Fault Estimation for a Class of Markov Jump Piecewise-Affine Systems: Current Feedback Based Iterative Learning Approach

In this paper, the issues of stochastic stability analysis and fault estimation are investigated for a class of continuoustime Markov jump piecewise-affine(PWA) systems against actuator and sensor faults. Firstly, a novel mode-dependent PWA iterative learning observer with current feedback is designed to estimate the system states and faults, simultaneously, which contains both the previous iteration information and the current feedback mechanism. The auxiliary feedback channel optimizes the response speed of the observer, therefore the estimation error would converge to zero rapidly. Then, sufficient conditions for stochastic stability with guaranteed performance are demonstrated for the estimation error system, and the equivalence relations between the system information and the estimated information can be established via iterative accumulating representation.Finally, two illustrative examples containing a class of tunnel diode circuit systems are presented to fully demonstrate the effectiveness and superiority of the proposed iterative learning observer with current feedback.

Time Delay Estimation in Radar System using Fuzzy Based Iterative Unscented Kalman Filter

RSs(Radar Systems)identify and trace targets and are commonly employed in applications like air traffic control and remote sensing.They are necessary for monitoring precise target trajectories.Estimations of RSs are non-linear as the parameters TDEs(time delay Estimations)and Doppler shifts are computed on receipt of echoes where EKFs(Extended Kalman Filters)and UKFs(Unscented Kalman Filters)have not been examined for computations.RSs,certain times result in poor accuracies and SNRs(low signal to noise ratios)especially,while encountering complicated environments.This work proposes IUKFs(Iterated UKFs)to track onlinefilter performances while using optimization techniques to enhance outcomes.The use of cost functions can assist state corrections while lowering costs.A new parameter is optimized using MCEHOs(Mutation Chaotic Elephant Herding Optimizations)by linearly approximating system non-linearity where OIUKFs(Optimized Iterative UKFs)predict a target's unknown parameters.To obtain optimal solutions theoretically,OIUKFs take less iteration,resulting in shorter execution times.The proposed OIUKFs provide numerical approximations which are derivative-free implementations.Simulation evaluation results with estimators show better performances in terms of reduced NMSEs(Normalized Mean Square Errors),RMSEs(Root Mean Squared Errors),SNRs,variances,and better accuracies than current approaches.

An enhanced iterative joint channel estimation and symbol detection algorithm for OFDM systems

For orthogonal frequency division multiplexing (OFDM) wireless communication, the system throughput and data rate are usually limited by pilots, especially in a high mobility environment. In this paper, an enhanced iterative joint channel estimation and symbol detection algorithm is proposed to enhance the system throughput and data rate. With lower pilot power, the proposed scheme increases system throughput firstly, and then the channel estimation and symbol detection proceed iteratively within one OFDM symbol to improve the BER performance. In the proposed algorithm, the original channel estimate of each OFDM symbol is based on the channel estimate of the previous OFDM symbol, thus the variation of the mobile channel is traced efficiently, so the number of pilots in the time domain can be reduced greatly. Besides reducing the system overhead, the proposed algorithm is also shown by simulation to give much better BER performance than the conventional iterative algorithm does.

Iterative learning based fault detection and estimation in nonlinear systems

Aiming at a class of nonlinear systems that contains faults, a novel iterative learning scheme is applied to fault detec- tion, and a novel algorithm of fault detection and estimation is proposed. This algorithm first constructs residual signals by the output of the practical system and the output of the designed fault tracking estimator, and then uses the residuals and the difference- value signal of the adjacent two residuals to gradually revise the introduced virtual faults, which can cause the virtual faults to close to the practical faults in systems, thereby achieving the goal of fault detection for systems. This algorithm not only makes full use of the existing valid information of systems and has a faster tracking con- vergent speed than the proportional-type （P-type） algorithm, but also calculates more simply than the proportional-derivative-type （PD-type） algorithm and avoids the unstable effects of differential operations in the system. The final simulation results prove the validity of the proposed algorithm.

Feedback strategies for iterative channel estimation in OFDM underwater acoustic communications

The extremely limited bandwidth in underwater acoustic communication makes channel estimation using fewer pilot symbols more challenging. Iterative channel estimation( ICE) can be used to refine channel estimation with limited number of pilots,by coupling the channel estimator with channel decoder. In this paper,various feedback strategies in ICE are discussed. The performance of a decision feedback based on the cost function is improved by modifying the design and another four feedback strategies are summarized,including hard/soft decision feedback and their threshold-controlled versions. Simulation results show that ICE can achieve impressive gains over the non-iterative receiver and the gains are more significant with fewer pilots. Furthermore,soft decision feedback outperforms hard decision feedback; while the feedback based on the cost function and soft decision feedback have quite close performance.

