Aiming at the reference range selection for different antennas in interferometric inverse synthetic aperture radar (InISAR) systems, this paper proposes a respective focusing (RF) method. The reference ranges for echoes of different antennas are selected respectively for RF, which is different from the traditional uniform focusing (UF) with the same reference range applied to all the antennas. First, a comparison between UF and RF for InISAR signal model considering the ranging error is given. Compared with RF, UF has an advantage in overcoming the ranging error differences between different antennas. Then the influence of ranging error upon the interferometric imaging with RF is investigated particularly, and it is found that the ranging error differences between different antennas are far smaller than the wavelength, which is advantageous to imaging. By comparing the capabilities of interferometric imaging between RF and UF, it is concluded that RF is a better choice in conquering problems such as image mismatching and phase ambiguity even with ranging errors. Simulations demonstrate the validity of the proposed method.
The finite set statistics provides a mathematically rigorous single target Bayesian filter (STBF) for tracking a target that generates multiple measurements in a cluttered environment. However, the target maneuvers may lead to the degraded tracking performance and even track loss when using the STBF. The multiple-model technique has been generally considered as the mainstream approach to maneuvering the target tracking. Motivated by the above observations, we propose the multiple-model extension of the original STBF, called MM-STBF, to accommodate the possible target maneuvering behavior. Since the derived MMSTBF involve multiple integrals with no closed form in general, a sequential Monte Carlo implementation (for generic models) and a Gaussian mixture implementation (for linear Gaussian models) are presented. Simulation results show that the proposed MM-STBF outperforms the STBF in terms of root mean squared errors of dynamic state estimates.
To improve the agility, dynamics, composability, reusability, and development efficiency restricted by monolithic federation object model (FOM), a modular FOM is proposed by high level architecture (HLA) evolved product development group. This paper reviews the state-of-the-art of HLA evolved modular FOM. In particular, related concepts, the overall impact on HLA standards, extension principles, and merging processes are discussed. Also permitted and restricted combinations, and merging rules are provided, and the influence on HLA interface specification is given. The comparison between modular FOM and base object model (BOM) is performed to illustrate the importance of their combination. The applications of modular FOM are summarized. Finally, the significance to facilitate compoable simulation both in academia and practice is presented and future directions are pointed out.
The incoherent control of finite-level quantum systems is investigated. Following a brief introduction to coherent control paradigms in quantum control, a control problem that can not be accomplished using only coherent control is presented. For such a control problem, it is proved that it can be accomplished using incoherent control based on projective measurement and coherent control for two classes of finite-level quantum systems, i.e., eigenstate controllable quantum systems and wavefunction controllable quantum systems.
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.
The problem of global robust asymptotical stability for a class of Takagi-Sugeno fuzzy neural networks (TSFNN) with discontinuous activation functions and time delays is investigated by using Lyapunov stability theory. Based on linear matrix inequalities (LMIs), we originally propose robust fuzzy control to guarantee the global robust asymptotical stability of TSFNNs. Compared with the existing literature, this paper removes the assumptions on the neuron activations such as Lipschitz conditions, bounded, monotonic increasing property or the right-limit value is bigger than the left one at the discontinuous point. Thus, the results are more general and wider. Finally, two numerical examples are given to show the effectiveness of the proposed stability results.
This paper is concerned with controller design of networked control systems (NCSs) with both network-induced delay and arbitrary packet dropout. By using a packet-loss-dependent Lyapunov function, sufficient conditions for state/output feedback stabilization and corresponding control laws are derived via a switched system approach. Different from the existing results, the proposed stabilizing controllers design is dependent on the packet loss occurring in the last two transmission intervals due to the network-induced delay. The cone complementary linearation (CCL) methodology is used to solve the non-convex feasibility problem by formulating it into an optimization problem subject to linear matrix inequality (LMI) constraints. Numerical examples and simulations are worked out to demonstrate the effectiveness and validity of the proposed techniques.
Sparse representation has recently been proved to be a powerful tool in image processing and object recognition. This paper proposes a novel small target detection algorithm based on this technique. By modelling a small target as a linear combination of certain target samples and then solving a sparse 0-minimization problem, the proposed apporach successfully improves and optimizes the small target representation with innovation. Furthermore, the sparsity concentration index (SCI) is creatively employed to evaluate the coefficients of each block representation and simpfy target identification. In the detection frame, target samples are firstly generated to constitute an over-complete dictionary matrix using Gaussian intensity model (GIM), and then sparse model solvers are applied to finding sparse representation for each sub-image block. Finally, SCI lexicographical evalution of the entire image incorparates with a simple threshold locate target position. The effectiveness and robustness of the proposed algorithm are demonstrated by the exprimental results.
