A multiple targets detection method based on spatial smoothing (MTDSS) is proposed to solve the problem of the source number estimation under the colored noise background. The forward and backward smoothing based on auxiliary vectors which are received data on some specific elements is computed. By the spatial smoothing with auxiliary vectors, the correlated signals are decorrelated, and the colored noise is partially alleviated. The correlation matrix formed from the cross correlations between subarray data and auxiliary vectors is computed. By exploring the second-order statistics property of the covariance matrix, a threshold based on Gerschgorin radii of the smoothing correlation matrix is set to estimate the number of sources. Simulations and experimental results validate that MTDSS has an effective performance under the condition of the colored noise background and coherent sources, and MTDSS is robust with the correlated factor of signals and noise.
Concurrent multipath transfer (CMT) using stream control transmission protocol (SCTP) multihoming has become an appealing option to increase the throughput and improve the performance of increasingly bandwidth-hungry applications. To investigate the rate allocation for applications in CMT, this paper analyzes the capacities of paths shared by competing sources, then proposes the rate allocation model for elastic flows based on the framework of network utility maximization (NUM). In order to obtain the global optimum of the model, a distributed algorithm is presented which depends only on local available information. Simulation results confirm that the proposed algorithm can achieve the global optimum within reasonable convergence times.
The mixture of factor analyzers(MFA)can accurately describe high resolution range profile(HRRP)statistical charac- teristics.But how to determine the proper number of the models is a problem.This paper develops a variational Bayesian mixture of factor analyzers(VBMFA)model.This procedure can obtain a lower bound on the Bayesian integral using the Jensen’s inequality. An analytical solution of the Bayesian integral could be obtained by a hypothesis that latent variables in the model are indepen- dent.During computing the parameters of the model,birth-death moves are utilized to determine the optimal number of model au- tomatically.Experimental results for measured data show that the VBMFA method has better recognition performance than FA and MFA method.
The symbol-error-rate (SER) and power allocation for hybrid cooperative (HC) transmission system are investigated. Closed-form SER expression is derived by using the moment generating function (MGF)-based approach. However, the resultant SER contains an MGF of the harmonic mean of two independent random variables (RVs), which is not tractable in SER analysis. We present a simple MGF expression of the harmonic mean of two independent RVs which avoids the hypergeometric functions used commonly in previous studies. Using the simple MGF, closed-form SER for HC system with M-ary phase shift keying (M-PSK) signals is provided. Further, an approximation as well as an upper bound of the SER is presented. It is shown that the SER approximation is asymptotically tight. Based on the tight SER approximation, the power allocation of the HC system is investigated. It is shown that the optimal power allocation does not depend on the fading parameters of the source-destination (SD) channel and it only depends on the source-relay (SR) and relay-destination (RD) channels. Moreover, the performance gain of the power allocation depends on the ratio of the channel quality between RD and SR. With the increase of this ratio, more performance gain can be acquired.
An adaptive neural network output-feedback regulation approach is proposed for a class of multi-input-multi-output nonlinear time-varying delayed systems. Both the designed observer and controller are free from time delays. Different from the existing results, this paper need not the assumption that the upper bounding functions of time-delay terms are known, and only a neural network is employed to compensate for all the upper bounding functions of time-delay terms, so the designed controller procedure is more simplified. In addition, the resulting closed-loop system is proved to be semi-globally ultimately uniformly bounded, and the output regulation error converges to a small residual set around the origin. Two simulation examples are provided to verify the effectiveness of control scheme.
The GPS multipath signal model is presented, which indicates that the coherent DLL outputs in multipath environment are the convolution between the ideal DLL outputs and the channel responses. So the channel responses can be estimated by a least square method using the observed curve of the DLL discriminator. In terms of the estimated multipath channels, two multipath mitigation methods are discussed, which are equalization filtering and multipath subtracting, respectively. It is shown, by computer simulation, that the least square method has a good performance in channels estimation and the multipath errors can be mitigated almost completely by either of the methods. However, the multipath subtracting method has relative small remnant errors than equalization filtering.
