机载光电跟踪系统的模糊PID控制

为了提高机载光电跟踪系统的控制性能,提出了一种模糊自适应PID控制算法。首先,针对机载光电跟踪控制系统的特点,建立了被控对象的模型。接着,对机载光电跟踪系统模糊PID控制器的设计进行了详细介绍。最后,利用经典PID控制、模糊控制、模糊PID控制3种算法对机载光电稳定跟踪系统进行仿真比较。仿真结果表明模糊PID控制算法较之前两种算法具有响应快、超调量小、抗干扰能力强、稳态性能好等优点,对机载光电跟踪系统具有较好的控制能力。     

RLS-based adaptive algorithms for generalized eigen-decomposition

The aim of this paper is to develop efficient online adaptive algorithms for the generalized eigen-decomposition problem which arises in a variety of modern signal processing applications. First, we reinterpret the generalized eigen-decomposition problem as an unconstrained minimization problem by constructing a novel cost function. Second, by applying projection approximation method and recursive least-square (RLS) technique to the cost function, a parallel adaptive algorithm for a basis for the r-dimensional (r>0) dominant generalized eigen-subspace and a sequential algorithm based on deflation technique for the first r-dominant generalized eigenvectors are derived. These algorithms can be viewed as counterparts of the extended projection approximation subspace tracking (PAST) and PASTd algorithms, respectively. Furthermore, we modify the parallel algorithm to explicitly estimate the first r-generalized eigenvectors in parallel, not the generalized eigen-subspace. More important, the modified parallel algorithm can be used to extract multiple generalized eigenvectors of two nonstationary sequences, while the proposed sequential algorithm lacks this ability because of slow convergence of minor generalized eigenvectors due to error propagation of the deflation technique. Third, following convergence analysis methods for PAST and PASTd, we prove the asymptotic convergence properties of the proposed algorithms. Finally, computer simulations are performed to investigate the accuracy and the speed advantages of the proposed algorithms.     

Statistical properties of the LMS fourier analyzer in the presence of frequency mismatch

The statistical performances of the conventional adaptive Fourier analyzers, such as the least mean square (LMS), the recursive least square (RLS) algorithms, and so forth, may degenerate significantly, if the signal frequencies given to the analyzers are different from the true signal frequencies. This difference is referred to as frequency mismatch (FM). We analyze extensively the performance of the conventional LMS Fourier analyzer in the presence of FM. Difference equations governing the dynamics and closed-form steady-state expression for the estimation mean square error (MSE) of the algorithm are derived in detail. It is revealed that the discrete Fourier coefficient (DFC) estimation problem in the LMS eventually reduces to a DFC tracking one due to the FM, and an additional term derived from DFC tracking appears in the closed-form MSE expression, which essentially deteriorates the performance of the algorithm. How to derive the optimum step size parameters that minimize or mitigate the influence of the FM is also presented, which can be used to perform robust design of step size parameters for the LMS algorithm in the presence of FM. Extensive simulations are conducted to reveal the validity of the analytical results.     

Bi-iteration SVD subspace tracking algorithms

We present a class of fast subspace tracking algorithms that arise from a straightforward extension of Bauer's (1957) classical bi-iteration to the sequential processing case. The bi-iteration concept has an unexpected potential in subspace tracking. Our new bi-SVD subspace trackers are well structured and show excellent convergence properties. They outperform the TQR-SVD subspace tracking algorithm. Detailed comparisons confirm our claims. An application to rank and data adaptive signal reconstruction is also discussed     

Fast recursive basis function estimators for identification oftime-varying processes

When system parameters vary rapidly with time, the weighted least squares filters are not capable of following the changes satisfactorily; some more elaborate estimation schemes, based on the method of basis functions, have to be used instead. The basis function estimators have increased tracking capabilities but are computationally very demanding. The paper introduces a new class of adaptive filters, based on the concept of postfiltering, which have improved parameter tracking capabilities that are typical of the basis function algorithms but, at the same time, have pretty low computational requirements, which is typical of the weighted least squares algorithms     

Generalized Adaptive Notch Smoothers for Real-Valued Signals and Systems

Systems with quasi-periodically varying coefficients can be tracked using the algorithms known as generalized adaptive notch filters (GANFs). GANF algorithms can be considered an extension, to the system case, of classical adaptive notch filters (ANFs). We show that estimation accuracy of the existing algorithms, as well as their robustness to the choice of design parameters, can be considerably improved by means of compensating estimation delay effects which occur in the amplitude tracking and frequency tracking loops of GANF/ANF filters. Apart from the increased computational burden, the price for the achieved improvements is paid in terms of a decision delay-the proposed generalized adaptive notch smoothing (GANS) algorithms must be run on delayed input/output data sequences. Since such delay is acceptable in many signal processing and system identification applications, the proposed solution seems to be an attractive alternative to the currently available trackers.     

