The problem of channel estimation for multiple antenna orthogonal frequency division multiplexing (OFDM) systems subject to unknown carrier frequency offset (CFO) is addressed. Multiple signal classification (MUSIC)-like algorithm, which generally has been used for direction estimation or frequency estimation, is used for channel estimation in multiple antenna OFDM systems. A reduced dimensional (RD)-MUSIC based algorithm for channel estimation is proposed in multiple antenna OFDM systems with unknown CFO. The Cram´er-Rao bound (CRB) of channel estimation in multiple antenna OFDM systems with unknown CFO is derived. The proposed algorithm has a superior performance of channel estimation compared with the Capon method and the least squares method.

The 16-ary quadrature amplitude modulation (16QAM) is a high spectral efficient scheme for high-speed transmission systems. To remove the phase ambiguity in the coherent detection system, differential-encoded 16QAM (DE-16QAM) is usually used, however, it will cause performance degradation about 3 dB as compared to the conventional 16QAM. To overcome the performance loss, a serial concatenated system with outer low density parity check (LDPC) codes and inner DE-16QAM is proposed. At the receiver, joint iterative differential demodulation and decoding (ID) is carried out to approach the maximum likelihood performance. Moreover, a genetic evolution algorithm based on the extrinsic information transfer chart is proposed to optimize the degree distribution of the outer LDPC codes. Both theoretical analyses and simulation results indicate that this algorithm not only compensates the performance loss, but also obtains a significant performance gain, which is up to 1 dB as compared to the conventional non-DE-16QAM.

Automatic modulation classification is the process of identification of the modulation type of a signal in a general environment. This paper proposes a new method to evaluate the tracking performance of large margin classifier against signal-tonoise ratio (SNR), and classifies all forms of primary user’s signals in a cognitive radio environment. For achieving this objective, two structures of a large margin are developed in additive white Gaussian noise (AWGN) channels with priori unknown SNR. A combination of higher order statistics and instantaneous characteristics is selected as effective features. Simulation results show that the classification rates of the proposed structures are well robust against environmental SNR changes.

To improve the recognition rate of signal modulation recognition methods based on the clustering algorithm under the low SNR, a modulation recognition method is proposed. The characteristic parameter of the signal is extracted by using a clustering algorithm, the neural network is trained by using the algorithm of variable gradient correction (Polak-Ribiere) so as to enhance the rate of convergence, improve the performance of recognition under the low SNR and realize modulation recognition of the signal based on the modulation system of the constellation diagram. Simulation results show that the recognition rate based on this algorithm is enhanced over 30% compared with the methods that adopt clustering algorithm or neural network based on the back propagation algorithm alone under the low SNR. The recognition rate can reach 90% when the SNR is 4 dB, and the method is easy to be achieved so that it has a broad application prospect in the modulating recognition.

Walsh-Hadamard transform (WHT) can solve linear error equations on Field F2, and the method can be used to recover the parameters of convolutional code. However, solving the equations with many unknowns needs enormous computer memory which limits the application of WHT. In order to solve this problem, a method based on segmented WHT is proposed in this paper. The coefficient vector of high dimension is reshaped and two vectors of lower dimension are obtained. Then the WHT is operated and the requirement for computer memory is much reduced. The code rate and the constraint length of convolutional code are detected from the Walsh spectrum. And the check vector is recovered from the peak position. The validity of the method is verified by the simulation result, and the performance is proved to be optimal.

This paper addresses the issue of the direction of arrival (DOA) estimation under the compressive sampling (CS) framework. A novel approach, modified multiple signal classification (MMUSIC) based on the CS array (CSA-MMUSIC), is proposed to resolve the DOA estimation of correlated signals and two closely adjacent signals. By using two random CS matrices, a large size array is compressed into a small size array, which effectively reduces the number of the front end circuit. The theoretical analysis demonstrates that the proposed approach has the advantages of low computational complexity and hardware structure compared to other MMUSIC approaches. Simulation results show that CSAMMUSIC can possess similar angular resolution as MMUSIC.