To preserve the sharp features and details of the synthetic aperture radar (SAR) image effectively when despeckling, a despeckling algorithm with edge detection in nonsubsampled second generation bandelet transform (NSBT) domain is proposed. First, the Canny operator is utilized to detect and remove edges from the SAR image. Then the NSBT which has an optimal approximation to the edges of images and a hard thresholding rule are used to approximate the details while despeckling the edge-removed image. Finally, the removed edges are added to the reconstructed image. As the edges are detected and protected, and the NSBT is used, the proposed algorithm reaches the state-of-the-art effect which realizes both despeckling and preserving edges and details simultaneously. Experimental results show that both the subjective visual effect and the mainly objective performance indexes of the proposed algorithm outperform that of both Bayesian wavelet shrinkage with edge detection and Bayesian least square-Gaussian scale mixture (BLS-GSM).
This paper proposes a particle swarm optimization (PSO) based particle filter (PF) tracking framework, the embedded PSO makes particles move toward the high likelihood area to find the optimal position in the state transition stage, and simultaneously incorporates the newest observations into the proposal distribution in the update stage. In the proposed approach, likelihood measure functions involving multiple features are presented to enhance the performance of model fitting. Furthermore, the multi-feature weights are self-adaptively adjusted by a PSO algorithm throughout the tracking process. There are three main contributions. Firstly, the PSO algorithm is fused into the PF framework, which can efficiently alleviate the particles degeneracy phenomenon. Secondly, an effective convergence criterion for the PSO algorithm is explored, which can avoid particles getting stuck in local minima and maintain a greater particle diversity. Finally, a multi-feature weight self-adjusting strategy is proposed, which can significantly improve the tracking robustness and accuracy. Experiments performed on several challenging public video sequences demonstrate that the proposed tracking approach achieves a considerable performance.
A new procedure for a design of multi-range controllers for use with highly nonlinear systems is developed. The procedure involves obtaining the describing function models of the nonlinear plant by software followed by designing a controller at nominal conditions. Then, the controller parameters are optimized to yield a satisfactory closed-loop response at all operating regimes. Finally, the performance and stability of the closed-loop system comprised of the designed controller and the nonlinear plant are verified. The procedure and the associated software are applied to a nonlinear control problem of the sort encountered in aerospace, and the results are compared with two other approaches.
To design approximately linear-phase complex coefficient finite impulse response (FIR) digital filters with arbitrary magnitude and group delay responses, a novel neural network approach is studied. The approach is based on a batch back-propagation neural network algorithm by directly minimizing the real magnitude error and phase error from the linear-phase to obtain the filter’s coefficients. The approach can deal with both the real and complex coefficient FIR digital filters design problems. The main advantage of the proposed design method is the significant reduction in the group delay error. The effectiveness of the proposed method is illustrated with two optimal design examples.
The problem on stabilization for the system with distributed delays is researched. The distributed time-delay under consideration is assumed to be a constant time-delay, but not known exactly. A design method is proposed for a memory proportional and integral (PI) feedback controller with adaptation to distributed time-delay. The feedback controller with memory simultaneously contains the current state and the past distributed information of the addressed systems. The design for adaptation law to distributed delay is very concise. The controller can be derived by solving a set of linear matrix inequalities (LMIs). Two numerical examples are given to illustrate the effectiveness of the design method.
Target tracking is one of the applications of wireless sensor networks (WSNs). It is assumed that each sensor has a limited range for detecting the presence of the object, and the network is sufficiently dense so that the sensors can cover the area of interest. Due to the limited battery resources of sensors, there is a tradeoff between the energy consumption and tracking accuracy. To solve this problem, this paper proposes an energy efficient tracking algorithm. Based on the cooperation of dispatchers, sensors in the area are scheduled to switch their working mode to track the target. Since energy consumed in active mode is higher than that in monitoring or sleeping mode, for each sampling interval, a minimum set of sensors is woken up based on the select mechanism. Meanwhile, other sensors keep in sleeping mode. Performance analysis and simulation results show that the proposed algorithm provides a better performance than other existing approaches.
According to the signal processing characteristic of MIMO radars, an adaptive dwell scheduling algorithm is proposed. It is based on a novel pulse interleaving technique, which makes full use of transmitting, waiting and receiving durations of radar dwells. The utilization of transmitting duration is unique for MIMO radars and is realized through transmitting duration overlapping. Simulation results show that, compared with the conventional scheduling algorithm, the scheduling performance of MIMO radars can be improved effectively by the proposed algorithm, and the scheduling rule can be chosen arbitrarily when using the proposed algorithm.
