With the emergence of location-based applications in various fields, the higher accuracy of positioning is demanded. By utilizing the time differences of arrival (TDOAs) and gain ratios of arrival (GROAs), an efficient algorithm for estimating the position is proposed, which exploits the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton method to solve nonlinear equations at the source location under the additive measurement error. Although the accuracy of two-step weighted-least-square (WLS) method based on TDOAs and GROAs is very high, this method has a high computational complexity. While the proposed approach can achieve the same accuracy and bias with the lower computational complexity when the signal-to-noise ratio (SNR) is high, especially it can achieve better accuracy and smaller bias at a lower SNR. The proposed algorithm can be applied to the actual environment due to its real-time property and good robust performance. Simulation results show that with a good initial guess to begin with, the proposed estimator converges to the true solution and achieves the Cramer-Rao lower bound (CRLB) accuracy for both near-field and far-field sources.

Vehicle positioning with the global navigation satellite system (GNSS) in urban environments faces two problems which are attenuation and dynamic. For traditional GNSS receivers hardly able to track dynamic weak signals, the coupling between all visible satellite signals is ignored in the absence of navigation state feedback, and thermal noise error and dynamic stress threshold are contradictory due to non-coherent discriminators. The vector delay/ frequency locked loop (VDFLL) with navigation state feedback and the joint vector tracking loop (JVTL) with coherent discriminator which is a synchronization parameter tracking loop based on maximum likelihood estimation (MLE) are proposed to improve the tracking sensitivity of GNSS receiver in dynamic weak signal environments. A joint vector position tracking loop (JVPTL) directly tracking user position and velocity is proposed to further improve tracking sensitivity. The coherent navigation parameter discriminator of JVPTL, being able to ease the contradiction between thermal noise error and dynamic stress threshold, is based on MLE according to the navigation parameter based linear model of received baseband signals. Simulation results show that JVPTL, which combines the advantages of both VDFLL and JVTL, performs better than both VDFLL and JVTL in dynamic weak signal environments.

A novel direct sequence (DS) design method for suppressing the narrowband interference in DS spread spectrum ultra-wideband receivers is proposed. The method has low computational complexity and can be easily implemented in practical systems. Simulation results prove that the proposed method is effective to suppress the narrowband interference. Therefore, the integrity of both the ultra-wide bandwidth and the narrowband systems can be highly enhanced.

Only in the presence of sidelobe jamming (SLJ), can the conventional adaptive monopulse technique null the jamming effectively and maintain the monopulse angle estimation accuracy simultaneously. While mainlobe jamming (MLJ) exists, the mainlobe of adaptive pattern will subject to serious distortion, which results in a failure of detecting and tracking targets by monopulse technique. Therefore, a monopulse angle estimation algorithm based on combining sum-difference beam and auxiliary beam is
presented. This algorithm utilizes both high gain difference beams and high gain auxiliary beams for cancelling the mainlobe jammer and multiple sidelobe jammers (SLJs) while keeping an adaptive monopulse ratio. Theoretical analysis and simulation results indicate that the serious invalidation of monopulse technique in MLJ and SLJs scenarios is resolved well, which improves the monopulse angle accuracy greatly. Furthermore, the proposed algorithm is of simple implementation and low computational complexity.

Low power and real time very large scale integration (VLSI) architectures of motion estimation (ME) algorithms for mobile devices and applications are presented. The power reduction is achieved by devising a novel correction recovery mechanism based on algorithms which allow the use of reduced bit sum of absolute difference (RBSAD) metric for calculating matching error and conversion to full resolution sum of absolute difference (SAD) metric whenever necessary. Parallel and pipelined architectures for high throughput of full search ME corresponding to both the full resolution SAD and the generalized RBSAD algorithm are synthesized using Xilinx Synthesis Tools (XST), where the ME designs based on reduced bit (RB) algorithms demonstrate the reduction in power consumption up to 45% and/or the reduction in area up to 38%.

Speckle effects on classification results can be suppressed to some extent by introducing the contextual information. An unsupervised classification algorithm is proposed for polarimetric synthetic aperture radar (POLSAR) images based on the mean shift (MS) segmentation and Markov random field (MRF). First, polarimetric features are exacted by target decomposition for MS segmentation. An initial classification is executed by using the target decomposition and the agglomerative hierarchical clustering algorithm. Thereafter, a classification step based on MRF is performed by using the mean coherence matrices obtained for each segment. Under the MRF framework, the smoothness term is defined according to the distance between neighboring areas. By using POLSAR images acquired by the German Aerospace Centre and National Aeronautics and Space Administration/Jet Propulsion Laboratory, the experimental results confirm that the proposed method has higher accuracy and better regional connectivity
than other classification methods.

In the distributed synthetic aperture radar (SAR), the alternating bistatic mode can perform phase reference without a synchronization link between two satellites compared with the pulsed alternate synchronization method. The key of the phase synchronization processing is to extract the oscillator phase differences from the bistatic echoes. A signal model of phase synchronization in the alternating bistatic mode is presented. The phase synchronization processing method is then studied. To reduce the phase errors introduced by SAR imaging, a sub-aperture processing method is proposed. To generalize the sub-aperture processing method, an echo-domain processing method using correlation of bistatic echoes is proposed. Finally, the residual phase errors of the both proposed processing methods are analyzed. Simulation experiments validate the proposed phase synchronization processing method and its phase error analysis results.

