Air combat target maneuver trajectory prediction based on robust regularized Volterra series and adaptive ensemble online transfer learning

Target maneuver trajectory prediction is an important prerequisite for air combat situation awareness and maneuver decision-making.However,how to use a large amount of trajectory data generated by air combat confrontation training to achieve real-time and accurate prediction of target maneuver trajectory is an urgent problem to be solved.To solve this problem,in this paper,a hybrid algorithm based on transfer learning,online learning,ensemble learning,regularization technology,target maneuvering segmentation point recognition algorithm,and Volterra series,abbreviated as AERTrOS-Volterra is proposed.Firstly,the model makes full use of a large number of trajectory sample data generated by air combat confrontation training,and constructs a Tr-Volterra algorithm framework suitable for air combat target maneuver trajectory prediction,which realizes the extraction of effective information from the historical trajectory data.Secondly,in order to improve the real-time online prediction accuracy and robustness of the prediction model in complex electromagnetic environments,on the basis of the TrVolterra algorithm framework,a robust regularized online Sequential Volterra prediction model is proposed by integrating online learning method,regularization technology and inverse weighting calculation method based on the priori error.Finally,inspired by the preferable performance of models ensemble,ensemble learning scheme is also incorporated into our proposed algorithm,which adaptively updates the ensemble prediction model according to the performance of the model on real-time samples and the recognition results of target maneuvering segmentation points,including the adaptation of model weights;adaptation of parameters;and dynamic inclusion and removal of models.Compared with many existing time series prediction methods,the newly proposed target maneuver trajectory prediction algorithm can fully mine the prior knowledge contained in the historical data to assist the current prediction.The rationality and effectiveness of the proposed algorithm are verified by simulation on three sets of chaotic time series data sets and a set of real target maneuver trajectory data sets.

基于记忆多项式的Volterra混合均衡器设计与研究

【目的】发光二极管(LED)的非线性特性导致可见光通信(VLC)系统的误码率(BER)性能恶化,特别是在具有较高峰均功率比(PAPR)的光正交频分复用(O⁃OFDM)系统中。基于Volterra级数的单级均衡器可以处理LED非线性高阶失真,且延时较小。但是求解传统Volterra级数需要多重积分运算,基于Volterra级数的均衡器实现复杂度高,且单级均衡器存在误差累积,因此均衡抑制效果有限。【方法】首先,针对传统Volterra级数计算复杂度高的问题,只保留Volterra级数中各阶非线性项和内核系数的高阶幂级数项,提出了基于记忆多项式的Volterra级数(MPVS)。相比于传统Volterra级数降低了计算复杂度,同时考虑了当前时刻的所有输入信号,提高了非线性系统建模的精度。然后,设计基于记忆多项式的Volterra(MPV)均衡器和基于记忆多项式的Volterra判决反馈均衡器(MPV⁃DFE),对于单级MPV⁃DFE,若判决部分一个码元判断错误,那么这种错误会连续成串出现,导致整个码元序列受到影响。文章提出将MPV和MPV⁃DFE两个非线性均衡器级联构成混合后均衡器(MPV+MPV⁃DFE)。MPV均衡器对LED非线性失真信号一级均衡,可以抑制一部分非线性失真,从而减少MPV⁃DFE码元判断错误。再经过MPV⁃DFE二次均衡,可以更好地抑制残留的非线性失真。【结果】最后,采用蒙特卡洛BER仿真方法验证系统设计的有效性。结果表明:与单级MPV均衡器、线性级联非线性混合均衡器(LMS+MPV-DFE)相比,在4阶正交幅度调制(QAM)非对称限幅光OFDM(ACO⁃OFDM)系统BER为10^(-4)时,所提混合均衡器分别可以获得大约7和2 dB的信噪比增益。【结论】MPV均衡器实现简单,且将两级非线性均衡器级联设计混合均衡器可以更好地抑制LED非线性失真。

Neural Volterra filter for chaotic time series prediction

A new second-order neural Volterra filter （SONVF） with conjugate gradient （CG） algorithm is proposed to predict chaotic time series based on phase space delay-coordinate reconstruction of chaotic dynamics system in this paper, where the neuron activation functions are introduced to constraint Volterra series terms for improving the nonlinear approximation of second-order Volterra filter （SOVF）. The SONVF with CG algorithm improves the accuracy of prediction without increasing the computation complexity. Meanwhile, the difficulty of neuron number determination does not exist here. Experimental results show that the proposed filter can predict chaotic time series effectively, and one-step and multi-step prediction performances are obviously superior to those of SOVF, which demonstrate that the proposed SONVF is feasible and effective.

