A new fault tolerant control (FTC) via a controller reconfiguration approach for general stochastic nonlinear systems is studied. Different from the formulation of classical FTC methods, it is supposed that the measured information for the FTC is the probability density functions (PDFs) of the system output rather than its measured value. A radial basis functions (RBFs) neural network technique is proposed so that the output PDFs can be formulated in terms of the dynamic weighings of the RBFs neural network. As a result, the nonlinear FTC problem subject to dynamic relation between the input and the output PDFs can be transformed into a nonlinear FTC problem subject to dynamic relation between the control input and the weights of the RBFs neural network approximation to the output PDFs. The FTC design consists of two steps. The first step is fault detection and diagnosis (FDD), which can produce an alarm when there is a fault in the system and also locate which component has a fault. The second step is to adapt the controller to the faulty case so that the system is able to achieve its target. A linear matrix inequality (LMI) based feasible FTC method is applied such that the fault can be detected and diagnosed. An illustrated example is included to demonstrate the efficiency of the proposed algorithm, and satisfactory results have been obtained.

A novel identification method for point source, coherently distributed (CD) source and incoherently distributed (ICD) source is proposed. The differences among the point source, CD source and ICD source are studied. According to the different characters of covariance matrix and general steering vector of the array received source, a second order blind identification method is used to separate the sources, the mixing matrix could be obtained. From the mixing matrix, the type of the source is identified by using an amplitude criterion. And the direction of arrival for the array received source is estimated by using the matching pursuit algorithm from the vectors of the mixing matrix.  omputer simulations validate the efficiency of the method.

The application of data envelopment analysis (DEA) as a multiple criteria decision making (MCDM) technique has been gaining more and more attention in recent research. In the practice of applying DEA approach, the appearance of uncertainties on input and output data of decision making unit (DMU) might make the nominal solution infeasible and lead to the efficiency scores meaningless from practical view. This paper analyzes the
impact of data uncertainty on the evaluation results of DEA, and proposes several robust DEA models based on the adaptation of recently developed robust optimization approaches, which would be immune against input and output data uncertainties. The robust DEA models developed are based on input-oriented and outputoriented
CCR model, respectively, when the uncertainties appear in output data and input data separately. Furthermore, the robust DEA models could deal with random symmetric uncertainty and unknown-but-bounded uncertainty, in both of which the distributions of the random data entries are permitted to be unknown. The robust DEA models are implemented in a numerical example and the efficiency scores and rankings of these models are compared.
The results indicate that the robust DEA approach could be a more reliable method for efficiency evaluation and ranking in MCDM problems.

The aim of this paper is to develop controllers for uncertain systems in the presence of stuck type actuator failures. A new scheme is proposed to design output feedback controllers for a class of uncertain systems having redundant control inputs, with which the relative degrees of transfer functions are different. To deal with these inputs using backstepping technique, a pre-filter is introduced before each actuator such that its output is the input to the actuator. The orders of the pre-filters are chosen properly to ensure all their inputs can be designed at the same step in the systematic design. To compensate for the effects of possible failed actuators, more uncertain parameters than system parameters are required to be identified. With the proposed scheme, the global boundedness of the closed-loop system can still be ensured and the system output can be regulated to a specific value when some of the actuators' outputs are stuck at unknown fixed values.

A method of object detection based on combination of local and spatial information is proposed. Firstly, the categorygiven representative images are chosen through clustering to be templates, and the local and spatial information of template are extracted and generalized as the template feature. At the same time, the codebook dictionary of local contour is also built up. Secondly, based on the codebook dictionary, sliding-window mechanism and the vote algorithm are used to select initial candidate object windows. Lastly, the final object windows are got from initial candidate windows based on local and spatial structure feature matching. Experimental results demonstrate that the proposed approach is able to consistently identify and accurately detect the objects with better performance than the existing methods.

To solve the finite-time error-tracking problem in missile guidance, this paper presents a unified design approach through error dynamics and free-time convergence theory. The proposed approach is initiated by establishing a desired model for free-time convergent error dynamics, characterized by its independence from initial conditions and guidance parameters, and adjustable convergence time. This foundation facilitates the derivation of specific guidance laws that integrate constraints such as leading angle, impact angle, and impact time. The theoretical framework of this study elucidates the nuances and synergies between the proposed guidance laws and existing methodologies. Empirical evaluations through simulation comparisons underscore the enhanced accuracy and adaptability of the proposed laws.