A novel multi-baseline phase unwrapping algorithm based on the unscented particle filter for interferometric synthetic aperture radar (INSAR) technology application is proposed. The proposed method is not constrained by the nonlinearity of the problem and is independent of noise statistics, and performs noise eliminating and phase unwrapping at the same time by combining with an unscented particle filter with a path-following strategy and an omni-directional local phase slope estimator. Results obtained from multi-baseline synthetic data and single-baseline real data show the performance of the proposed method.

Attribute reduction in the rough set theory is an important feature selection method, but finding a minimum attribute reduction has been proven to be a non-deterministic polynomial (NP)-hard problem. Therefore, it is necessary to investigate some fast and effective approximate algorithms. A novel and enhanced quantum-inspired shuffled frog leaping based minimum attribute reduction algorithm (QSFLAR) is proposed. Evolutionary frogs are represented by multi-state quantum bits, and both quantum rotation gate and quantum mutation operators are used to exploit the mechanisms of frog population diversity and convergence to the global optimum. The decomposed attribute subsets are co-evolved by the elitist frogs with a quantum-inspired shuffled frog leaping algorithm. The experimental results validate the better feasibility and effectiveness of QSFLAR, comparing with some representative algorithms. Therefore, QSFLAR can be considered as a more competitive algorithm on the efficiency and accuracy for minimum attribute reduction.

Multi-living agent system (MLAS) is a new concept in the field of complex system research, which is peculiarly suitable for the design and analysis of a complex information system in a serious confrontation and tight constraint environment. However, the universal method to quantitatively measure the living degree of an MLAS remains uncertain, which is critical to the self-organizing process. Therefore, a novel analytic hierarchy process (AHP) based method with dependent pairwise comparison matrix (PCM) for the evaluation of living degree of the MLAS is proposed, which eliminates the shortcoming of fixed PCM in traditional process. Furthermore, to avoid the annoying procedure of the consistency validation, the PCMs are appropriately reconstructed. Through an illustration of the netted radar system, the calculation detail is explicitly presented. Altogether, the advanced evaluation method successfully accomplishes the preset objective and promotes the development of the MLAS theory and AHP as well.

Firstly, the research progress of grey model GM (1,1) is summarized, which is divided into three development stages: assimilation, alienation and melting stages. Then, the matrix analysis theory is used to study the modeling mechanism of GM (1,1), which decomposes the modeling data matrix into raw data transformation matrix, accumulated generating operation matrix and background value selection matrix. The changes of these three matrices are the essential reasons affecting the modeling and the accuracy of GM (1,1). Finally, the paper proposes a generalization grey model GGM (1,1), which is a extended form of GM (1,1) and also a unified form of model GM (1,1), model GM (1,1,α), stage grey model, hopping grey model, generalized accumulated model, strengthening operator model, weakening operator model and unequal interval model. And the theory and practical significance of the extended model is analyzed.

Space electromagnetic docking technology, free of propellant and plume contamination, offers continuous, reversible and synchronous controllability, which is widely applied in the future routine on-orbit servicing missions. Due to the inherent nonlinearities, couplings and uncertainties of an electromagnetic force model, the dynamics and control problems of them are difficult. A new modeling approach for relative motion dynamics with intersatellite force is proposed. To resolve these control problems better, a novel nonlinear control method for soft space electromagnetic docking is proposed, which combines merits of artificial potential function method, Lyapunov theory and extended state observer. In addition, the angular momentum management problem of space electromagnetic docking and approaches of handling it by exploiting the Earth’s magnetic torque are investigated. Finally, nonlinear simulation results demonstrate the feasibility of the dynamic model and the novel nonlinear control method.

This paper describes an adaptive control approach for an air-breathing hypersonic vehicle. The control objective is to provide robust altitudes and velocity tracking in the presence of model uncertainties and varying disturbances. A fuzzy-neural disturbance observer is developed to estimate uncertainties and disturbances, and the adaptive controller is synthesized by the dynamic surface approach combing with the observer. The tracking error at the steady state can be guaranteed to converge to inside of a small residue set which the size of the set can be an arbitrary small value. Simulation results demonstrate the effectiveness of the presented approach.

Distributed stereoscopic rotating formation control of networks of second-order agents is investigated. A distributed control protocol is proposed to enable all agents to form a stereoscopic formation and surround a common axis. Due to the existence of the rotating mode, the desired relative position between every two agents is time-varying, and a Lyapunov-based approach is employed to solve the rotating formation control problem. Finally, simulation results are provided to illustrate the effectiveness of the theoretical results.