Event region detection is the important application for wireless sensor networks (WSNs), where the existing faulty sensors would lead to drastic deterioration of network quality of service. Considering single-moment nodes fault-tolerance, a novel distributed fault-tolerant detection algorithm named distributed fault-tolerance based on weighted distance (DFWD) is proposed, which exploits the spatial correlation among sensor nodes and their redundant information. In sensor networks, neighborhood sensor nodes will be endowed with different relative weights respectively according to the distances between them and the central node. Having syncretized the weighted information of dual-neighborhood nodes appropriately, it is reasonable to decide the ultimate status of the central sensor node. Simultaneously, readings of faulty sensors would be corrected during this process. Simulation results demonstrate that the DFWD has a higher fault detection accuracy compared with other algorithms, and when the sensor fault probability is 10%, the DFWD can still correct more than 91% faulty sensor nodes, which significantly improves the performance of the whole sensor network.
The receding horizon control (RHC) problem is considered for nonlinear Markov jump systems which can be represented by Takagi-Sugeno fuzzy models subject to constraints both on control inputs and on observe outputs. In the given receding horizon, for each mode sequence of the T-S modeled nonlinear system with Markov jump parameter, the cost function is optimized by constraints on state trajectories, so that the optimization control input sequences are obtained in order to make the state into a terminal invariant set. Out of the receding horizon, the stability is guaranteed by searching a state feedback control law. Based on such stability analysis, a linear matrix inequality approach for designing receding horizon predictive controller for nonlinear systems subject to constraints both on the inputs and on the outputs is developed. The simulation shows the validity of this method.
The Global Position System (GPS) is a reliable method for positioning in most scenarios, but it falls short in harsh environments like urban vehicular scenarios, where numerous trees or flyovers obstruct the signals. This presents an unprecedented challenge for autonomous vehicles or applications requiring high accuracy. Fortunately, vehicular ad-hoc networks (VANET) offer an effective solution, where vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications are used to enhance location awareness. In V2I communications, the roadside units (RSU) transmit beacon packets, and the vehicle receives numerous packets from different RSUs to establish communication. To further improve localization accuracy, a cross-covariance matrices-alternating least square (CCM-ALS) algorithm is proposed. The algorithm relies on ALS of the CCM for obtaining the position of vehicles in V2I communications. The algorithm is highly precise compared to traditional angle of arrival (AOA) positioning and not inferior to direct position determination (DPD) approaches while being low in complexity, which is crucial for moving vehicles. The numerical results verify the superiority of the proposed method.
Ubiquitous radar is a new radar system that provides continuous and uninterrupted multifunction capability within a coverage volume. Continuous coverage from close-in “pop-up” targets in clutter to long-range targets impacts selection of waveform parameters. The coherent processing interval (CPI) must be long enough to achieve a certain signal-to-noise ratio (SNR) that ensures the efficiency of detection. The condition of detection in the case of low SNR is analyzed, and three different cases that would occur during integration are discussed and a method to determine the CPI is presented. The simulation results show that targets detection with SNR as low as −26 dB in the experimental system can possibly determine the CPI.
To solve the multi-class fault diagnosis tasks, decision tree support vector machine (DTSVM), which combines SVM and decision tree using the concept of dichotomy, is proposed. Since the classification performance of DTSVM highly depends on its structure, to cluster the multi-classes with maximum distance between the clustering centers of the two sub-classes, genetic algorithm is introduced into the formation of decision tree, so that the most separable classes would be separated at each node of decisions tree. Numerical simulations conducted on three datasets compared with “one-against-all” and “one-against-one” demonstrate the proposed method has better performance and higher generalization ability than the two conventional methods.
Frequency-invariant beamformer (FIB) design is a key issue in wideband array signal processing. To use commonly wideband linear array with tapped delay line (TDL) structure and complex weights, the FIB design is provided according to the rule of minimizing the sidelobe level of the beampattern at the reference frequency while keeping the distortionless response constraint in the mainlobe direction at the reference frequency, the norm constraint of the weight vector and the amplitude constraint of the averaged spatial response variation (SRV). This kind of beamformer design problem can be solved with the interior-point method after being converted to the form of standard second order cone programming (SOCP). The computer simulations are presented which illustrate the effectiveness of our FIB design method for the wideband linear array with TDL structure and complex weights.