基于不变矩特征的红外成像制导跟踪算法研究

随着红外成像制导技术的发展,它对制导跟踪算法的精度和实时性的要求越来越高。制导系统已被要求在20 ms的时间内输出跟踪结果。某些跟踪算法虽然效果较好,但达不到实时输出。经典的制导算法-相关跟踪算法主要基于像素灰度特征进行跟踪,无法解决跟踪过程中由于目标的旋转、膨胀以及仿射变换带来的跟踪点漂移问题。为了解决经典相关跟踪算法无法解决图像旋转时的跟踪稳定性问题以及目标图像急剧膨胀时的跟踪点漂移问题,研究了图像不变矩的特征。利用图像不变矩的旋转-伸缩-平移不变性,选择合适的不变矩特征用于跟踪。通过用归一化的乘法相关函数以及基于相关系数值的模板更新策略设计跟踪算法,解决了复杂背景及强噪声条件下不能对尺寸和形状发生变化的目标进行稳定跟踪的问题。     

Misadjustment and tracking analysis of the constant modulus array

The constant modulus (CM) array is a blind adaptive beamformer that can separate cochannel signals. A follow-on adaptive signal canceler may be used to perform direction finding of the source captured by the array. In this paper, we analyze the convergence and tracking properties of the CM array using a least-mean-square approximation. Expressions are derived for the misadjustment of the adaptive algorithms, and a tracking model is developed that accurately predicts the behavior of the system during fades. It is demonstrated that the adaptive canceler contributes more to the overall misadjustment than does the adaptive CM beamformer. Computer simulations are presented to illustrate the transient properties of the system and to verify the analytical results     

A set of algorithms linking NLMS and block RLS algorithms

This paper describes a set of block processing algorithms which contains as extremal cases the normalized least mean squares (NLMS) and the block recursive least squares (BRLS) algorithms. All these algorithms use small block lengths, thus allowing easy implementation and small input-output delay. It is shown that these algorithms require a lower number of arithmetic operations than the classical least mean squares (LMS) algorithm, while converging much faster. A precise evaluation of the arithmetic complexity is provided, and the adaptive behavior of the algorithm is analyzed. Simulations illustrate that the tracking characteristics of the new algorithm are also improved compared to those of the NLMS algorithm. The conclusions of the theoretical analysis are checked by simulations, illustrating that, even in the case where noise is added to the reference signal, the proposed algorithm allows altogether a faster convergence and a lower residual error than the NLMS algorithm. Finally, a sample-by-sample version of this algorithm is outlined, which is the link between the NLMS and recursive least squares (RLS) algorithms     

Online Bayesian estimation of transition probabilities for Markovian jump systems

Markovian jump systems (MJSs) evolve in a jump-wise manner by switching among simpler models, according to a finite Markov chain, whose parameters are commonly assumed known. This paper addresses the problem of state estimation of MJS with unknown transition probability matrix (TPM) of the embedded Markov chain governing the jumps. Under the assumption of a time-invariant but random TPM, an approximate recursion for the TPMs posterior probability density function (PDF) within the Bayesian framework is obtained. Based on this recursion, four algorithms for online minimum mean-square error (MMSE) estimation of the TPM are derived. The first algorithm (for the case of a two-state Markov chain) computes the MMSE estimate exactly, if the likelihood of the TPM is linear in the transition probabilities. Its computational load is, however, increasing with the data length. To limit the computational cost, three alternative algorithms are further developed based on different approximation techniques-truncation of high order moments, quasi-Bayesian approximation, and numerical integration, respectively. The proposed TPM estimation is naturally incorporable into a typical online Bayesian estimation scheme for MJS [e.g., generalized pseudo-Bayesian (GPB) or interacting multiple model (IMM)]. Thus, adaptive versions of MJS state estimators with unknown TPM are provided. Simulation results of TPM-adaptive IMM algorithms for a system with failures and maneuvering target tracking are presented.     

