A variably trimmed mean CFAR radar detector

A variably trimmed mean (VTM) constant false alarm rate (CFAR) detector in which the threshold is determined by processing a linear combination of a group of ordered samples in each window is introduced. Unlike the trimmed mean detector, the number of ordered samples that require further processing is allowed to vary according to a data-dependent rule. It is demonstrated that the VTM detector exhibits performance characteristics that are independent of the total (stationary) noise power. Simulated performance results are presented for regions of clutter power transitions and for multiple target environments to illustrate the possible improvement over the order-statistic detector that can be obtained by using a VTM detector     

High-resolution simulation for rotorcraft aerodynamics in hovering and vertical descending flight using a hybrid method

A high-resolution simulation tool for rotorcraft aerodynamics is developed by coupling CFD with a Vorticity Transport Model (VTM). An Eulerian-based CFD module is used to model the blade near body flowfield, and a Lagrangian-based VTM module is employed for vortex tracking in the far wake. The coupling procedure is implemented by transmitting vortex sources to the VTM module and feeding boundary conditions back to the CFD module. The presented CFD/VTM hybrid solver is firstly validated by hover cases of three different rotor configurations. Simulation results, including the blade surface pressure distribution, rotor downwash, and hover figure of merit, exhibit favorable correlations with available experimental data. Then, a rotor operated in vertical descending flight with a fixed collective pitch is investigated. It is shown that the CFD/VTM coupling method is suitable for rotor wake simulation. Wake instabilities (far wake breakdown in hover and toroidal wake pattern in the vortex ring state) are successfully demonstrated with a moderate computational cost.     

Reliability estimation of rotary lip seal in aircraft utility system based on time-varying dependence degradation model and its experimental validation

With several attractive properties, rotary lip seals are widely used in aircraft utility system, and their reliability estimation has drawn more and more attention. This work proposes a reliability estimation approach based on time-varying dependence analysis. The dependence between the two performance indicators of rotary lip seals, namely leakage rate and friction torque, is modeled by time-varying copula function with polynomial to denote the time-varying parameters, and an efficient copula selection approach is utilized to select the optimal copula function. Parameter estimation is carried out based on a Bayesian method and the reliability during the whole lifetime is calculated based on a Monte Carlo method. Degradation test for rotary lip seal is conducted and the proposed model is validated by test data. The optimal copula function and optimal order of polynomial are determined based on test data. Results show that this model is effective in estimating the reliability of rotary lip seals and can achieve a better goodness of fit.     

相关色噪声下无冗余累积量稀疏表示DOA估计

针对传统均匀线阵中四阶累积量计算复杂度大、对快拍数敏感的问题,提出了一种快速去冗余的高分辨波达方向估计新方法。该方法首先通过构造选择矩阵对四阶累积量矩阵进行第1次降维处理,摒弃传统四阶累积量中大量冗余数据,然后对无冗余累积量矩阵进行矢量化并通过二次降维得到统计性能更优的向量观测模型,最后在相应的过完备基下建立观测模型的稀疏表示进行波达方向(Direction of Arrival,DOA)估计。同时将方法推广到L型阵列2维DOA估计,扩展了其应用范围。与传统的四阶累积量方法相比,该方法大大地减小了计算量,对快拍数要求不高,并且能够有效地抑制相关色噪声。理论分析和仿真实验验证了该方法对1维和2维DOA估计都具有较高的估计精度和分辨率。     

Optimal sensor selection strategy for discrete-time stateestimators

A novel sensor selection strategy is introduced, which can be implemented on-line in time-varying discrete-time system. We consider a case in which several measurement subsystem are available, each of which may be used to drive a state estimation algorithm. However, due to practical implementation constraints (such as the ability of the on-board computer to process the acquired data), only one of these subsystems can actually by utilized at a measurement update. An algorithm is needed, by which the optimal measurement subsystem to be used is selected at each sensor selection epoch. The approach described is based on using the square root V-Lambda information filter as the underlying state estimation algorithm. This algorithm continuously provides its user with the spectral factors of the estimation error covariance matrix, which are used in this work as the basis for an on-line decision procedure by which the optimal measurement strategy is derived. At each sensor selection epoch, a measurement subsystem is selected, which contributes the largest amount of information along the principal state space direction associated with the largest current estimation error. A numerical example is presented, which demonstrates the performance of the new algorithm. The state estimation problem is solved for a third-order time-varying system equipped with three measurement subsystem, only one of which can be used at a measurement update. It is shown that the optimal measurement strategy algorithm enhances the estimator by substantially reducing the maximal estimation error     