In the spaceborne/airborne forward-looking bistatic synthetic aperture radar (SA-FBSAR), due to the system platforms’ remarkable velocity difference and the forward-looking mode, the range cell migration (RCM) not only depends on the target’s twodimensional location, but also varies with the range location nonlinearly. And the nonlinearity is not just the slight deviation from the linear part, but exhibits evident nonlinear departure in the RCM trajectory. If the RCM is not properly corrected, nonlinear image distortions would occur. Based on the RCM model, a modified two-step RCM compensation (RCMC) method for SA-FBSAR is proposed. In this method, firstly the azimuth-dependent RCM is compensated by the scaling Fourier transform and the phase multiplication. And then the range-dependent RCM is removed through interpolation. The effectiveness of the proposed RCMC method is verified by the simulation results of both point scatterers and area targets.
As the solutions of the least squares support vector regression machine (LS-SVRM) are not sparse, it leads to slow prediction speed and limits its applications. The defects of the existing adaptive pruning algorithm for LS-SVRM are that the training speed is slow, and the generalization performance is not satisfactory, especially for large scale problems. Hence an improved algorithm is proposed. In order to accelerate the training speed, the pruned data point and fast leave-one-out error are employed to validate the temporary model obtained after decremental learning. The novel objective function in the termination condition which involves the whole constraints generated by all training data points and three pruning strategies are employed to improve the generalization performance. The effectiveness of the proposed algorithm is tested on six benchmark datasets. The sparse LS-SVRM model has a faster training speed and better generalization performance.
With the development of global position system (GPS), wireless technology and location aware services, it is possible to collect a large quantity of trajectory data. In the field of data mining for moving objects, the problem of anomaly detection is a hot topic. Based on the development of anomalous trajectory detection of moving objects, this paper introduces the classical trajectory outlier detection (TRAOD) algorithm, and then proposes a density-based trajectory outlier detection (DBTOD) algorithm, which compensates the disadvantages of the TRAOD algorithm that it is unable to detect anomalous defects when the trajectory is local and dense. The results of employing the proposed algorithm to Elk1993 and Deer1995 datasets are also presented, which show the effectiveness of the algorithm.
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.
The pruning algorithms for sparse least squares support vector regression machine are common methods,and easily com- prehensible,but the computational burden in the training phase is heavy due to the retraining in performing the pruning process, which is not favorable for their applications.To this end,an im- proved scheme is proposed to accelerate sparse least squares support vector regression machine.A major advantage of this new scheme is based on the iterative methodology,which uses the previous training results instead of retraining,and its feasibility is strictly verified theoretically.Finally,experiments on bench- mark data sets corroborate a significant saving of the training time with the same number of support vectors and predictive accuracy compared with the original pruning algorithms,and this speedup scheme is also extended to classification problem.
Most of the existing non-line-of-sight (NLOS) localization methods depend on the layout information of the scene which is difficult to be obtained in advance in the practical application scenarios. To solve the problem, an NLOS target localization method in unknown L-shaped corridor based ultra-wideband (UWB) multiple-input multiple-output (MIMO) radar is proposed in this paper. Firstly, the multipath propagation model of L-shaped corridor is established. Then, the localization process is analyzed by the propagation characteristics of diffraction and reflection. Specifically, two different back-projection imaging processes are performed on the radar echo, and the positions of focus regions in the two images are extracted to generate candidate targets. Furthermore, the distances of propagation paths corresponding to each candidate target are calculated, and then the similarity between each candidate target and the target is evaluated by employing two matching factors. The locations of the targets and the width of the corridor are determined based on the matching rules. Finally, two experiments are carried out to demonstrate that the method can effectively obtain the target positions and unknown scene information even when partial paths are lost.
This survey presents a comprehensive review of various methods and algorithms related to passing-through control of multi-robot systems in cluttered environments. Numerous studies have investigated this area, and we identify several avenues for enhancing existing methods. This survey describes some models of robots and commonly considered control objectives, followed by an in-depth analysis of four types of algorithms that can be employed for passing-through control: leader-follower formation control, multi-robot trajectory planning, control-based methods, and virtual tube planning and control. Furthermore, we conduct a comparative analysis of these techniques and provide some subjective and general evaluations.