Power spectral density analysis for nonlinear systems based on Volterra series

A consequence of nonlinearities is a multi-harmonic response via a monoharmonic excitation.A similar phenomenon also exists in random vibration.The power spectral density(PSD)analysis of random vibration for nonlinear systems is studied in this paper.The analytical formulation of output PSD subject to the zero-mean Gaussian random load is deduced by using the Volterra series expansion and the conception of generalized frequency response function(GFRF).For a class of nonlinear systems,the growing exponential method is used to determine the first 3 rd-order GFRFs.The proposed approach is used to achieve the nonlinear system’s output PSD under a narrow-band stationary random input.The relationship between the peak of PSD and the parameters of the nonlinear system is discussed.By using the proposed method,the nonlinear characteristics of multi-band output via single-band input can be well predicted.The results reveal that changing nonlinear system parameters gives a one-of-a-kind change of the system’s output PSD.This paper provides a method for the research of random vibration prediction and control in real-world nonlinear systems.

Volterra series based predistortion for broadband RF power amplifiers with memory effects

RF power amplifiers （PAs） are usually considered as memoryless devices in most existing predistortion techniques. However, in broadband communication systems, such as WCDMA, the PA memory effects are significant, and memoryless predistortion cannot linearize the PAs effectively. After analyzing the PA memory effects, a novel predistortion method based on the simplified Volterra series is proposed to linearize broadband RF PAs with memory effects. The indirect learning architecture is adopted to design the predistortion scheme and the reeursive least squares algorithm with forgetting factor is applied to identify the parameters of the predistorter. Simulation results show that the proposed predistortion method can compensate the nonlinear distortion and memory effects of broadband RF PAs effectively.

THE PARALLEL RECURSIVE AP ADAPTIVE ALGORITHM BASED ON VOLTERRA SERIES

Aiming at the nonlinear system identification problem, a parallel recursive affine projection (AP) adaptive algorithm for the nonlinear system based on Volterra series is presented in this paper. The algorithm identifies in parallel the Volterra kernel of each order, recursively estimate the inverse of the autocorrelation matrix for the Volterra input of each order, and remarkably improve the convergence speed of the identification process compared with the NLMS and conventional AP adaptive algorithm based on Volterra series. Simulation results indicate that the proposed method in this paper is efficient.

AN ALGEBRAIC ALGORITHM TO VOLTERRA SERIES REPRESENTATION OF A NONLINEAR NETWORK

It is important to solve the nth-order Volterra kernel or nonlinear transfer function indescribing a nonlinear network by the Volterra series.Based on an auxiliary algebraic expression ofthe Volterra series,an algebraic algorithm is proposed to evaluate the nth-order Volterra kernel andnonlinear transfer function in regular,triangular and symmetric forms.In addition,the complexity ofthe algebraic algorithm is improved.

A Series Approach to Perturbed Stochastic Volterra Equations of Convolution Type

In the paper, perturbed stochastic Volterra Equations with noise terms driven by series of independent scalar Wiener processes are considered. In the study, the resolvent approach to the equations under consideration is used. Sufficient conditions for the existence of strong solution to the class of perturbed stochastic Volterra Equations of convolution type are given. Regularity of stochastic convolution is supplied, as well.