Doppler ambiguity resolution using multiple PRF

An algorithm for velocity ambiguity resolution in coherent pulsed Doppler radar using multiple pulse repetition frequencies (PRF) is presented. It relies on the choice of particular values for the PRFs. The folded frequency of the target signal is obtained by averaging the folded frequency estimates for each PRF, and a quasi maximum likelihood criterion is maximized for ambiguity order estimation. The fast implementation of this nonambiguous estimation procedure is based on the fast Fourier transform (FFT), The proposed waveform allows full exploitation of any (even) number of PRFs, which appears to be important for estimation improvement. The effects of the waveform parameters and the folded frequency estimation variance on the performance of the ambiguity order estimation procedure are evaluated theoretically and through computer simulations. Mean square error (MSE) curves are given to assess the Doppler frequency estimation accuracy. Finally, the new method is compared with a classical technique and the implementation of the algorithm in a clutter environment is addressed.     

基于离散混沌蝙蝠算法的测试点选择

针对模拟电路测试点的选择问题,基于智能优化算法的测试点选择方法相比于传统的迭代法具有一定的优越性,但是仍然存在算法复杂度过高等问题.在深入研究蝙蝠算法(BA)的基础上,采用混沌映射对算法进行优化,并用变换函数将算法离散化,提出一种离散混沌蝙蝠算法(CBBA),将算法应用于测试点选择.通过实验仿真,证明该算法具有较高的准确度,而且与其他算法对比发现,该算法具有更好的性能.     

Satellite lithium-ion battery remaining useful life estimation with an iterative updated RVM fused with the KF algorithm

Lithium-ion batteries have become the third-generation space batteries and are widely utilized in a series of spacecraft. Remaining Useful Life (RUL) estimation is essential to a spacecraft as the battery is a critical part and determines the lifetime and reliability. The Relevance Vector Machine (RVM) is a data-driven algorithm used to estimate a battery’s RUL due to its sparse feature and uncertainty management capability. Especially, some of the regressive cases indicate that the RVM can obtain a better short-term prediction performance rather than long-term prediction. As a nonlinear kernel learning algorithm, the coefficient matrix and relevance vectors are fixed once the RVM training is conducted. Moreover, the RVM can be simply influenced by the noise with the training data. Thus, this work proposes an iterative updated approach to improve the long-term prediction performance for a battery’s RUL prediction. Firstly, when a new estimator is output by the RVM, the Kalman filter is applied to optimize this estimator with a physical degradation model. Then, this optimized estimator is added into the training set as an on-line sample, the RVM model is re-trained, and the coefficient matrix and relevance vectors can be dynamically adjusted to make next iterative prediction. Experimental results with a commercial battery test data set and a satellite battery data set both indicate that the proposed method can achieve a better performance for RUL estimation.     

Second-order acceleration models for an MMAE target tracker

The performance of a multiple model adaptive estimator (MMAE) for an enhanced correlator/forward-looking-infrared tracker for airborne targets is analyzed in order to improve its performance. Performance evaluation is based on elemental filter selection and MMAE estimation error sizes and trends. The elemental filters are based on either first or second-order acceleration models. Improved filter selection is achieved by using acceleration models that separate the frequency content of acceleration power spectral densities into non-overlapping regions with second-order models versus the more traditional overlapping regions with first-order models. A revised tuning method is presented. The maximum a posteriori (MAP) versus the Bayesian MMAE is investigated. The calculation of the hypothesis probability calculation is altered to see how performance is affected. The impact of the ad hoc selection of a lower bound on the elemental filter probability calculation to prevent filter lockout is evaluated. Parameter space discretization is investigated     