The observing failure and feedback instability might happen when the partial sensors of a satellite attitude control system (SACS) go wrong. A fault diagnosis and isolation (FDI) method based on a fault observer is introduced to detect and isolate the fault sensor at first. Based on the FDI result, the object system state-space equation is transformed and divided into a corresponsive triangular canonical form to decouple the normal subsystem from the fault subsystem. And then the KX fault-tolerant observers of the system in different modes are designed and embedded into online monitoring. The outputs of all KX fault-tolerant observers are selected by the control switch process. That can make sense that the SACS is part-observed and in stable when the partial sensors break down. Simulation results demonstrate the effectiveness and superiority of the proposed method.
It is now well known that the time-varying sliding mode control (TVSMC) is characterized by its global robustness against matched model uncertainties and disturbances. The accurate tracking problem of the mechanical system in the presence of the parametric uncertainty and external disturbance is addressed in the TVSMC framework. Firstly, an exponential TVSMC algorithm is designed and the main features are analyzed. Especially, the control parameter is obtained by solving an optimal problem. Subsequently, the global chattering problem in TVSMC is considered. To reduce the static error resulting from the continuous TVSMC algorithm, a disturbance observer based time-varying sliding mode control (DOTVSMC) algorithm is presented. The detailed design principle and the stability of the closed-loop system under the composite controller are provided. Simulation results verify the effectiveness of the proposed algorithm.
Traditional data envelopment analysis (DEA) theory assumes that decision variables are regarded as inputs or outputs, and no variable can play the roles of both an input and an output at the same time. In fact, there exist some variables that work as inputs and outputs simultaneously and are called dual-role variables. Traditional DEA models cannot be used to appraise the performance of decision making units containing dual-role variables. The paper analyzes the structure and properties of the production systems comprising dual-role variables, and proposes a DEA model integrating dual-role variables. Finally the proposed model is illustrated to evaluate the efficiency of university departments.
Due to defects of time-difference of arrival localization, which influences by speed differences of various model waveforms and waveform distortion in transmitting process, a neural network technique is introduced to calculate localization of the acoustic emission source. However, in back propagation (BP) neural network, the BP algorithm is a stochastic gradient algorithm virtually, the network may get into local minimum and the result of network training is dissatisfactory. It is a kind of genetic algorithms with the form of quantum chromosomes, the random observation which simulates the quantum collapse can bring diverse individuals, and the evolutionary operators characterized by a quantum mechanism are introduced to speed up convergence and avoid prematurity. Simulation results show that the modeling of neural network based on quantum genetic algorithm has fast convergent and higher localization accuracy, so it has a good application prospect and is worth researching further more.
Aiming at mitigating end effects of empirical mode decomposition (EMD), a new approach motivated by the nonequidistance grey model (NGM) termed as NGM(1,1) is proposed. Other than trapezoid formulas, the cubic Hermite spline is put forward to improve the accuracy of derivative to the accumulated generating operation (AGO) series. Hopefully, it is worth stressing that the proposed NGM(1,1) model is particularly useful for predicting uncertainty data. Qualitative and quantitative comparisons between the proposed approach and other well-known algorithms are carried out through computer simulations on synthetic as well as natural signals. Simulation results demonstrate the proposed method can reduce end effects and improve the decomposition results of EMD.
The problem of the quantized dynamic output feedback controller design for networked control systems is mainly discussed. By using the quantized information of the system measurement output and the control input, a novel networked control system model is described. This model includes many networkinduced features, such as multi-rate sampled-data, quantized signal, time-varying delay and packet dropout. By constructing suitable Lyapunov-Krasovskii functional, a less conservative stabilization criterion is established in terms of linear matrix inequalities. The quantized control strategy involves the updating values of the quantizer parameters μi(i = 1, 2)(μi take on countable sets of values which dependent on the information of the system measurement outputs and the control inputs). Furthermore, a numerical example is given to illustrate the effectiveness of the proposed method.
A new adaptive neural network (NN) output-feedback stabilization controller is investigated for a class of uncertain stochastic nonlinear strict-feedback systems with discrete and distributed time-varying delays and unknown nonlinear functions in both drift and diffusion terms. First, an extensional stability notion and the related criterion are introduced. Then, a nonlinear observer to estimate the unmeasurable states is designed, and a systematic backstepping procedure to design an adaptive NN output-feedback controller is proposed such that the closed-loop system is stable in probability. The effectiveness of the proposed control scheme is demonstrated via a numerical example.