For increasing the cross-track resolution, the multiple input multiple output (MIMO) technique is introduced into the swath bathymetry system and a new swath bathymetry approach using MIMO sonar is proposed. The MIMO sonar is composed of two parallel transmitting uniform linear arrays (ULAs) and a receiving ULA which is perpendicular to the former. The spacing between the two transmitting ULAs is equal to the product of the receiving sensor number and the receiving inter-sensor spacing. Furthermore, two narrowband linear frequency modulation (LFM) pulses, sharing the same frequency band but with opposite modulation slopes, are used as transmitting waveforms of the two transmitting ULAs. With such an array layout and transmitting signals, the MIMO sonar can sound a swath with the cross-track resolution doubling that of the traditional multibeam sonar using a Mills cross array. Numerical examples are provided to verify the effectiveness of the proposed approach.

The signal to noise ratio (SNR) of seismic waves is usually very low after long distance transmission. For this condition, to improve the bearing estimation capability in the low SNR, a frequency domain polarization weighted ESPRIT method using a single vector device is proposed. The frequency domain polarization parameters extracted from the signals are used to design the weighted function which is applied to the received signals. The bearing angle and the target frequency are estimated through ESPRIT using the weighted signals. The simulation and experiment results show that the presented method can obtain accurate estimation values under the low SNR with little prior information.

An infrared (IR) imaging simulation framework based on the strap-down platform is proposed for midcourse ballistic targets. It overcomes the shortcoming of the existing algorithms, which cannot simulate IR imaging from the entire midcourse process. The proposed framework includes three steps, target characteristic modeling, motion modeling, and imaging modeling. In imaging modeling, the staring focal plane is taken into account due to its wide employment. In order to obtain IR images of high fidelity, especially that the fluctuation of the target signal-to-noise ratio (SNR) is reasonably similar to the actual one, this paper proposes an improved IR imaging simulation method. The proposed method considers two critical factors of the pixel plane, occupy-empty ratio and defect elements, which affect the imaging of targets markedly but are neglected in previous work. Finally, the IR image sequence of high fidelity is obtained. And the correlative parameters of simulation can be set according to the given scene. Thus the generated images can satisfy the needs of algorithms validation for tracking and recognition.

This paper focuses on the general case (GC) airborne bistatic synthetic aperture radar (SAR) data processing, and a new analytical imaging algorithm based on the extended Loffeld’s bistatic formula (ELBF) is proposed. According to the bistatic SAR geometry, the track decoupling formulas that convert the bistatic geometry to the receiver-referenced geometry in a concise way are derived firstly. Then phase terms of ELBF are decomposed into two independent phase terms as the range phase term and the azimuth phase term in a new way. To get the focusing result, the bistatic deformation (BD) term is compensated in the two-dimensional (2-D) frequency domain, and the space-variances of the range phase term and the azimuth phase term are eliminated by chirp scaling (CS) and chirp z-transform (CZT), respectively. The effectiveness of the proposed algorithm is verified by the simulation results.

In order to realize the elevation angle estimation for low-altitude targets at a low computational cost, a generalized multiple signal classification (GMUSIC) algorithm based on unitary transform is proposed, i.e., the DU-GMUSIC algorithm. Firstly, the covariance matrix of received data is used to construct the Centro-Hermitian matrix. Then, the real-domain GMUSIC algorithm is used to implement the initial angle estimation, and the multipath attenuation coefficient is calculated in succession. Finally, the attenuation coefficient is taken into account in the GMUSIC method to carry out the secondary angle estimation which is beneficial to further improvement of the angle estimation accuracy. This method can meet requirements of low-angle accuracy as well as lower computational burden. Simulation results prove the correctness and effectiveness of the proposed algorithm. Moreover, field experiment data are used to further validate the effectiveness of this method.

Due to the limited transmission resources for data relay in the tracking and data relay satellite system (TDRSS), there are many job requirements in busy days which will be discarded in the conventional job scheduling model. Therefore, the improvement of scheduling efficiency in the TDRSS can not only help to increase the resource utilities, but also to reduce the scheduling failure ratio. A model of nonhomogeneous parallel machines scheduling problems with time window (NPM-TW) is firstly built up for the TDRSS, considering the distinct features of the variable preparation time and the nonhomogeneous transmission rates for different types of antennas on each tracking and data relay satellite (TDRS). Then, an adaptive subsequence adjustment (ASA) framework with evolutionary asymmetric path-relinking (EvAPR) is proposed to solve this problem, in which an asymmetric progressive crossover operation is involved to overcome the local optima by the conventional job inserting methods. The numerical results show that, compared with the classical greedy randomized adaptive search procedure (GRASP) algorithm, the scheduling failure ratio of jobs can be reduced over 11% on average by the proposed ASA with EvAPR.