先进复合材料制造工艺成本估算模型研究现状

降低制造成本是复合材料推广使用的必经之路,建立成本估算模型是定量评价复合材料工艺成本的基础和降低复合材料工艺成本的重要技术手段。复合材料制造设计的核心就是针对特定产品选择最佳的材料和制造工艺,实现产品性能与成本的最佳平衡。而成本估算模型可以在工艺过程与成本之间建立定量关系,这是制造设计的关键组成部分。本文对国外先进复合材料制造成本估算模型研究的现状进行了总结和评述,给出了主要成本估算模型的类型、建模方法以及各自的特点及适用范围,提出了基于成本估算模型的工艺 性能 成本集成分析的未来研究方向。     

燃气轮机发电机组突变负荷对性能的影响

针突变负荷对快速响应和实时性的要求,采用容积法建立分轴燃气轮机发电模型,制定突变负荷的控制规律和串级三回路控制策略,分析影响突变负荷性能的关键因素,表明不同的限制参数对突变过程动态性能具有不同的影响,合理的选取限制参数及限制值能够优化突变性能.通过燃气轮机发电机组突变负荷试验验证,表明模型计算结果具有较好的实时性、收敛性与动态响应特性,与试验结果的吻合性较好.通过对燃气轮机特性分析及发电机组突变负荷的试验研究表明：为了获取良好的突变性能,控制系统应在突变负荷1s内达到燃料调节限制线;甩负荷时需设置最小燃料限制值,合理的选取限制值是保证稳定燃烧和控制超调量的关键.     

引入神经网络的交互式多模型算法

在交互式多模型算法中引入神经网络算法以改进目标跟踪的精度。利用神经网络算法对基于机动目标“当前”统计模型的均值和方差自适应滤波算法进行修改,提高该算法的性能,然后采用交互作用多模型算法跟踪机动目标,提高了机动目标的跟踪精度。     

On radar detection and direction finding using sparse arrays

The potential performance of radar systems employing sparse arrays is investigated. Different sparse array geometries are evaluated with respect to detection and estimation performance. We present appropriate tools for analytical computation of the detection and estimation performance of the likelihood ratio test (LRT) and the maximum likelihood estimator (MLE), respectively. Particular attention is paid to the effect of ambiguity errors on system performance. The results show that sparse arrays have several advantages over nonsparse arrays, but that ambiguities deteriorate performance when sidelobe interference is present. Some ways to mitigate the ambiguity effects on system performance are briefly discussed.     

Improved Carrier Tracking by Smoothing Estimators

Smoothing as a way to improve the carrier phase estimation is proposed and analyzed. The performance of first-and second-order Kalman optimum smoothers are investigated. This perfomance is evaluated in terms of steady-state covariance error computation, dynamic tracking, and noise response. It is shown that with practical amounts of memory, a second-order smoother can have a position error due to an acceleration or jerk step input less than any prescribed maximum. As an example of importance to the NASA deep space network (DSN), a second-order smoother can be used to track the Voyager spacecraft at Uranus and Neptune encounters with significantly better performance than a second-order phaselocked loop.     

基于RF-SVR的燃油计量装置性能衰退检测和剩余寿命估计方法

为了实现航空发动机燃油系统的安全状态监测和健康管理,开展了燃油系统性能衰退检测和剩余使用寿命估计方面的研究。以燃油系统燃油计量装置为例,分析了其主要的性能衰退模式,设计了基于电流-速度数据的健康指标(HIs)选取方案,并考虑环境及模型参数不确定性,进行模型不确定性仿真,基于健康数据与性能衰退数据间的马氏距离对部件性能衰退进行检测。提出了基于随机森林-支持向量回归(RF-SVR)的剩余使用寿命(RUL)估计方法,利用通过RF特征选择优化的SVR模型实现部件RUL估计。最后基于某型民用涡扇发动机机械液压模型仿真数据对该方法进行了验证,结果表明:该方法的性能衰退检测虚警率及漏报率低于2%,RUL估计误差低于3%,可为航空发动机燃油系统的预测性维护提供参考。      

