基于自适应差分进化算法的变循环发动机模型求解方法研究

为了降低传统迭代算法在求解变循环发动机非线性模型时对初值的依赖性,将模型的求解问题转换为求最小值的优化问题,引入差分进化算法进行模型的求解,并提出一种自适应差分进化算法(ADE)。ADE借助轮盘赌选择法,利用种群的进化经验可以自适应的选择最适合当前种群的差分策略与算法控制参数。针对变循环发动机四个典型工作点的模型求解问题,研究了标准差分进化算法(SDE)的控制参数对其性能的影响,获取了SDE在求解四个典型工作点时的最优控制参数组合,对比分析了ADE与SDE的性能差异,最后研究了种群规模对ADE性能的影响。结果表明：SDE在求解发动机模型时具有较好的鲁棒性,在求解不同工作点时算法的最优控制参数并不完全相同;相比于使用最优控制参数的SDE,ADE可以在不影响算法鲁棒性的情况下提升效率50%以上;减少ADE的种群规模会在提升算法效率的同时破坏鲁棒性。     

基于ADE-ELM的涡轴发动机建模方法

提出了基于自适应微分进化-极端学习机(ADE-ELM)求解平衡方程的高精度涡轴发动机实时部件级模型建立方法.基于牛顿-拉夫逊(N-R)迭代模型,以迭代计算前模型平衡方程残差为输入,迭代收敛后平衡方程猜值修正量为输出,训练极端学习机,并采用自适应微分进化(ADE)算法优化极端学习机(ELM)参数,提高猜值修正量映射精度.ADE算法中采用sigmoid型自适应缩放因子,提高了微分进化算法的寻优能力.在涡轴发动机不同飞行状态下的测试结果表明,以N-R迭代算法模型为基准,基于ADE-ELM的发动机模型,最大建模误差约为一次通过算法的1/3,运算耗时约为一次通过算法的1/3,验证了算法的有效性.      

Active flutter suppression of a multiple-actuated-wing wind tunnel model

In this study, a multi-input/multi-output（MIMO） time-delay feedback controller is designed to actively suppress the flutter instability of a multiple-actuated-wing（MAW） wind tunnel model in the low subsonic flow regime. The unsteady aerodynamic forces of the MAW model are computed based on the doublet-lattice method（DLM）. As the first attempt, the conventional linear quadratic-Gaussian（LQG） controller is designed to actively suppress the flutter of the MAW model. However, because of the time delay in the control loop, the wind tunnel tests illustrate that the LQG-controlled MAW model has no guaranteed stability margins. To compensate the time delay, hence, a time-delay filter, approximated via the first-order Pade approximation, is added to the LQG controller. Based on the time-delay feedback controller, a new digital control system is constructed by using a fixed-point and embedded digital signal processor（DSP） of high performance. Then, a number of wind tunnel tests are implemented based on the digital control system.The experimental results show that the present time-delay feedback controller can expand the flutter boundary of the MAW model and suppress the flutter instability of the open-loop aeroelastic system effectively.     

Doppler Compensation & Single Pulse Imaging using Adaptive Pulse Compression

The effects of target Doppler are addressed in relation to adaptive receive processing for radar pulse compression. To correct for Doppler-induced filter mismatch over a single pulse, the Doppler-compensated adaptive pulse compression (DC-APC) algorithm is presented whereby the respective Doppler shifts for large target returns are jointly estimated with the illuminated range profile and subsequently incorporated into the original APC adaptive receive filter formulation. As a result, the Doppler-mismatch-induced range sidelobes can be suppressed thereby regaining a significant portion of the sensitivity improvement that is possible when applying adaptive pulse compression (APC) without the existence of significant Doppler mismatch. In contrast, instead of compensating for Doppler mismatch, the single pulse imaging (SPI) algorithm generalizes the APC formulation for a bank of Doppler-shifted matched filters thereby producing a sidelobe-suppressed range-Doppler image from the return signal of a single radar pulse which is applicable for targets with substantial variation in Doppler. Both techniques are based on the recently proposed APC algorithm and its generalization, the multistatic adaptive pulse compression (MAPC) algorithm, which have been shown to be effective for the suppression of pulse compression range sidelobes thus dramatically increasing the sensitivity of pulse compression radar.     

An Improved Algorithm for Adaptive Processing

The Widrow-Hoff least mean square (LMS) algorithm based on the method of steepest descent is conditionally stable. A modified algorithm is given which is unconditionally stable, capable of better performance when used in adaptive filter processing, and provides a more realistic means for simulating the Applebaum-Howells adaptive loop.     