Based on the characteristics of high-end products, crowd-sourcing user stories can be seen as an effective means of gathering requirements, involving a large user base and generating a substantial amount of unstructured feedback. The key challenge lies in transforming abstract user needs into specific ones, requiring integration and analysis. Therefore, we propose a topic mining-based approach to categorize, summarize, and rank product requirements from user stories. Specifically, after determining the number of story categories based on pyLDAvis, we initially classify “I want to” phrases within user stories. Subsequently, classic topic models are applied to each category to generate their names, defining each post-classification user story category as a requirement. Furthermore, a weighted ranking function is devised to calculate the importance of each requirement. Finally, we validate the effectiveness and feasibility of the proposed method using 2 966 crowd-sourced user stories related to smart home systems.

With the development of information technology, a large number of product quality data in the entire manufacturing process is accumulated, but it is not explored and used effectively. The traditional product quality prediction models have many disadvantages, such as high complexity and low accuracy. To overcome the above problems, we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model: radial basis function model optimized by the firefly algorithm with Levy flight mechanism (RBFFALM). First, the new data equalization method is introduced to pre-process the dataset, which reduces the dimension of the data, removes redundant features, and improves the data distribution. Then the RBFFALFM is used to predict product quality. Comprehensive experiments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous methods on predicting pro-duct quality.

To improve the total throughput of the uplink orthogonal frequency division multiple access system, a low complexity hybrid power distribution (HPD) combined with subcarrier allocation scheme is proposed. For the fairness mechanism for the subcarrier, the inter-cell interference is first analyzed to calculate the capacity of the multi-cell. The user selects the subcarrier with the largest channel gain. Based on the above subcarrier allocation scheme, a new kind of HPD scheme is proposed, which adopts the waterfilling-power-distributed scheme and the equal-power-distributed scheme in the cell-boundary and the cellcenter, respectively. Simulation results show that compared with the waterfilling-power-distributed scheme in the whole cell, the proposed HPD scheme decreases the system complexity significantly, meanwhile its capacity is 2% higher than that of the equal-powerdistributed scheme over the same subcarrier allocation.

An adaptive robust approach for actuator fault-tolerant control of a class of uncertain nonlinear systems is proposed. The two chief ways in which the system performance can degrade following an actuator-fault are undesirable transients and unacceptably large steady-state tracking errors. Adaptive control based schemes can achieve good final tracking accuracy in spite of change in system parameters following an actuator fault, and robust control based designs can achieve guaranteed transient response. However, neither adaptive control nor robust control based fault-tolerant designs can address both the issues associated with actuator faults. In the present work, an adaptive robust fault-tolerant control scheme is claimed to solve both the problems, as it seamlessly integrates adaptive and robust control design techniques. Comparative simulation studies are performed using a nonlinear hypersonic aircraft model to show the effectiveness of the proposed scheme over a robust adaptive control based faulttolerant scheme.

The fuzzy non-cooperative game with fuzzy payoff function is studied. Based on fuzzy set theory with game theory, the fuzzy Nash equilibrium of fuzzy non-cooperative games is proposed. Most of researchers rank fuzzy number by its center of gravity or by the real number with its maximal membership. By reducing fuzzy number into a real number, we lose much fuzzy information that should be kept during the operations between fuzzy numbers. The fuzzy quantities or alternatives are ordered directly by Yuan’s binary fuzzy ordering relation. In doing so, the existence of fuzzy Nash equilibrium for fuzzy non-cooperative games is shown based on the utility function and the crisp Nash theorem. Finally, an illustrative example in traffic flow patterns of equilibrium is given in order to show the detailed calculation process of fuzzy Nash equilibrium.

A novel class of periodically changing features hidden in radar pulse sequence environment, named G features, is proposed. Combining fractal theory and Hilbert-Huang transform, the features are extracted using changing characteristics of pulse parameters in radar emitter signals. The features can be applied in modern complex electronic warfare environment to address the issue of signal sorting when radar emitter pulse signal parameters severely or even completely overlap. Experiment results show that the proposed feature class and feature extraction method can discriminate periodically changing pulse sequence signal sorting features from radar pulse signal flow with complex variant features, therefore provide a new methodology for signal sorting.