Relay selection based on MAP estimation for cooperative communication with outdated channel state information

In this paper, we consider an amplify-and-forward (AF) cooperative communication system when the channel state information (CSI) used in relay selection differs from that during data transmission, i.e., the CSI used in relay selection is outdated. The selected relay may not be actually the best for data transmission and the outage performance of the cooperative system will deteriorate. To improve its performance, we propose a relay selection strategy based on maximum a posteriori (MAP) estimation, where relay is selected based on predicted signal-to-noise ratio (SNR). To reduce the computation complexity, we approximate the a posteriori probability density of SNR and obtain a closed-form predicted SNR, and a relay selection strategy based on the approximate MAP estimation (RS-AMAP) is proposed. The simulation results show that this approximation leads to trivial performance loss from the perspective of outage probability. Compared with relay selection strategies given in the literature, the outage probability is reduced largely through RS-AMAP for medium-to-large transmitting powers and medium-to-high channel correlation coefficients.     

试论民用飞机选型程序

飞机选型的最终目的是为航空公司选择一个最满意的机群。它从每个城市对子之间的航班对飞机的优选开始,综合公司全部航班选型结果,从中挑选不同的若干机型组合,形成不同的机群方案;再评估这些方案,选取其中最为满意的一个。本文就如何进行民用飞机和机群的优选进行了探讨,并提出了一个“飞机选型程序框图”。     

联翼布局传感器飞机多目标优化设计

针对联翼布局传感器飞机的任务需求特点,建立了专用于该类无人机的能够综合考虑气动、结构和雷达性能的多目标优化设计模型。利用改进的类函数/形函数参数化方法完成整机外形进行参数化,利用风洞试验进行了气动分析模型的验证,基于工程梁理论搭建了联翼布局结构重量估算模型。在雷达距离方程的基础上,建立了机翼内部雷达天线性能估算的数学模型。利用该模型,能够在优化过程中考虑到内置机载预警天线安装位置和性能评估对翼型选择、结构重量和气动特性的影响,最终得到全局最优设计。对某方案的优化结果表明,相较于优化前,多目标优化结果能够明显提升整机升阻比和前后视雷达探测范围,同时减轻结构重量。优化结果和敏感性分析表明了该多目标优化模型的可行性和必要性。     

PLS-SEM在装备成本估算核心参数中的应用

参数估算法是当前武器装备成本估算的主要方法。TruePlanning软件作为国际主流的参数法成本估算软件,其核心参数是制造复杂度（MCPLX）。由于该参数在武器装备发展早期无法准确获取,从而使得TruePlanning软件难以适用于新型武器装备的发展早期阶段。以美军战斗机/攻击机为研究对象,从装备性能参数出发,建立制造复杂度的偏最小二乘法的结构方程模型（PLS-SEM）,并进行参数估计和检验。最终结果显示,所建模型拟合结果的平均相对误差为7.94%,拟合效果好;检验样本估算的平均相对误差为5.94%,预测能力较高,满足于战斗机/攻击机新型号发展早期进行费用估算的要求。这也为中国装备发展早期的费用估算工作开辟了新的思路。     

性能退化双线性过程方法与成组试验方法的比较

针对性能退化可靠性分析问题,将双线性过程方法与成组试验方法进行了对比分析.首先,从理论层面阐述了双线性过程方法和成组试验方法对性能退化数据的建模过程,可以发现:双线性过程方法通过将性能退化数据作为一个整体进行统计分析,能够避免两步估计造成的信息浪费,提高分析精度.其次,以窗雨刮器开关性能退化试验为例进行了对比分析,结果表明,双线性过程方法共有2.5%的试验数据位于概率为90%的百分位值曲线估计结果上方,符合工程实际需要,而成组试验方法共有15%的试验数据位于百分位值曲线估计结果上方.此外,基于残差分析的模型检验结果表明双线性过程方法更为合理.