This study concentrates on management problems of the new generation of the agile earth observation satellite (AEOS). AEOS is a key study object in many countries because of its many advantages over non-agile satellites. Hence, the mission planning and scheduling of AEOS is a popular research problem. This research investigates AEOS characteristics and establishes a mission planning model based on the working principle and constraints of AEOS as per analysis. To solve the scheduling issue of AEOS, several improved algorithms are developed. Simulation results suggest that these algorithms are effective.

The electromagnetic detection satellite (EDS) is a type of earth observation satellites (EOSs). The Information collected by EDSs plays an important role in some fields, such as industry, science and military. The scheduling of EDSs is a complex combinatorial optimization problem. Current research mainly focuses on the scheduling of imaging satellites and SAR satellites, but little work has been done on the scheduling of EDSs for its specific characteristics. A multi-satellite scheduling model is established, in which the specific constrains of EDSs are considered, then a scheduling algorithm based on the genetic algorithm (GA) is proposed. To deal with the specific constrains of EDSs, a penalty function method is introduced. However, it is hard to determine the appropriate penalty coefficient in the penalty function. Therefore, an adaptive adjustment mechanism of the penalty coefficient is designed to solve the problem, as well as improve the scheduling results. Experimental results are used to demonstrate the correctness and practicability of the proposed scheduling algorithm.

This paper aims to study a novel expansion discrete grey forecasting model, which could aggregate input information more effectively. In general, existing multi-factor grey forecasting models, such as one order and h variables grey forecasting model (GM (1, h)), always aggregate the main system variable and independent variables in a linear form rather than a nonlinear form, while a nonlinear form could be used in more cases than the linear form. And the nonlinear form could aggregate collinear independent factors, which widely lie in many multi-factor forecasting problems. To overcome this problem, a new approach, named as the Solow residual method, is proposed to aggregate independent factors. And a new expansion model, feedback multi-factor discrete grey forecasting model based on the Solow residual method (abbreviated as FDGM (1, h)), is proposed accordingly. Then the feedback control equation and the parameters’ solution of the FDGM (1, h) model are given. Finally, a real application is used to test the modelling accuracy of the FDGM (1, h) model. Results show that the FDGM (1, h) model is much better than the nonhomogeneous discrete grey forecasting model (NDGM) and the GM (1, h) model.

To overcome the too fine-grained granularity problem of multivariate grey incidence analysis and to explore the comprehensive incidence analysis model, three multivariate grey incidences degree models based on principal component analysis (PCA) are proposed. Firstly, the PCA method is introduced to extract the feature sequences of a behavioral matrix. Then, the grey incidence analysis between two behavioral matrices is transformed into the similarity and nearness measure between their feature sequences. Based on the classic grey incidence analysis theory, absolute and relative incidence degree models for feature sequences are constructed, and a comprehensive grey incidence model is proposed. Furthermore, the properties of models are researched. It proves that the proposed models satisfy the properties of translation invariance, multiple transformation invariance, and axioms of the grey incidence analysis, respectively. Finally, a case is studied. The results illustrate that the model is effective than other multivariate grey incidence analysis models.

A novel high-order sliding mode control strategy is proposed for the attitude control problem of reentry vehicles in the presence of parametric uncertainties and external disturbances, which results in the robust and accurate tracking of the aerodynamic angle commands with the finite time convergence. The proposed control strategy is developed on the basis of integral sliding mode philosophy, which combines conventional sliding mode control and a linear quadratic regulator over a finite time interval with a free-final-state and allows the finite-time establishment of a high-order sliding mode. Firstly, a second-order sliding mode attitude controller is designed in the proposed high-order siding mode control framework. Then, to address the control chattering problem, a virtual control is introduced in the control design and hence a third-order sliding mode attitude controller is developed, leading to the chattering reduction as well as the control accuracy improvement. Finally, simulation examples are given to illustrate the effectiveness of the theoretical results.