Connection between Volterra series and perturbation method in nonlinear systems analyses

This paper is concerned with the connection between the Volterra series and the regular perturbation method in nonlinear systems analyses. It is revealed for the first time that, for a forced polynomial nonlinear system, if its derived linear system is a damped dissipative system, the steady response obtained through the regular perturbation method is exactly identical to the response given by the Volterra series. On the other hand, if the derived linear system is an undamped conservative system, then the Volterra series is incapable of modeling the forced polynomial nonlinear system. Numerical examples are further presented to illustrate these points. The results provide a new criterion for quickly judging whether the Volterra series is applicable for modeling a given polynomial nonlinear system.

Volterra级数混沌自适应模型在变形分析中的应用

针对变形监测数据混沌序列的特点,提出一种基于Volterra级数的混沌时间序列变形预测模型。经过相空间重构,确定合适的嵌入维数和延迟时间,输入Volterra级数自适应预测模型,然后得到变形量的预测值。将预测值与实际值及其他预测模型的预测结果进行比较,发现基于Volterra级数的混沌时间序列预测模型精度较高,在变形预测上是可行的。

简化Volterra级数的S模式信号失真恢复

机场场面多点定位利用S模式信号到达时间差实现目标定位。针对S模式信号易受接收系统内部非线性影响和机场场面复杂电磁干扰的问题,研究S模式信号失真恢复方法。根据S模式信号频谱特征和接收基站弱非线性记忆系统特征简化Volterra级数,降低计算量的同时满足Volterra级数关键核函数对S模式信号非线性失真表示能力,得到S模式信号失真恢复模型。仿真结果表明:3阶简化Volterra级数模型恢复15 dB和0 dB S模式信号前导脉冲的失真前波形,误差仅有2.23%和12.59%,且模型在典型运用环境下满足一定的适用性、抗干扰性和稳定性,为S模式信号到达时间戳的准确提取提供基础。

基于SABA优化的Volterra级数空战目标机动轨迹预测

目标机动轨迹预测是空战态势感知和目标威胁评估的重要前提。针对传统目标机动轨迹预测模型复杂度大、预测精度低等问题,通过分析并结合目标机动轨迹时序数据所具备的混沌特性,引入Volterra泛函级数模型进行目标机动轨迹预测。为解决Volterra泛函级数模型中存在高阶核函数难以求解的问题,利用变异机制和自适应步长控制机制改进蝙蝠算法的寻优能力,进而构建了一种基于自适应蝙蝠算法(SABA)优化的Volterra泛函级数目标机动轨迹预测模型,并利用优化后不同阶数的Volterra泛函级数模型对目标未来机动轨迹进行预测。仿真实验中,通过与其他优化算法改进的Volterra泛函级数模型的预测精度对比,验证了所提预测模型的可行性,同时也说明了二阶Volterra泛函级数模型更加适用于目标机动轨迹预测。

钢板缺陷识别的Volterra-SVM模型研究

针对钢板缺陷识别问题,结合超声波脉冲反射原理,提出一种基于Volterra级数和支持向量机的钢板缺陷识别方法。首先,利用Volterra级数模型建立起钢板缺陷的特征模型;其次,使用分数阶粒子群优化算法提取出原始信号中的特征参数,即Volterra级数时域核;最后,将提取到的特征向量输入支持向量机模型进行训练与测试,完成对钢板缺陷的分类识别。设计实验得到多组数据样本,进行模型验证,实验结果表明:基于Volterra级数和支持向量机的识别模型能够较好的完成对钢板缺陷的分类识别,识别准确率达93.3%。

CHARACTERISTIC FUNCTIONS OF BILINEAR TIME SERIES MODEL

Bilinear time series models are of importance to nonlinear time seriesanalysis.In this paper,the autocovariance function and the relation between linearand general bilinear time series models are derived.With the help of Volterra seriesexpansion,the impulse response function and frequency characteristic function of thegeneral bilinear time series model are also derived.