Factorized All-Pass Based IIR Adaptive Notch Filters

This paper introduces a new family of infinite impulse response adaptive notch filters that forms multiple notches using a second-order factorization of an all-pass transfer function. The new orthogonal realization is amenable for adaptive filtering to obtain the unknown frequencies of interest. Two new adaptive filtering algorithms are presented that can achieve fast convergence at low computational cost. Local convergence analysis for the new algorithms is performed, and a detailed discussion of their properties is provided. The new all-pass based notch realization introduces a different compromise between bias and signal-to-noise ratio (SNR) when compared with realizations previously reported in the literature. Specifically, it achieves lower bias than other approaches at low SNR. This property is particularly attractive for the estimation and tracking of multiple sinusoids. Furthermore, the bias can be made arbitrarily small or can be accurately estimated and compensated for. Extensive computer simulations are provided to illustrate the performance of the proposed adaptive notch filters in terms of bias, speed of convergence, and tracking capability.     

Continuous-time tracking algorithms involving two-time-scale Markov chains

This work is concerned with least-mean-squares (LMS) algorithms in continuous time for tracking a time-varying parameter process. A distinctive feature is that the true parameter process is changing at a fast pace driven by a finite-state Markov chain. The states of the Markov chain are divisible into a number of groups. Within each group, the transitions take place rapidly; among different groups, the transitions are infrequent. Introducing a small parameter into the generator of the Markov chain leads to a two-time-scale formulation. The tracking objective is difficult to achieve. Nevertheless, a limit result is derived yielding algorithms for limit systems. Moreover, the rates of variation of the tracking error sequence are analyzed. Under simple conditions, it is shown that a scaled sequence of the tracking errors converges weakly to a switching diffusion. In addition, a numerical example is provided and an adaptive step-size algorithm developed.     

Tracking control of biomathematical model of muscular vessel

Based on the backstepping design and Nussbaum-type gain function methods, the tracking control problem of chaotic muscular vessel systems with parameter uncertainties and external disturbances is addressed. The derived adaptive robust tracking controller guarantees that the closed-loop system is globally and uniformly bounded, and the tracking error is convergent to a small neighbourhood of zero. In addition, the singular problem of the controller can be avoided. Simulation results demonstrate the validity of this developed controller.     

Blind Multiuser Detection in Multirate CDMA Based on Cyclic LMS Adaptation

The problem of blind adaptive multiuser detection in multirateCDMA systems is considered. Indeed, since symboldetection in multirate CDMA systems requires periodicallytime-varying processing of the observables, classical LMS and RLSadaptive algorithms, which assume that the solution to be trackedis time-invariant or slowly time-varying, are not suited for blindadaptive multiuser detection in a multirate system. While a cyclicRLS algorithm has recently appeared in the literature, thispaper focuses on the development of LMS-based cyclic filteringalgorithms. In particular, cyclic versions of the standard LMSalgorithm, of the LMS algorithm with iterate averaging and of theLMS algorithm with adaptive step-size are derived. Interestingly,the last two algorithms are shown to exhibit a convergence speed close to thatof the cyclicRLS procedure, but with an order of magnitude lower computationalcomplexity.An adaptive procedure for the automatic selection ofthe algorithm periodicity is also presented, which is based on aminimum mean-output-energy criterion, and that obviates theneed for knowledge of the transmitted data-rates from theinterfering signals.Moreover, the case of known multipathfading channels is also examined. In particular, it is shown that theproposed cyclic LMS algorithms can be used to achieve RLS-likeperformance also in the presence of multipath distortion.Extensive computer simulation results, along with some analyticalconvergence results, confirmthat the proposed algorithms are effective and achieve very satisfactoryperformance.     

Fast adaptive blind beamforming algorithm for antenna array in CDMA systems

In this paper, the maximum signal-to-interference-plus-noise ratio (MSINR) beamforming problem in antenna-array CDMA systems is considered. In this paper, a modified MSINR criterion presented in a previous paper is interpreted as an unconstrained scalar cost function. By applying recursive least squares (RLS) to minimize the cost function, a novel blind adaptive beamforming algorithm to estimate the beamforming vector, which optimally combines the desired signal contributions from different antenna elements while suppressing noise and interference, is derived. Neither the knowledge of the channel conditions (fading coefficients, signature sequences and timing of interferers, statistics of other noises, etc.) nor training sequence is required. Compared with previously published adaptive beamforming algorithms based on the stochastic-gradient method, it has faster convergence and better tracking capability in the time-varying environment. Simulation results in various signal environments are presented to show the performance of the proposed algorithm.     