The effects of IF bandpass mismatch errors on adaptive cancellation

The effects of IF bandpass mismatch errors on adaptive cancellers are investigated. Frequency mismatch errors occur because of errors in the synthesis process of the bandpass filters which are designed to be identical and are in each input channel. Tapped-delay line transversal filters can be used to compensate for these frequency mismatches and thus improve cancellation performance. A pole/zero error model of the filters is developed whereby closed-form solutions of the maximum achievable average cancellation are obtained. This cancellation is a function of the order of the ideally matched frequency filters, the number of time-delay taps in the compensating transversal filter, the bandwidth-tapped time-delay product, and the constraints on these parameters. A design procedure is outlined for optimizing the canceller with respect to these parameters and their constraints; specifically, results are presented for Butterworth-type input filters. It is shown that an arbitrarily low output noise residue cannot be achieved by arbitrarily increasing the number of time-delay taps     

An improved LMS adaptive algorithm for narrowband interferencesuppression in direct sequence spread spectrum

In 1990 Vijayan and Poor proposed nonlinear predictive methods for suppressing narrowband interference in spread spectrum (SS) systems with a significant increase in signal-to-noise ratio (SNR) improvement. The main drawback of their adaptive nonlinear filter is its slow convergence rate. A new adaptive least mean squares (LMS) algorithm to increase the slow convergence of their nonlinear adaptive filter is described. Computer simulation results are presented to support the advantages of the new filter     

Adaptive Gaussian sum squared-root cubature Kalman filter with split-merge scheme for state estimation

The paper deals with state estimation problem of nonlinear non-Gaussian discrete dynamic systems for improvement of accuracy and consistency. An efficient new algorithm called the adaptive Gaussian-sum square-root cubature Kalman filter（AGSSCKF） with a split-merge scheme is proposed. It is developed based on the squared-root extension of newly introduced cubature Kalman filter（SCKF） and is built within a Gaussian-sum framework. Based on the condition that the probability density functions of process noises and initial state are denoted by a Gaussian sum using optimization method, a bank of SCKF are used as the sub-filters to estimate state of system with the corresponding weights respectively, which is adaptively updated. The new algorithm consists of an adaptive splitting and merging procedure according to a proposed split-decision model based on the nonlinearity degree of measurement. The results of two simulation scenarios（one-dimensional state estimation and bearings-only tracking） show that the proposed filter demonstrates comparable performance to the particle filter with significantly reduced computational cost.     

Unsupervised Tracking of Maneuvering Vehicles

The design of adaptive filters for the tracking of high-performance maneuvering targets is a fundamental problem in real-time surveillance systems. As is well known, a filter which provides heavy smoothing can not accurately track an evasive maneuver, and conversely. Consequently, one is led to the consideration of adaptive methods of filter design. This paper presents an improved self-adaptive filter algorithm for on-line solution of the above problem. Basically, this algorithm utilizes the orthogonality property of the residual time series to force the filter to automatically track the optimal gain levels in a changing environment.     

Efficient robust AMF using the FRACTA algorithm

The FRACTA algorithm has been shown to be an effective space-time adaptive processing (STAP) methodology for the airborne radar configuration in which there exists nonhomogeneous clutter, jamming, and dense target clusters. Further developments of the FRACTA algorithm are presented here in which the focus is on the robust, efficient implementation of the FRACTA algorithm. Enhancements to the FRACTA algorithm include a censoring stopping mechanism, an alternative data blocking approach for adaptive power residue (APR) censoring, and a fast reiterative censoring (RC) procedure. Furthermore, a coherent processing interval (CPI) segmentation scheme for computing the adaptive weights is presented as an alternative approach to computing the adaptive matched filter (AMF) weight vector that allows for lower sample support and reduced computational complexity. The enhanced FRACTA algorithm, denoted as FRACTA.E, is applied to the KASSPER I challenge datacube which possesses dense ground target clusters that are known to have a significant deleterious effect on standard adaptive matched filtering (AMF) processors. It is shown that the FRACTA.E algorithm outperforms and is considerably more computationally efficient than both the original FRACTA algorithm and the standard sliding window processing (SWP) approach. Furthermore, using the KASSPER I datacube, the FRACTA.E algorithm is shown to have the same detection performance as the clairvoyant algorithm where the exact range-dependent clutter covariance matrices are known.     

一种新型模糊自适应Kalman滤波器在组合导航中的应用

针对光纤陀螺捷联惯导(FOG SINS)/GPS组合导航系统实际工作环境中,由于系统噪声与量测噪声模型发生变化而带来的滤波器发散的问题,提出一种新型模糊自适应Kalman滤波器(FSHAKF).通过引入IMU精度因子与GPS水平精度因子,构造模糊推理系统(FIS),实时更新自适应参数,有效地解决了传统Sage-Husa自适应滤波器(SHAKF)估计模型不准确、系统噪声与量测噪声无法同时估计以及滤波器长时间易发散的问题.仿真实验表明,本文提出的FSHAKF算法相较于SHAKF算法,估计精度得到明显提高,且避免了滤波器的发散.     