The existing direction of arrival (DOA) estimation algorithms based on the electromagnetic vector sensors array barely deal with the coexisting of independent and coherent signals. A two-dimensional direction finding method using an L-shape electromagnetic vector sensors array is proposed. According to this method, the DOAs of the independent signals and the coherent signals are estimated separately, so that the array aperture can be exploited sufficiently. Firstly, the DOAs of the independent signals are estimated by the estimation of signal parameters via rotational invariance techniques, and the influence of the coherent signals can be eliminated by utilizing the property of the coherent signals. Then the data covariance matrix containing the information of the coherent signals only is obtained by exploiting the Toeplitz property of the independent signals, and an improved polarimetric angular smoothing technique is proposed to de-correlate the coherent signals. This new method is more practical in actual signal environment than common DOA estimation algorithms and can expand the array aperture. Simulation results are presented to show the estimating performance of the proposed method.
The location of a moving target based on signal fitting and sub-aperture tracking from an airborne multi-channel radar is dealt with. The proposed approach is applied in two steps: first, the ambiguous slant-range velocity is derived with a modified single-snapshot multiple direction of arrival estimation method, and second, the unambiguous slant-range velocity is found using a track-based criterion. The prominent advantage of the proposed approach is that the unambiguous slant-range velocity can be very large. Besides, the first stage is carried out at the determinate range-Doppler test cell by azimuth searching for fitting best to the moving target signal, therefore, the location performance would not be sacrificed in order to suppress clutter and/or interference. The effectiveness and efficiency of the proposed method are validated with a set of airborne experimental data.
To correct the range walk through resolution cell in Doppler beam sharpening (DBS) imaging, a new DBS imaging algorithm based on Keystone transform is proposed. Without the exact values of the movement parameters and the look angle of the radar platform in the multi-targets environment, a linear transform on the received data is employed to correct different range walk values accurately under the condition of Doppler frequency ambiguity in this algorithm. This method can realize the coherent integration in azimuth dimension and improve the azimuth resolution. In order to reduce the computational burden, a fast implementation of Keystone transform is used. Theoretical analysis and simulation results demonstrate the effectiveness of the new algorithm. And through comparing the computational load of the fast implementation with several other algorithms, the real-time processing ability of the proposed algorithm is superior to that of other algorithms.
Reduction of conservatism is one of the key and difficult problems in missile robust gain scheduling autopilot design based on multipliers. This article presents a scheme of adopting linear parameter-varying (LPV) control approach with full block multipliers to design a missile robust gain scheduling autopilot in order to eliminate conservatism. A model matching design structure with a high demand on matching precision is constructed based on the missile linear fractional transformation (LFT) model. By applying full block S-procedure and elimination lemma, a convex feasibility problem with an infinite number of constraints is formulated to satisfy robust quadratic performance specifications. Then a grid method is adopted to transform the infinite-dimensional convex feasibility problem into a solvable finite-dimensional convex feasibility problem, based on which a gain scheduling controller with linear fractional dependence on the flight Mach number and altitude is derived. Static and dynamic simulation results show the effectiveness and feasibility of the proposed scheme.
Multi-disciplinary virtual prototypes of complex products are increasingly and widely used in modern advanced manufacturing. How to effectively address the problems of unified modeling, composition and reuse based on the multi-disciplinary heterogeneous models has brought great challenges to the modeling and simulation (M&S) science and technology. This paper presents a top-level modeling theory based on the meta modeling framework (M2F) of the COllaborative SIMulation (COSIM) theory of virtual prototyping to solve the problems. Firstly the fundamental principles of the top-level modeling theory are decribed to expound the premise, assumptions, basic conventions and special requirements in the description of complex heterogeneous systems. Next the formalized definitions for each factor in top level modeling are proposed and the hierarchical nature of them is illustrated. After demonstrating that they are self-closing, this paper divides the toplevel modeling into two views, static structural graph and dynamic behavioral graph. Finally, a case study is discussed to demonstrate the feasibility of the theory.