Tracking of time-varying channels using two-step LMS-type adaptive algorithm

This paper presents a modified version of the two-step least-mean-square (LMS)-type adaptive algorithm motivated by the work of Gazor. We describe the nonstationary adaptation characteristics of this modified two-step LMS (MG-LMS) algorithm for the system identification problem. It ensures stable behavior during convergence as well as improved tracking performance in the smoothly time-varying environments. The estimated weight increment vector is used for the prediction of weight vector for the next iteration. The proposed modification includes the use of a control parameter to scale the estimated weight increment vector in addition to a smoothing parameter used in the two-step LMS (G-LMS) algorithm, which controls the initial oscillatory behavior of the algorithm. The analysis focuses on the effects of these parameters on the lag-misadjustment in the tracking process. The mathematical analysis for a nonstationary case, where the plant coefficients are assumed to follow a first-order Markov process, shows that the MG-LMS algorithm contributes less lag-misadjustment than the conventional LMS and G-LMS algorithms. Further, the stability criterion imposes upper bound on the value of the control parameter. These derived analytical results are verified and demonstrated with simulation examples, which clearly show that the lag-misadjustment reduces with increasing values of the smoothing and control parameters under permissible limits.     

Fast adaptive algorithms for AR parameters estimation using higherorder statistics

Time-varying statistics in linear filtering and linear estimation problems necessitate the use of adaptive or time-varying filters in the solution. With the rapid availability of vast and inexpensive computation power, models which are non-Gaussian even nonstationary are being investigated at increasing intensity. Statistical tools used in such investigations usually involve higher order statistics (HOS). The classical instrumental variable (IV) principle has been widely used to develop adaptive algorithms for the estimation of ARMA processes. Despite, the great number of IV methods developed in the literature, the cumulant-based procedures for pure autoregressive (AR) processes are almost nonexistent, except lattice versions of IV algorithms. This paper deals with the derivation and the properties of fast transversal algorithms. Hence, by establishing a relationship between classical (IV) methods and cumulant-based AR estimation problems, new fast adaptive algorithms, (fast transversal recursive instrumental variable-FTRIV) and (generalized least mean squares-GLMS), are proposed for the estimation of AR processes. The algorithms are seen to have better performance in terms of convergence speed and misadjustment even in low SNR. The extra computational complexity is negligible. The performance of the algorithms, as well as some illustrative tracking comparisons with the existing adaptive ones in the literature, are verified via simulations. The conditions of convergence are investigated for the GLMS     

Recursive expectation-maximization (EM) algorithms for time-varyingparameters with applications to multiple target tracking

We investigate the application of expectation maximization (EM) algorithms to the classical problem of multiple target tracking (MTT) for a known number of targets. Conventional algorithms, which deal with this problem, have a computational complexity that depends exponentially on the number of targets, and usually divide the problem into a localization stage and a tracking stage. The new algorithms achieve a linear dependency and integrate these two stages. Three optimization criteria are proposed, using deterministic and stochastic dynamic models for the targets     

Fault Tolerant Control for a Class of Nonlinear Systems with Application to Near Space Vehicle

In this paper, a fault tolerant control (FTC) scheme, which is based on backstepping and neural network (NN) methodology, is proposed for a general class of nonlinear systems with known structure and unknown faults. Firstly, the linearly parameterized radial basis function (RBF) NNs are employed to approximate unknown system faults, and the network weights are adapted using adaptive on-line parameter-learning algorithms. Then an adaptive backstepping based FTC is designed to compensate for the effect of system faults. The asymptotical stability of the closed-loop system and uniform boundedness of the state tracking errors are proved according to Lyapunov theory. Finally, the designed strategy is applied to near space vehicle (NSV) attitude dynamics, and simulation results are presented to demonstrate the effectiveness of the proposed approach.     

抗冲激干扰的稀疏惩罚约束遗漏最小均方算法

当被识别系统是稀疏系统时,传统的遗漏最小均方( LLMS )自适应算法收敛性能较差,特别在非高斯噪声环境中,该算法性能进一步恶化甚至算法不平稳收敛。为了解决因信道的稀疏性使算法收敛变慢的问题,对LLMS算法的代价函数分别利用加权詛1-norm和加权零吸引两种稀疏惩罚项进行改进;为了优化算法的抗冲激干扰的性能,利用符号函数对已改进的算法迭代式作进一步改进。同时,将提出的两个算法运用于非高斯噪声环境下的稀疏系统识别,仿真结果显示提出的算法性能优于现存的同类稀疏算法。