The proposed Doppler measurement technique shows that the Doppler measurements can be accomplished by a single pulse with multiple frequency components through optical fibre delay lines. Range and velocity ambiguity can be removed, and the velocity resolution can be improved dramatically by using long optical fibre delay lines. Furthermore, the velocity resolution can be modified by adjusting the length of optical fibre delay lines. In addition, the proposed radar can achieve high range resolution by using a single wideband pulse. As a result, the new approach can improve radar performance significantly.

A new non-decoupling three-dimensional guidance law is proposed for bank-to-turn (BTT) missiles with the motion coupling problem. In this method, the different geometry is taken for theoretically modeling on BTT missiles’ motion within the threedimensional style without information loss, and meanwhile, Liegroup is utilized to describe the line-of-sight (LOS) azimuth when the terminal angular constraints are considered. Under these circumstances, a guidance kinematics model is established based on differential geometry. Then, corresponding to no terminal angular constraint and terminal angular constraints, guidance laws are respectively designed by using proportional control and generalized proportional-derivative (PD) control in SO(3) group. Eventually, simulation results validate that this developed method can effectively avoid the complexity of pure Lie-group method and the information loss of the traditional decoupling method as well.

This article deals with the uniformly globally asymptotic controllability of discrete nonlinear systems with disturbances. It is shown that the system is uniformly globally asymptotic controllability with respect to a closed set if and only if there exists a smooth control Lyapunov function. Further, it is obtained that the control Lyapunov function may be used to construct a feedback law to stabilize the closed-loop system. In addition, it is proved that for periodic discrete systems, the resulted control Lyapunov functions are also time periodic.

The rotational parameters estimation of maneuvering target is the key of cross-range scaling of ISAR (inverse synthetic aperture radar), which can be used in the target feature extraction. The cross-range signal model of rotating target with fixed acceleration is presented and the weighted linear least squares estimation of rotational parameters with fixed velocity or acceleration is proposed via the relationship of cross-range FM (frequency modulation) parameter, scatterers coordinates and rotational parameters. The FM parameter is calculated via RWT (Radon-Wigner transform). The ISAR imaging and cross-range scaling based on scaled RWT imaging method are implemented after obtaining rotational parameters. The rotational parameters estimation and cross-range scaling are validated by the ISAR processing of experimental radar data, and the method presents good application foreground to the ISAR imaging and scaling of maneuvering target.

As one of the next generation imaging spectrometers, the interferential spectrometer (IS) possesses the advantages of high throughput, multi-channel and great resolution. The data of IS are produced in the spatial domain, but optical applications are in the Fourier domain. Traditional compression methods can only protect the visual quality of interferometer data in the spatial domain but ignore the distortion in the Fourier domain. The relation between the distortion in the Fourier domain and the compression in the spatial domain is analyzed. By mathematical proof and validation with experiments, the relation between spatial and Fourier domains is discovered, and the significance in the Fourier domain is more important as optical path difference (OPD) increasing in the spatial domain. Based on this relation, a novel coding scheme is proposed, which can compress data in the spatial domain while reducing the distortion in the Fourier domain. In this scheme, the bit stream of the set partitioning in hierarchical trees (SPIHT) is truncated by adaptively lifting rate-distortion slopes according to the priorities of OPD based on rate-distortion optimization theory. Experimental results show that the proposed method can provide better protection of spectrum curves in the Fourier domain while maintaining a comparable visual quality in the spatial domain.

Low Earth orbit (LEO) satellite networks exhibit distinct characteristics, e.g., limited resources of individual satellite nodes and dynamic network topology, which have brought many challenges for routing algorithms. To satisfy quality of service (QoS) requirements of various users, it is critical to research efficient routing strategies to fully utilize satellite resources. This paper proposes a multi-QoS information optimized routing algorithm based on reinforcement learning for LEO satellite networks, which guarantees high level assurance demand services to be prioritized under limited satellite resources while considering the load balancing performance of the satellite networks for low level assurance demand services to ensure the full and effective utilization of satellite resources. An auxiliary path search algorithm is proposed to accelerate the convergence of satellite routing algorithm. Simulation results show that the generated routing strategy can timely process and fully meet the QoS demands of high assurance services while effectively improving the load balancing performance of the link.