Non-cooperative target pose estimation based on improved iterative closest point algorithm

For localisation of unknown non-cooperative targets in space,the existence of interference points causes inaccuracy of pose estimation while utilizing point cloud registration.To address this issue,this paper proposes a new iterative closest point(ICP)algorithm combined with distributed weights to intensify the dependability and robustness of the non-cooperative target localisation.As interference points in space have not yet been extensively studied,we classify them into two broad categories,far interference points and near interference points.For the former,the statistical outlier elimination algorithm is employed.For the latter,the Gaussian distributed weights,simultaneously valuing with the variation of the Euclidean distance from each point to the centroid,are commingled to the traditional ICP algorithm.In each iteration,the weight matrix W in connection with the overall localisation is obtained,and the singular value decomposition is adopted to accomplish high-precision estimation of the target pose.Finally,the experiments are implemented by shooting the satellite model and setting the position of interference points.The outcomes suggest that the proposed algorithm can effectively suppress interference points and enhance the accuracy of non-cooperative target pose estimation.When the interference point number reaches about 700,the average error of angle is superior to 0.88°.

CHANNEL ESTIMATION FOR ITERATIVE DECODING OVER FADING CHANNELS

A method of coherent detection and channel estimation for punctured convolutional coded binary Quadrature Amplitude Modulation (QAM) signals transmitted over a frequency-flat Rayleigh fading channels used for a digital radio broadcasting transmission is presented. Some known symbols are inserted in the encoded data stream to enhance the channel estimation process.The pilot symbols are used to replace the existing parity symbols so no bandwidth expansion is required. An iterative algorithm that uses decoding information as well as the information contained in the known symbols is used to improve the channel parameter estimate. The scheme complexity grows exponentially with the channel estimation filter length. The performance of the system is compared for a normalized fading rate with both perfect coherent detection (corresponding to a perfect knowledge of the fading process and noise variance) and differential detection of Differential Amplitude Phase Shift Keying (DAPSK). The tradeoff between simplicity of implementation and bit-error-rate performance of different techniques is also compared.

A Method for Assessing Customer Harmonic Emission Level Based on the Iterative Algorithm for Least Square Estimation

With the power system harmonic pollution problems becoming more and more serious, how to distinguish the harmonic responsibility accurately and solve the grid harmonics simply and effectively has become the main development direction in harmonic control subjects. This paper, based on linear regression analysis of basic equation and improvement equation, deduced the least squares estimation (LSE) iterative algorithm and obtained the real-time estimates of regression coefficients, and then calculated the level of the harmonic impedance and emission estimates in real time. This paper used power system simulation software Matlab/Simulink as analysis tool and analyzed the user side of the harmonic amplitude and phase fluctuations PCC (point of common coupling) at the harmonic emission level, thus the research has a certain theoretical significance. The development of this algorithm combined with the instrument can be used in practical engineering.

Iterative Reweighted <i>l</i>₁Penalty Regression Approach for Line Spectral Estimation

In this paper, we proposed an iterative reweighted l1?penalty regression approach to solve the line spectral estimation problem. In each iteration process, we first use the ideal of Bayesian lasso to update the sparse vectors;the derivative of the penalty function forms the regularization parameter. We choose the anti-trigonometric function as a penalty function to approximate the?l0? norm. Then we use the gradient descent method to update the dictionary parameters. The theoretical analysis and simulation results demonstrate the effectiveness of the method and show that the proposed algorithm outperforms other state-of-the-art methods for many practical cases.

Comparison of Iterative Wavefront Estimation Methods

The iterative reconstruction methods of the wavefront phase estimation from a set of discrete phase slope measurements have been considered. The values of the root-mean-square difference between the reconstructed and original wavefront have been received for Jacobi, Gauss-Seidel, Successive over-relaxation and Successive over-relaxation with Simpson Reconstructor methods. The method with the highest accuracy has been defined.

ITERATIVE RECEIVER FOR OFDM SYSTEMS BASED ON SEMI-BLIND CHANNEL ESTIMATION

In this paper we propose two iterative algorithms of joint channel estimation and symbol detection for Orthogonal Frequency Division Multiplexing (OFDM) systems. In which, superimposed pilot scheme is adopted and an initial Channel State Information (CSI) is obtained by employing a first-order statistic. In each subsequent iteration, we propose two algorithms to update the CSI. The Mean Square Error (MSE) of channel estimation and Bit Error Rate (BER) performance are given and simulation results demonstrate that the iterative algorithm using method B has good perform-ance approaching the ideal condition.

A LAW OF THE ITERATED LOGARITHM FOR NEAREST NEIGHBOR ESTIMATION OF MULTIVARIATE DENSITY FUNCTION

Let X be a d-dimensional random vector with unknown density function f(z) = f (z1, ..., z(d)), and let f(n) be teh nearest neighbor estimator of f proposed by Loftsgaarden and Quesenberry (1965). In this paper, we established the law of the iterated logarithm of f(n) for general case of d greater-than-or-equal-to 1, which gives the exact pointwise strong convergence rate of f(n).

