracking in clutter by PDA smoothing

An algorithm is presented for the recursive tracking of multiple targets in cluttered environment by making use of the joint probabilistic data association fixed-lag smoothing (JPDAS) techniques. It is shown that a significant improvement in the accuracy of track estimation of both nonmaneuvering and maneuvering targets may be achieved by introducing a time lag of one or two sampling periods between the instants of estimation and latest measurement. Results of simulation experiments for a radar tracking problem that demonstrate the effects of fixed-lag smoothing are also presented     

密集杂波环境下的数据关联快速算法

基于联合概率数据互联（JPDA）的思想,提出了一种新的数据关联快速算法（Fast Al-gorithm for Data Association,简称FAFDA算法）.该方法不需象在最优JPDA算法中那样生成所有可能的联合互联假设,因而具有计算量小,易于工程实现的特点。仿真结果表明,与最优JPDA算法相比,FAFDA算法的跟踪性能令人满意,并且在密集杂波环境下可实时、有效地跟踪100批次以上的目标。     

Bearings-only tracking of maneuvering targets using abatch-recursive estimator

We present a new batch-recursive estimator for tracking maneuvering targets from bearings-only measurements in clutter (i.e., for low signal-to-noise ratio (SNR) targets), Standard recursive estimators like the extended Kalman Iter (EKF) suffer from poor convergence and erratic behavior due to the lack of initial target range information, On the other hand, batch estimators cannot handle target maneuvers. In order to rectify these shortcomings, we combine the batch maximum likelihood-probabilistic data association (ML-PDA) estimator with the recursive interacting multiple model (IMM) estimator with probabilistic data association (PDA) to result in better track initialization as well as track maintenance results in the presence of clutter. It is also demonstrated how the batch-recursive estimator can be used for adaptive decisions for ownship maneuvers based on the target state estimation to enhance the target observability. The tracking algorithm is shown to be effective for targets with 8 dB SNR     

一种密集多回波环境下的机动多目标跟踪起始算法

 本文研究密集多回波环境下的机动多目标跟踪起始问题。文中首先提出主跟踪子空间和边缘跟踪子空间的定义与性质,接着修正Bayes轨迹确定方法BTC,并将其与具有残差滤波的修正概率数据关联滤波算法MPDAF-RF有机地结合起来,提出一种适合高密集多回波环境的机动多目环跟踪起始方法——“全邻”Bayes跟踪起始算法ABTI。Monte Carlo仿真表明,本文所给出的算法不仅克服了一类概率数据关联滤波方法没有跟踪起始机理的缺陷,而且辨别目标与虚警的能力很强,不失为解决高密集多回波环境下机动多目标跟踪起始的有效方法。     

Tracking with classification-aided multiframe data association

In most conventional tracking systems, only the target kinematic information from, for example, a radar or sonar or an electro-optical sensor, is used in measurement-to-track association. Target class information, which is typically used in postprocessing, can also be used to improve data association to give better tracking accuracy. The use of target class information in data association can improve discrimination by yielding purer tracks and preserving their continuity. In this paper, we present the simultaneous use of target classification information and target kinematic information for target tracking. The approach presented integrates target class information into the data association process using the 2-D (one track list and one measurement list) as well as multiframe (one track list and multiple measurement lists) assignments. The multiframe association likelihood is developed to include the classification results based on the "confusion matrix" that specifies the accuracy of the target classifier. The objective is to improve association results using class information when the kinematic likelihoods are similar for different targets, i.e., there is ambiguity in using kinematic information alone. Performance comparisons with and without the use of class information in data association are presented on a ground target tracking problem. Simulation results quantify the benefits of classification-aided data association for improved target tracking, especially in the presence of association uncertainty in the kinematic measurements. Also, the benefit of 5-D (or multiframe) association versus 2-D association is investigated for different quality classifiers. The main contribution of this paper is the development of the methodology to incorporate exactly the classification information into multidimensional (multiframe) association.