This paper is concerned with the robust stabilization problem of networked control systems with stochastic packet
dropouts and uncertain parameters. Considering the stochastic packet dropout occuring in two channels between the sensor and the controller, and between the controller and the actuator, networked control systems are modeled as the Markovian jump linear system with four operation modes. Based on this model, the necessary and sufficient conditions for the mean square stability of the deterministic networked control systems and uncertain networked
control systems are given by using the theory of the Markovian jump linear system, and corresponding controller design procedures are proposed via the cone complementarity linearization method. Finally, the numerical example and simulations are given to illustrate the effectiveness of the proposed results.

This paper considers the problem of reference tracking control for the flexible air-breathing hypersonic flight vehicle with actuator delay and uncertainty. By constructing the Lyapunov functional including the lower and upper bounds of the time-varying delay, the non-fragile controller is designed such that the resulting closed-loop system is asymptotically stable and satisfies a prescribed performance cost index. The simulation results are given to show the effectiveness of the proposed control method, which is validated by excellent output reference altitude and velocity tracking performance.

The theory of compressed sensing (CS) provides a new chance to reduce the data acquisition time and improve the data usage factor of the stepped frequency radar system. In light of the sparsity of radar target reflectivity, two imaging methods based on CS, termed the CS-based 2D joint imaging algorithm and the CS-based 2D decoupled imaging algorithm, are proposed. These methods incorporate the coherent mixing operation into the sparse dictionary, and take random measurements in both range and azimuth directions to get high resolution radar images, thus can remarkably reduce the data rate and simplify the hardware design of the radar system while maintaining imaging quality. Experimentsfrom both simulated data and measured data in the anechoic chamber show that the proposed imaging methods can get more focused images than the traditional fast Fourier transform method. Wherein the joint algorithm has stronger robustness and can provide clearer inverse synthetic aperture radar images, while the decoupled algorithm is computationally more efficient but has slightly degraded imaging quality, which can be improved by increasing measurements or using a robuster recovery algorithm nevertheless

The fault diagnosis problem is investigated for a class of nonlinear neutral systems with multiple disturbances. Time-varying faults are considered and multiple disturbances are supposed to include the unknown disturbance modeled by an exo-system and norm bounded uncertain disturbance. A nonlinear disturbance observer is designed to estimate the modeled disturbance. Then, the fault diagnosis observer is constructed by integrating disturbance observer with disturbance attenuation and rejection performances. The augmented Lyapunov functional approach, which involves the tuning parameter and slack variable, is applied to make the solution of inequality more flexible. Finally, applications for a two-link robotic manipulator system are given to show the efficiency of the proposed approach.

For the estimation of MIMO frequency selective channel, to mitigate the curse of dimensionality, a novel particle filtering scheme combined with time delay domain processing is proposed. In order to extract the time delay domain channel impulse response from the observed signal, the least-squares (LS) and minimum mean squared error (MMSE) criteria are discussed and the comparable performance of LS with MMSE for samplespaced channel is revealed. Incorporated the dynamical channel model, gradient particle filtering is further introduced to improve the estimation performance. The robustness of the channel estimator for underestimated Doppler frequency and the effectiveness of the new estimation scheme are illustrated through simulation at last.

During a sea firing training, the intelligent detection of projectile-induced water column targets in a firing video is the prerequisite for and critical to the automatic calculation of miss distance, while the correct and precise calculation of miss distance is directly affected by the accuracy, false alarm rate and time delay of detection. After analyzing the characteristics of projectile-induced water columns, an accurate detection algorithm for time backtracked projectile-induced water columns based on the improved you only look once (YOLO) network is put forward. The capability and accuracy of detecting projectile-induced water column targets with the conventional YOLO network are improved by optimizing the anchor box through K-means clustering and embedding the squeeze and excitation (SE) attention module. The detection area is limited by adopting a sea-sky line detection algorithm based on gray level co-occurrence matrix (GLCM), so as to effectively eliminate such disturbances as ocean waves and ship wakes, and lower the false alarm rate of projectile-induced water column detection. The improved algorithm increases the mAP₅₀ of water column detection by 30.3%. On the basis of correct detection, a time backtracking algorithm is designed with mean shift to track images containing projectile-induced water column in reverse time sequence. It accurately detects a projectile-induced water column at the time of its initial appearance as well as its pixel position in images, and considerably reduces detection delay, so as to provide the support for the automatic, accurate, and real-time calculation of miss distance.