一个用于目标跟踪的改进粒子滤波算法

简化UT（unscented transformation）转化参数，修改UKF（unscented Kalmanfilter）提议分布，提出了改进的粒子滤波算法。调节因子的增加使得能在线自适应估计，滤波性能提高，并形成一个自适应的算法。仅有角测量的目标跟踪仿真试验证实了改进的粒子滤波算法要优于其它滤波方式。     

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     

基于卡尔曼滤波的航空发动机单神经元自适应控制

针对航空发动机多任务、多变量、高精度和一体化控制的需求,提出了一种基于卡尔曼滤波的单神经元自适应控制方法。该方法在单神经元自适应控制算法的基础上,增加了对控制量和发动机反馈量的滤波,提高了响应速度,精度较高。仿真结果证明,该方法对过程噪声和测量噪声具有很强的克服能力,所需计算量较小,能满足发动机控制对实时性的要求。     

基于Kalman滤波的高频CW电报信号自动识别

针对强噪声背景下高频CW电报信号检测算法性能严重下降、误码率较高的问题,文章提出一种基于卡尔曼滤波的高频CW电报信号同步检测识别算法。利用自同步法对CW电报信号实现位同步,进而利用卡尔曼滤波针对时变干扰噪声设置自适应阈值,对信号能量进行软判决,实现CW电报信号的自适应跟踪检测,提取有效信号进行识别。通过短波信道仿真软件和实际短波通信测试表明,该算法能够在强噪声背景下有效检测识别CW电报信号,且算法可由迭代实现。     

稳定平台中陀螺漂移自适应实时估计算法

 针对陀螺稳定平台的漂移问题,建立了陀螺稳定跟踪装置在不同工作模式下陀螺漂移的数学模型,指出稳定模式下包含常值漂移和相关漂移的陀螺低频噪声是影响稳定精度的主要原因。提出一种自适应实时估计算法,采用卡尔曼滤波框架和滤波器收敛判据,结合Sage-Husa滤波和加权Sage-Husa滤波算法,利用跟踪器跟踪静止目标时输出的脱靶量信号对陀螺常值漂移和相关漂移进行估计。实验结果表明:该算法能够在系统模型和噪声特性均不准确的情况下使用,收敛时间小于3 s,估计均方差小于0.02 (°)/s,具有良好的鲁棒性和自适应能力。     

航空发动机气路故障诊断的SANNWA-PF算法

针对航空发动机非线性、非高斯的特点,提出一种用于航空发动机气路故障诊断的自适应神经网络权值调整粒子滤波（SANNWA PF）算法。该算法根据粒子分布情况确定分裂和调整的粒子数目,进而根据粒子权重采用正态分布的方式进行分裂,采用反向传插(BP)神经网络进行权值调整,缓解了粒子的退化和贫化,具有更强的自适应性能和跟踪能力。通过一维非线性跟踪模型和航空发动机气路故障诊断仿真研究表明:SANNWA PF算法具有良好的非高斯性能,相对粒子滤波一维非线性追踪模型估计精度提高约21%,航空发动机气路故障诊断在高斯噪声和非高斯噪声下分别提高约30%和26%,诊断速度分别提高约7倍和10倍。      

基于Kalman滤波的GPS/INS接收机自适应干扰抑制方法

 考虑到惯导信息辅助GPS(GPS/INS)接收机对干扰抑制实时性的要求,提出一种基于Kalman滤波的GPS/INS接收机自适应干扰抑制方法。自适应广义旁瓣相消(GSC)多采用低复杂度最小均方(LMS)算法更新权矢量,收敛速率较低,严重时会导致接收机定位中断。首先利用Householder变换构建GSC下支路的阻塞矩阵,用于阻塞任意二维阵型阵列接收的期望信号;再用Kalman滤波自适应更新下支路权矢量,从而有效提高阵列输出信干噪比(SINR)。理论分析和仿真结果说明本文方法可有效抑制干扰对接收机的影响,且具有实时性高的特点。     

单兵导航多传感器信息融合自适应滤波算法

基于足绑式INS的单兵导航系统,通过将惯性导航系统、人体运动学约束、磁强计等多传感器信息进行融合得到准确的单兵导航信息。对于匀速步行时的单兵导航,可采用普通Kalman滤波算法进行多传感器信息融合,但不能满足跑步等激烈运动模式下单兵导航需求。提出衰减记忆新息自适应Kalman滤波算法,可满足多步态模式下的单兵导航多源信息融合的需求,实验结果验证了算法的有效性。     

GPS/INS组合导航系统自适应滤波算法与仿真研究

随着组合导航系统应用环境的日趋复杂，给噪声统计特性的准确描述带来困难，这将造成Kalman滤波器不稳定甚至发散。首先对目前解决此问题常用的自适应滤波方法进行了总结和分析，在此基础上，给出了基于滤波收敛性判据，结合Sage-Husa自适应滤波和强跟踪Kalman滤波的改进自适应滤波算法。最后以GPS／INS组合导航系统为例进行了计算机仿真，结果表明：该算法可有效抑制滤波发散，具有较大范围的自适应能力。