Iterative receivers for SFBC-OFDM systems with adaptive training scheme

This paper considers the design of iterative receivers for space-frequencyblock-coded orthogonal frequency division multiplexing (SFBC-OFDM) systems in unknown wirelessdispersive fading channels. An iterative joint channel estimation and symbol detection algorithm isderived. In the algorithm, the channel estimator performs alternately in two modes. During thetraining mode, the channel state information (CSI) is obtained by a discrete-Fourier-transform-basedchannel estimator and the noise variance and covariance matrix of the channel response is estimatedby the proposed method. In the data transmission mode, the CSI and transmitted data is obtainediteratively. In order to suppress the error propagation caused by a random error in identifyingsymbols, a simple error propagation detection criterion is proposed and an adaptive training schemeis applied to suppress the error propagation. Both theoretical analysis and simulation results showthat this algorithm gives better bit-error-rate performance and saves the overhead of OFDM systems.

Reduced-complexity multiple parameters estimation via toeplitz matrix triple iteration reconstruction with bistatic MIMO radar

In this advanced exploration, we focus on multiple parameters estimation in bistatic Multiple-Input Multiple-Output(MIMO) radar systems, a crucial technique for target localization and imaging. Our research innovatively addresses the joint estimation of the Direction of Departure(DOD), Direction of Arrival(DOA), and Doppler frequency for incoherent targets. We propose a novel approach that significantly reduces computational complexity by utilizing the TemporalSpatial Nested Sampling Model(TSNSM). Our methodology begins with a multi-linear mapping mechanism to efficiently eliminate unnecessary virtual Degrees of Freedom(DOFs) and reorganize the remaining ones. We then employ the Toeplitz matrix triple iteration reconstruction method, surpassing the traditional Temporal-Spatial Smoothing Window(TSSW) approach, to mitigate the single snapshot effect and reduce computational demands. We further refine the highdimensional ESPRIT algorithm for joint estimation of DOD, DOA, and Doppler frequency, eliminating the need for additional parameter pairing. Moreover, we meticulously derive the Cramér-Rao Bound(CRB) for the TSNSM. This signal model allows for a second expansion of DOFs in time and space domains, achieving high precision in target angle and Doppler frequency estimation with low computational complexity. Our adaptable algorithm is validated through simulations and is suitable for sparse array MIMO radars with various structures, ensuring higher precision in parameter estimation with less complexity burden.

Iterative learning based fault diagnosis for discrete linear uncertain systems

In order to detect and estimate faults in discrete lin-ear time-varying uncertain systems, the discrete iterative learning strategy is applied in fault diagnosis, and a novel fault detection and estimation algorithm is proposed. And the threshold limited technology is adopted in the proposed algorithm. Within the chosen optimal time region, residual signals are used in the proposed algorithm to correct the introduced virtual faults with iterative learning rules, making the virtual faults close to these occurred in practical systems. And the same method is repeated in the rest optimal time regions, thereby reaching the aim of fault diagnosis. The proposed algorithm not only completes fault detection and estimation for discrete linear time-varying uncertain systems, but also improves the reliability of fault detection and decreases the false alarm rate. The final simulation results verify the validity of the proposed algorithm.

Efficient Iterative Method for Solving the General Restricted Linear Equation

An iterative method is developed for solving the solution of the general restricted linear equation. The convergence, stability, and error estimate are given. Numerical experiments are presented to demonstrate the efficiency and accuracy.

A New Method for Pose Estimation from Line Correspondences

我们能通常在一个一般位置从三根线决定目标的姿势。三的配置非 -- 在二个点交叉的共面的线有一些特别特征，在一个一般位置的三根线没有它。这里，我们在场用这种特别的线配置决定目标姿势的一个新方法。在理论，这个方法从三线通讯充实姿势评价方法。另外，它为实际应用提供指导。而且，我们建议一个方法处理多答案现象和一个新反复的方法。模拟结果证明我们的算法快速地并且要用体力地工作。

Estimates of Convergence Rate of Parallel Multisplitting Itertive Methods

This paper givers an estimated formula of convergence rate for parallel multisplitting iterative method.Using the formula,we can simplify and unify the proof of convergence of PMI_method.

A Hybrid Estimation of Distribution Algorithm for Unrelated Parallel Machine Scheduling with Sequence-Dependent Setup Times

A hybrid estimation of distribution algorithm (EDA) with iterated greedy (IG) search (EDA-IG) is proposed for solving the unrelated parallel machine scheduling problem with sequence-dependent setup times (UPMSP-SDST). For makespan criterion, some properties about neighborhood search operators to avoid invalid search are derived. A probability model based on neighbor relations of jobs is built in the EDA-based exploration phase to generate new solutions by sampling the promising search region. Two types of deconstruction and reconstruction as well as an IG search are designed in the IG-based exploitation phase. Computational complexity of the algorithm is analyzed, and the effect of parameters is investigated by using the Taguchi method of design-of-experiment. Numerical tests on 1640 benchmark instances are carried out. The results and comparisons demonstrate the effectiveness of the EDA-IG. Especially, the bestknown solutions of 531 instances are updated. In addition, the effectiveness of the properties is also demonstrated by numerical comparisons. © 2014 Chinese Association of Automation.