Learning is widely used in intelligent planning to shorten the planning process or improve the plan quality. This paper aims at introducing learning and fatigue into the classical hierarchical task network (HTN) planning process so as to create better highquality plans quickly. The process of HTN planning is mapped during a depth-first search process in a problem-solving agent, and the models of learning in HTN planning is conducted similar to the learning depth-first search (LDFS). Based on the models, a learning method integrating HTN planning and LDFS is presented, and a fatigue mechanism is introduced to balance exploration and exploitation in learning. Finally, experiments in two classical domains are carried out in order to validate the effectiveness of the proposed learning and fatigue inspired method.
This paper develops a robust control methodology for a class of morphing aircraft, which is called innovative control effector (ICE) aircraft. For the ICE morphing aircraft, the distributed arrays of hundreds of shape-change devices are employed to stabilize and maneuver the air vehicle. Because the morphing aircraft have the inherent uncertainty and varying dynamics due to the alteration of their configuration, a desired control performance can not be satisfied with a fixed feedback controller. Therefore, a novel control framework including an adaptive flight control law and an adaptive allocation algorithm is proposed. Firstly, a state feedback adaptive control law is designed to guarantee closed-loop stability and state tracking in the presence of uncertain dynamics caused by the wing shape change due to different flight missions. In the control allocation, many distributed arrays are managed in an optimal way to improve the robustness of the system. The scheme is used to an uncertain morphing aircraft model, and the simulation results demonstrate their performance.
The capture probability of interceptors has been deeply studied. Firstly, the definition of capture probability is analyzed. It is transformed into calculating the probability that the relative position vector between the target and the interceptor locates in a certain cone. The relative position vector and associated covariance matrix are projected in line-of-sight coordinates, and the 3-dimensional integral of a probability function in a cone is calculated to obtain the capture probability. The integral equation is a complicated expression of probability, and it is simplified to an explicit approximate expression according to some assumptions based on the characteristics of the engineering problems. The approximation precision is analyzed by comparative simulation difference, which indicates that approximate assumptions are reasonable. Utilizing the explicit xpression, the characteristics of capture probability are analyzed respectively with the factors, such as the distance between the interceptor and the target, the precision of relative position vector, the maximum capture distance and the maximum field angle of interceptor seeker.
The tracking and stable control of a typical shipmounted mobile satellite communication system (MSCS) is studied. Unlike the former studies based on simplified single-axis models, a tri-axis nonlinear model including the kinematic and dynamic features of the MSCS is used as the control object. An adaptive robust controller with trajectory planning is designed to deal with large parametric uncertainties and uncertain nonlinearities of the system. A theoretic performance result is given and proved. The designed adaptive robust controller and other two traditional controllers are tested in the comparative simulations under three different situations. The simulation results show the tracking and stable validity of the proposed controller.
The class of multiple attribute decision making (MADM) problems is studied, where the attribute values are intuitionistic fuzzy numbers, and the information about attribute weights is completely unknown. A score function is first used to calculate the score of each attribute value and a score matrix is constructed, and then it is transformed into a normalized score matrix. Based on the normalized score matrix, an entropy-based procedure is proposed to derive attribute weights. Furthermore, the additive weighted averaging operator is utilized to fuse all the normalized scores into the overall scores of alternatives, by which the ranking of all the given alternatives is obtained. This paper is concluded by extending the above results to interval-valued intuitionistic fuzzy set theory, and an illustrative example is also provided.
The high resolution radar target detection is addressed in the non-Gaussian clutter. An adaptive detector is derived for range-spread target based on a novel covariance matrix estimator. It is proved that the new detector is constant false alarm rate (CFAR) to both of the clutter covariance matrix structure and power level theoretically for match cases. The simulation results show that the new detector is almost CFAR for mismatch cases, and it outperforms the existing adaptive detector based on the sample covariance matrix. It also shows that the detection performance improves, as the number of pulses, the number of secondary data or the clutter spike increases. In addition, the derived detector is robust to different subsets, estimated clutter group sizes and correlations of clutter. Importantly, the number of iterations for practical application is just one.
According to the characteristic and the requirement of multipath planning, a new multipath planning method is proposed based on network. This method includes two steps: the construction of network and multipath searching. The construction of network proceeds in three phases: the skeleton extraction of the configuration space, the judgment of the cross points in the skeleton and how to link the cross points to form a network. Multipath searching makes use of the network and iterative penalty method (IPM) to plan multi-paths, and adjusts the planar paths to satisfy the requirement of maneuverability of unmanned aerial vehicle (UAV). In addition, a new height planning method is proposed to deal with the height planning of 3D route. The proposed algorithm can find multiple paths automatically according to distribution of terrain and threat areas with high efficiency. The height planning can make 3D route following the terrain. The simulation experiment illustrates the feasibility of the proposed method.