Multi-objective optimization (MOO) for the microwave metamaterial absorber (MMA) normally adopts evolutionary algorithms, and these optimization algorithms require many objective function evaluations. To remedy this issue, a surrogate-based MOO algorithm is proposed in this paper where Kriging models are employed to approximate objective functions. An efficient sampling strategy is presented to sequentially capture promising samples in the design region for exact evaluations. Firstly, new sample points are generated by the MOO on surrogate models. Then, new samples are captured by exploiting each objective function. Furthermore, a weighted sum of the improvement of hypervolume (IHV) and the distance to sampled points is calculated to select the new sample. Compared with two well-known MOO algorithms, the proposed algorithm is validated by benchmark problems. In addition, two broadband MMAs are applied to verify the feasibility and efficiency of the proposed algorithm.

The quality of synthetic aperture radar (SAR) image degrades in the case of multiple imaging projection planes (IPPs) and multiple overlapping ship targets, and then the performance of target classification and recognition can be influenced. For addressing this issue, a method for extracting ship targets with overlaps via the expectation maximization (EM) algorithm is proposed. First, the scatterers of ship targets are obtained via the target detection technique. Then, the EM algorithm is applied to extract the scatterers of a single ship target with a single IPP. Afterwards, a novel image amplitude estimation approach is proposed, with which the radar image of a single target with a single IPP can be generated. The proposed method can accomplish IPP selection and targets separation in the image domain, which can improve the image quality and reserve the target information most possibly. Results of simulated and real measured data demonstrate the effectiveness of the proposed method.

Overlooking the issue of false alarm suppression in heterogeneous change detection leads to inferior detection performance. This paper proposes a method to handle false alarms in heterogeneous change detection. A lightweight network of two channels is bulit based on the combination of convolutional neural network (CNN) and graph convolutional network (GCN). CNNs learn feature difference maps of multitemporal images, and attention modules adaptively fuse CNN-based and graph-based features for different scales. GCNs with a new kernel filter adaptively distinguish between nodes with the same and those with different labels, generating change maps. Experimental evaluation on two datasets validates the efficacy of the proposed method in addressing false alarms.

The single-carrier block transmission (SCBT), a.k.a., single-carrier frequency-domain equalization (SC-FDE), is being considered as an option technique for the wireless personal area network (WPAN) operating at 60 GHz. It is found that for residential environment, in non-line-of-sight (NLOS) multi-path channels, the SCBT is much more effective to combat the inter-symbol interference (ISI) compared with orthogonal frequency division multiplexing (OFDM). Low-density parity-check (LDPC) codes are a class of linear block codes which provide near capacity performance on a large collection of data transmission and storage channels while simultaneously admitting implementable decoders. To facilitate using LDPC codes for SCBT system, a new log-likelihood ratio (LLR) calculation method is proposed based on pilot symbols (PS). Golay Sequences whose sum autocorrelation has a unique peak and zero sidelobe are used for creating the PS. The position and length of the PS are not fixed in the data blocks. The simulation results show that the proposed method can significantly improve the LDPC decoding performance in SCBT system. This is very promising to support ultra high-data-rate wireless transmission.

A new robust fault-tolerant controller scheme integrating a main controller and a compensator for the self-repairing flight control system is discussed. The main controller is designed for high performance of the original faultless system. The compensating controller can be seen as a standalone loop added to the system to compensate the effects of fault guaranteeing the stability of the system. A design method is proposed using nonlinear dynamic inverse control as the main controller and nonlinear extended state observer-based compensator. The stability of the whole closed-loop system is analyzed. Feasibility and validity of the new controller is demonstrated with an aircraft simulation example.

Combining beamlet transform with steerable filters, a new edge detection method based on line gradient is proposed. Compared with operators based on point local properties, the edge-detection results with this method achieve higher SNR and position accuracy, and are quite helpful for image registration, object identification, etc. Some edge-detection experiments on optical and SAR images that demonstrate the significant improvement over classical edge operators are also presented. Moreover, the template matching result based on edge information of optical reference image and SAR image also proves the validity of this method.

An efficient algorithm is proposed for computing the solution to the constrained finite time optimal control (CFTOC) problem for discrete-time piecewise affine (PWA) systems with a quadratic performance index. The maximal positively invariant terminal set, which is feasible and invariant with respect to a feedback control law, is computed as terminal target set and an associated Lyapunov function is chosen as terminal cost. The combination of these two components guarantees constraint satisfaction and closed-loop stability for all time. The proposed algorithm combines a dynamic programming strategy with a multi-parametric quadratic programming solver and basic polyhedral manipulation. A numerical example shows that a larger stabilizable set of states can be obtained by the proposed algorithm than precious work.