The problem of fault-tolerant control is discussed for the longitudinalmodel of an airbreathing hypersonic vehicle (AHV) with actuator faults and external disturbances. Firstly, a fault-tolerant control strategy is presented for the longitudinal model of an AHV, which guarantees that velocity and altitude track their reference trajectories at an exponential convergence rate. However, this method needs to know the minimum value of the actuator efficiency factor and the upper bound of the external disturbances, which makes it not easy to implement. Then an improved adaptive fault-tolerant control scheme is proposed, where two adaptive laws are employed to estimate the upper bound of the external disturbances and the minimum value of the actuator efficiency factor, respectively. Secondly, the problem of designing a control scheme with control constraints is further considered, and a new adaptive fault-tolerant control strategy with input saturation is designed to guarantee that velocity and altitude track their reference trajectories. Finally, simulation results are given to show the effectiveness of the proposed methods.

For the fault detection and diagnosis problem in largescale industrial systems, there are two important issues: the missing data samples and the non-Gaussian property of the data. However, most of the existing data-driven methods cannot be able to handle both of them. Thus, a new Bayesian network classifier based fault detection and diagnosis method is proposed. At first, a non-imputation method is presented to handle the data incomplete samples, with the property of the proposed Bayesian network classifier, and the missing values can be marginalized in an elegant manner. Furthermore, the Gaussian mixture model is used to approximate the non-Gaussian data with a linear combinationof finite Gaussian mixtures, so that the Bayesian network can process the non-Gaussian data in an effective way. Therefore, the entire fault detection and diagnosis method can deal with the high-dimensional incomplete process samples in an efficient and robust way. The diagnosis results are expressed in the manner of probability with the reliability scores. The proposed approach is evaluated with a benchmark problem called the Tennessee Eastman process. The simulation results show the effectiveness and robustness of the proposed method in fault detection and diagnosis for large-scale systems with missing measurements.

This paper deals with the problem of optimal fault detection filter (FDF) design for a class of discrete-time switched linear systems under arbitrary switching. By using an observer-based FDF as a residual generator, the design of the FDF is formulated into an optimization problem through maximizing the H−/H∞ or H∞/H∞ performance index. With the aid of an operator optimization method, it is shown that a mode-dependent unified optimal solution can be derived by solving a coupled Riccati equation. A numerical example is given to show the effectiveness of the proposed method.

The problems of robust exponential stability in mean square and delayed state feedback stabilization for uncertain stochastic systems with time-varying delay are studied. By using Jensen’s integral inequality and combining with the free weighting matrix approach, new delay-dependent stability conditions and delayed state feedback stabilization criteria are obtained in terms of linear matrix inequalities. Meanwhile, the proposed delayed state feedback stabilization criteria are more convenient in application than the existing ones since fewer tuning parameters are involved. Numerical examples are given to illustrate the effectiveness of the proposed methods.

In the methods of image thresholding segmentation, such methods based on two-dimensional (2D) histogram and optimal objective functions are important. However, when they are used for infrared image segmentation, they are weak in suppressing background noises and worse in segmenting targets with non-uniform gray level. The concept of 2D histogram shape modification is proposed, which is realized by target information prior restraint after enhancing target information using plateau histogram equalization. The formula of 2D minimum Renyi entropy is deduced for image segmentation, then the shape-modified 2D histogram is combined with four optimal objective functions (i.e., maximum between-class variance, maximum entropy, maximum correlation and minimum Renyi entropy) respectively for the application of infrared image segmentation. Simultaneously, F-measure is introduced to evaluate the segmentation effects objectively. The experimental results show that F-measure is an effective evaluation index for image segmentation since its value is fully consistent with the subjective evaluation, and after 2D histogram shape modification, the methods of optimal objective functions can overcome their original forms’ deficiency and their segmentation effects are more or less improvements, where the best one is the maximum entropy method based on 2D histogram shape modification.

Particle filters have been widely used in nonlinear/non- Gaussian Bayesian state estimation problems. However, efficient distribution of the limited number of particles in state space remains a critical issue in designing a particle filter. A simplified unscented particle filter (SUPF) is presented, where particles are drawn partly from the transition prior density (TPD) and partly from the Gaussian approximate posterior density (GAPD) obtained by a unscented Kalman filter. The ratio of the number of particles drawn from TPD to the number of particles drawn from GAPD is adaptively determined by the maximum likelihood ratio (MLR). The MLR is defined to measure how well the particles, drawn from the TPD, match the likelihood model. It is shown that the particle set generated by this sampling strategy is more close to the significant region in state space and tends to yield more accurate results. Simulation results demonstrate that the versatility and estimation accuracy of SUPF exceed that of standard particle filter,
extended Kalman particle filter and unscented particle filter.

This paper proposes a hybrid approach for recognizing human activities from trajectories. First, an improved hidden Markov model (HMM) parameter learning algorithm, HMM-PSO, is proposed, which achieves a better balance between the global and local exploitation by the nonlinear update strategy and repulsion operation. Then, the event probability sequence (EPS) which consists of a series of events is computed to describe the unique characteristic of human activities. The analysis on EPS indicates that it is robust to the changes in viewing direction and contributes to improving the recognition rate. Finally, the effectiveness of the proposed approach is evaluated by data experiments on current popular datasets.