一类非线性系统的广义伴随线性方程分析研究

非线性输出频率响应函数是线性系统理论中频率响应函数在非线性系统中的一种推广,越来越多的学者已将其应用在结构损伤检测及故障诊断中.基于Volterra级数理论与由非线性微分方程描述的单输入单输出非线性系统的广义频率响应函数递归计算公式,利用多重积分性质和多维傅里叶变换,将广义频率响应函数映射到了一维频域中,推导出了一类非线性系统的广义伴随线性方程计算公式.研究表明,这类系统的第n阶非线性输出响应是以系统输入激励和前n-1阶非线性输出响应的组合函数作为广义激励作用到系统各阶广义伴随线性方程中的输出响应,最后通过求解一系列线性微分方程可得到这类非线性系统的任意阶非线性输出响应,其结果弥补了伴随线性方程无法求解这类非线性系统的不足.同时,针对广义伴随线性方程的数值计算问题,论文提出了一种耦合计算法,提高了计算非线性输出响应的精度,为非线性输出频率响应函数的计算提供了一种新思路.最后利用广义伴随线性方程与线性算子理论研究了两种典型非线性系统中非线性现象产生的原因,研究结果为非线性系统的分析与设计提供了一种有效途径.

Volterra泛函级数在非线性系统辨识中的应用

利用 Volterra泛函级数描述非线性系统 ,将非线性系统辨识问题转化为标准的最小二乘问题 ,并通过 QR分解进行求解。在对输入矩阵 P进行 Householder变换过程中 ,提出利用输入向量对输出向量的影响因子进行 P阵的列选择 ,同时完成模型辨识 ,有效地克服了 Volterra泛函级数辨识中维数灾难问题。

基于Volterra级数的全解耦RLS自适应辨识算法

针对非线性系统辨识问题,提出了一种基于Volterra级数模型的非线性系统的全解耦RLS自适应辨识算法。按照Volterra伪线性组合结构,采用RLS自适应辨识和约束优化理论,导出了具有分块对角形输入相关矩阵的全解耦Volterra标准方程,据此设计了一种基于Volterra级数模型的全解耦的RLS自适应辨识算法。该算法与部分解耦的RLS自适应算法相比,显著提高了辨识过程的收敛速度和精度。仿真结果验证了该方法的有效性。

基于Volterra级数的非线性非定常气动力降阶模型

发展了一种基于Volterra级数的非线性非定常气动力降阶模型。着重探讨了基于CFD/CSD耦合计算的Volterra核辨识方法。CFD/CSD耦合计算采用了一种简单的动网格快速生成方法,气动网格与结构网格节点间的物理信息交换采用一种改进的CVT方法。通过与CFD/CSD直接计算的结果对比,表明非定常气动力Volterra级数模型是一种效率高、精度好、能描述非线性、便于使用的降阶模型,特别适用于预测结构做非定常运动下的非定常气动力。

非线性系统故障诊断的Volterra模型方法

针对非线性系统的故障诊断问题,系统地总结了基于Volterra级数模型的故障诊断的理论与方法。由于Volterra级数模型可以完全描述非线性系统的传递特性,因而可将其应用于系统的故障诊断,即在系统工作过程中,通过分析故障发生前后Volterra模型中非线性因素的变化来判知系统的故障。在介绍了这一新的故障诊断理论的基本思想后,结合减震弹簧的疲劳诊断,总结了当前的两种主要研究方法,即基于非线性谱分析的故障诊断方法和基于多重预设Volterra模型的故障诊断方法。阐述了这两种方法的工作原理和实现方法,指出了今后在这一方向上需要进一步研究解决的问题。

基于改进粒子群算法的Volterra模型参数辨识

针对非线性系统Volterra泛函级数模型,结合混沌优化策略和种群多样性控制思想,提出了一种改进粒子群算法,并应用于Volterra模型参数的辨识,将非线性系统的辨识问题转化为高维参数空间上的优化问题。利用混沌序列增加初始种群的多样性,通过构建动态子群以进行协作寻优,且各子群采用不同的参数自适应调整策略,并定义算法收敛性测度以对精英粒子进行合理的混沌变异,避免了算法早熟收敛,提高了算法的寻优速度和寻优精度。仿真实验中,将该方法与基于标准粒子群算法、遗传算法、量子粒子群算法的Volterra模型参数辨识方法相比较,验证了该辨识方法的有效性和鲁棒性。