To avoid uneven energy consuming in wireless sensor networks, a clustering routing model is proposed based on a Bayesian game. In the model, Harsanyi transformation is introduced to convert a static game of incomplete information to the static game of complete but imperfect information. In addition, the existence of Bayesian nash equilibrium is proved. A clustering routing algorithm is also designed according to the proposed model, both cluster head distribution and residual energy are considered in the design of the algorithm. Simulation results show that the algorithm can balance network load, save energy and prolong network lifetime effectively.
This paper considers the uniform parallel machine scheduling problem with unequal release dates and delivery times to minimize the maximum completion time. For this NP-hard problem, the largest sum of release date, processing time and delivery time first rule is designed to determine a certain machine for each job, and the largest difference between delivery time and release date first rule is designed to sequence the jobs scheduled on the same machine, and then a novel algorithm for the scheduling problem is built. To evaluate the performance of the proposed algorithm, a lower bound for the problem is proposed. The accuracy of the proposed algorithm is tested based on the data with problem size varying from 200 jobs to 600 jobs. The computational results indicate that the average relative error between the proposed algorithm and the lower bound is only 0.667%, therefore the solutions obtained by the proposed algorithm are very accurate.
Orthogonal netted radar systems (ONRS) can fundamentally improve the radar performance by using a group of specially designed orthogonal polyphase code signals which require a very low aperiodic autocorrelation peak sidelobe level, low aperiodic cross-correlation, and a good resilience to small Doppler shifts. However, the existing numerical solutions degrade severely in the presence of small Doppler shifts. A new set of polyphase sequences is presented with good correlation properties as well as resilience to Doppler shifts. These sequences are built by using numerical optimization based on correlation properties as well as the Doppler effects on matched filter outputs, which maintains the Doppler tolerance. The statistical simulated annealing algorithm and the greedy code search method are used to optimize the sequences. Correlation and Doppler results are compared with the best-known sequences and show to be superior.
This paper proposes a scheme to construct timefrequency codes based on protograph low density parity check (LDPC) codes in orthogonal frequency division multiplexing (OFDM) communication systems. This approach synthesizes two techniques: protograph LDPC codes and OFDM. One symbol of encoded information by protograph LDPC codes corresponds to one sub-carrier, namely the length of encoded information equals to the number of sub-carriers. The design of good protograph LDPC codes with short lengths is given, and the proposed protograph LDPC codes can be of fast encoding, which can reduce the encoding complexity and simplify encoder hardware implementation. The proposed approach provides a higher coding gain in the Rayleigh fading channel. The simulation results in the Rayleigh fading channel show that the bit error rate (BER) performance of the proposed time-frequency codes is as good as random LDPCOFDM codes and is better than Tanner LDPC-OFDM codes under the condition of different fading coefficients.
Technology management is recognized as a key for organizations to achieve competitiveness. How to promote an organization’s technology management capability is of great significance in creating efficiencies and achieving a competitive edge. The knowledge essence of technology management capability is introduced and then the correlation between knowledge diffusion and the development of technology management capability is discussed. Further, the basic and extended dynamic models of the development of technology management capability are constructed, and is applied into an enterprise. The results show that the dynamic models can well explain how the knowledge improves the development of technology management capability, and they can be used as an useful tool by an enterprise to promote technology management capability. Finally, the managerial implications of the models are discussed.
The novel compensating method directly demodulates the signals without the carrier recovery processes, in which the carrier with original modulation frequency is used as the local coherent carrier. In this way, the phase offsets due to frequency shift are linear. Based on this premise, the compensation processes are: firstly, the phase offsets between the base band neighbor-symbols after clock recovery are unbiasedly estimated among the reference symbols; then, the receiving signals symbols are adjusted by the phase estimation value; finally, the phase offsets after adjusting are compensated by the least mean squares (LMS) algorithm. In order to express the compensation processes and ability clearly, the quadrature phase shift keying (QPSK) modulation signals are regarded as examples for Matlab simulation. BER simulations are carried out using the Monte-Carlo method. The learning curves are obtained to study the algorithm’s convergence ability. The constellation figures are also simulated to observe the compensation results directly.
A collaborative optimization model for maintenance and spare ordering of a single-unit degrading system is proposed in this paper based on the continuous detection. A gamma distribution is used to model the material degradation. The degrading decrement after the imperfect maintenance action is assumed as a random variable normal distribution. This model aims to obtain the optimal maintenance policy and spare ordering point with the expected cost rate within system lifecycle as the optimization objective. The rationality and feasibility of the model are proved through a numerical example.