Without considering security, existing message scheduling mechanisms may expose critical messages to malicious threats like confidentiality attacks. Incorporating confidentiality improvement into message scheduling, this paper investigates the problem of scheduling aperiodc messages with time-critical and security-critical requirements. A risk-based security profit model is built to quantify the security quality of messages; and a dynamic programming based approximation algorithm is proposed to schedule aperiodic messages with guaranteed security performance. Experimental results illustrate the efficiency and effectiveness of the proposed algorithm.

The relationship between the technique by statedependent Riccati equations (SDRE) and Hamilton-Jacobi-Isaacs (HJI) equations for nonlinear H∞ control design is  investigated. By establishing the Lyapunov matrix equations for partial derivates of the solution of the SDREs and introducing symmetry measure for some related matrices, a method is proposed for examining whether the SDRE method admits a global optimal control equivalent to that solved by the HJI equation method. Two examples with simulation are given to illustrate the method is effective.

By employing function one-direction S-rough sets and rough law generation method based on function S-rough sets, ¯ f-decomposition law and ¯ F-decomposition rough law are proposed, and the measurement of rough law variation in the process of rough law ¯ F-decomposition is researched. The concepts of law energy and attribute ¯ f-interference degree are presented, which make the variation of rough law become measurable. ¯ f-decomposition law energy characteristic theorem, ¯ fdecomposition law energy inequality theorem, ¯ F-decomposition rough law energy characteristic theorem, and ¯ f-decomposition law energy mean value theorem are presented.

Tree topologies, which construct spatial graphs with large characteristic path lengths and small clustering coefficients, are ubiquitous in deployments of wireless sensor networks. Small worlds are investigated in tree-based networks. Due to link additions, characteristic path lengths reduce rapidly and clustering coefficients increase greatly. A tree abstract, Cayley tree, is considered for the study of the navigation algorithm, which runs automatically in the small worlds of tree-based networks. In the further study, epidemics in the small worlds of tree-based wireless sensor networks on the large scale are studied, and the percolation threshold is calculated, at which the outbreak of the epidemic takes place. Compared with Cayley tree, there is a smaller percolation threshold suffering from the epidemic.

A new filtering method for SAR data de-noising using wavelet support vector regression (WSVR) is developed. On the basis of the grey scale distribution character of SAR imagery, the logarithmic SAR image as a noise polluted signal is taken and the noise model assumption in logarithmic domain with Gaussian noise and impact noise is proposed. Based on the better performance of support vector regression (SVR) for complex signal approximation and the wavelet for signal detail expression, the wavelet kernel function is chosen as support vector kernel function. Then the logarithmic SAR image is regressed with WSVR. Furthermore the regression distance is used as a judgment index of the noise type. According to the judgment of noise type every pixel can be adaptively de-noised with different filters. Through an approximation experiment for a one-dimensional complex signal, the feasibility of SAR data regression based on WSVR is confirmed. Afterward the SAR image is treated as a two-dimensional continuous signal and filtered by an SVR with wavelet kernel function. The results show that the method proposed here reduces the radar speckle noise effectively while maintaining edge features and details well.

H-infinity estimator is generally implemented in timevariant state-space models, but it leads to high complexity when the model is used for multiple input multiple output with orthogonal frequency division multiplexing (MIMO-OFDM) systems. Thus, an H-infinity estimator over time-invariant system models is proposed, which modifies the Krein space accordingly. In order to avoid the large matrix inversion and multiplication required in each OFDM symbol from different transmit antennas, expectation maximization
(EM) is developed to reduce the high computational load. Joint estimation over multiple OFDM symbols is used to resist the high pilot overhead generated by the increasing number of transmit antennas. Finally, the performance of the proposed estimator is enhanced via an angle-domain process. Through performance analysis and simulation experiments, it is indicated that the proposed algorithm has a better mean square error (MSE) and bit error rate (BER) performance than the optimal least square (LS)
estimator. Joint estimation over multiple OFDM symbols can not only reduce the pilot overhead but also promote the channel performance. What is more, an obvious improvement can be obtained by using the angle-domain filter.