This paper investigates a distributed coordination control scheme using an adaptive terminal sliding mode for formation flying spacecraft with coupled attitude and translational dynamics. In order to overcome the singularity of the traditional fast terminal sliding manifold, a novel fast terminal sliding manifold is given. And then, based on the adaptive control method, a continuous robust coordinated controller is designed to compensate external disturbances and to alleviate the chattering phenomenon. The theoretical analysis shows that the coordinated controller can guarantee the finite-time stability of the overall closed-loop system through local information exchange, and numerical simulations also demonstrate its effectiveness.

The extended optimal guidance law with terminal constraints of miss distance and impact angle is derived by the Schwartz inequality. To reduce terminal acceleration and eliminate gravity disturbance absolutely, the object function, which designs the weight of control command to be the power function of time-to-go’s reciprocal, is given. And the gravity is considered when building the state equation. Based on the parsing express of the guidance command change with varying time and adjoint system analysis method, the command characteristics and the non-dimensional miss distance of the guidance law are analyzed, a design principle of guidance order coefficients is discussed. Finally, based on the requirement of engineering, the method to calculate the guidance condition and maximal required acceleration of the guidance law is given. The simulation demonstrates that not only the guidance law can satisfy the terminal position and impact angle constraints, but also the terminal acceleration can be converged toward zero, which will support a good situation for the terminal angle of attacking control.

This paper addresses a problem of observer-based sensor fault reconstruction for continuous-time systems subject to sensor faults and measurement disturbances via a descriptor system approach. An augmented descriptor plant is first formulated, by assembling measurement disturbances and sensor faults into an auxiliary state vector. Then a novel descriptor state observer for the augmented plant is constructed such that simultaneous reconstruction of original system states, sensor faults and measurement disturbances are obtained readily. Sufficient conditions for the existence of the proposed observer are explicitly provided, and the application scope of the observer is further discussed. In addition, an extension of the proposed linear approach to a class of nonlinear systems with Lipschitz constraints is investigated. Finally, two numerical examples are simulated to illustrate the effectiveness of the proposed fault-reconstructing approaches.

While it is very reasonable to use a multigraph consisting of multiple edges between vertices to represent various relationships, the multigraph has not drawn much attention in research. To visualize such a multigraph, a clear layout representing a global structure is of great importance, and interactive visual analysis which allows the multiple edges to be adjusted in appropriate ways for detailed presentation is also essential. A novel interactive two-phase approach to visualizing and exploring multigraph is proposed. The approach consists of two phases: the first phase improves the previous popular works on force-directed methods to produce a brief drawing for the aggregation graph of the input multigraph, while the second phase proposes two interactive strategies, the magnifier model and the thematic-oriented subgraph model. The former highlights the internal details of an aggregation edge which is selected interactively by user, and draws the details in a magnifying view by cubic Bezier curves; the latter highlights only the thematic subgraph consisting of the selected multiple edges that the user concerns. The efficiency of the proposed approach is demonstrated with a real-world multigraph dataset and how it is used effectively is discussed for various potential applications.

A method for fast l-fold cross validation is proposed for the regularized extreme learning machine (RELM). The computational time of fast l-fold cross validation increases as the fold number decreases, which is opposite to that of naive l-fold cross validation. As opposed to naive l-fold cross validation, fast l-fold cross validation takes the advantage in terms of computational time, especially for the large fold number such as l > 20. To corroborate the efficacy and feasibility of fast l-fold cross validation, experiments on five benchmark regression data sets are evaluated.

Optimal distribution feeder reconfiguration (DFR) is a valuable and costless approach to increase the load balance, reduce the amount of power losses, and improve the voltage of the buses. In this way, this paper aims to investigate the optimal DFR strategy as a proper tool to improve the reliability of the radial distribution networks. The idea of failure rate reduction is employed to see the effect of feeder current reduction on the reliability of the system more accurately. The objects to be investigated are system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), average energy not supplied (AENS) and total active power losses. The problem is then formulated in a stochastic framework based on the point estimate method (PEM) to handle the uncertainty effects. The feasibility and satisfying performance of the proposed method is examined on a standard IEEE test system.