A proper weapon system is very important for a national defense system. Generally, it means selecting the optimal weapon system among many alternatives, which is a multipleattribute decision making (MADM) problem. This paper proposes a new mathematical model based on the response surface method (RSM) and the grey relational analysis (GRA). RSM is used to obtain the experimental points and analyze the factors that have a significant impact on the selection results. GRA is used to analyze the trend relationship between alternatives and reference series. And then an RSM model is obtained, which can be used to calculate all alternatives and obtain ranking results. A real world application is introduced to illustrate the utilization of the model for the weapon selection problem. The results show that this model can be used to help decision-makers to make a quick comparison of alternatives and select a proper weapon system from multiple alternatives, which is an effective and adaptable method for solving the weapon system selection problem.
The paper proposes a decentralized concurrent transmission strategy in shared channels based on an incomplete information game in Ad Hoc networks. Based on the nodal channel quality, the game can work out a channel gain threshold, which decides the candidates for taking part in the concurrent transmission. The utility formula is made for maximizing the overall throughput based on channel quality variation. For an achievable Bayesian Nash equilibrium (BNE) solution, this paper further prices the selfish players in utility functions for attempting to improve the channel gain one-sidedly. Accordingly, this game allows each node to distributedly decide whether to transmit concurrently with others depending on the Nash equilibrium (NE). Besides, to make the proposed game practical, this paper next presents an efficient particle swarm optimization (PSO) model to fasten the otherwise very slow convergence procedure due to the large computational complexity. Numerical results show the proposed approach is feasible to increase concurrent transmission opportunities for active nodes and the convergence can be swiftly obtained with a few of iteration times by the proposed PSO algorithm.
Orthogonal waveform design is quite an important issue for waveform diversity systems. A chaos based method for the orthogonal discrete frequency coding waveform (DFCW) design is proposed to increase the insufficient orthogonal waveform number and their finite coding length. Premises for chaos choosing and the frequency quantification method are discussed to obtain the best correlation properties. Simulation results show the validity of the theoretic analysis.
A novel group decision-making (GDM) method based on intuitionistic fuzzy sets (IFSs) is developed to evaluate the ergonomics of aircraft cockpit display and control system (ACDCS). The GDM process with four steps is discussed. Firstly, approaches are proposed to transform four types of common judgement representations into a unified expression by the form of the IFS, and the features of unifications are analyzed. Then, the aggregation operator called the IFSs weighted averaging (IFSWA) operator is taken to synthesize decision-makers’ (DMs’) preferences by the form of the IFS. In this operator, the DM’s reliability weights factors are determined based on the distance measure between their preferences. Finally, an improved score function is used to rank alternatives and to get the best one. An illustrative example proves the proposed method is effective to valuate the ergonomics of the ACDCS.
A new code concept is used for the L1 civil (L1C) signal of the global positioning system (GPS). The generation of L1C codes is quite different from the generation of traditional ranging codes. Thus, it is necessary to find a method for the correct generation to pave the way for future research. L1C codes are based on only one Legendre sequence which consists of Legendre symbols. To calculate these Legendre symbols, the Euler criterion is always used to evaluate quadratic residues. However, due to the great length of L1C codes, this procedure causes overflow problems. Therefore, the quadratic reciprocity law, some related theorems and properties are introduced to solve the problems. Moreover, if the quadratic reciprocity law, some related theorems and properties are used to calculate different Legendre symbols, the combination modes may vary, which causes a complex generation process. The proposed generation method deals with this complex generation process effectively. In addition, through simulations, it is found that the autocorrelation features of obtained Legendre sequences and L1C codes are in accordance with theoretical results, which proves the correctness of the proposed method.
The problem of detecting signal with multiple input multiple output (MIMO) radar in correlated Gaussian clutter dominated scenario with unknown covariance matrix is dealt with. The general MIMO model, with widely separated sub-arrays and co-located antennas at each sub-array, is adopted. Firstly, the generalized likelihood ratio test (GLRT) with known covariance matrix is obtained, and then the Rao and Wald detectors are devised, which have proved that the Rao and Wald test coincide with GLRT detector.To make the detectors fully adaptive, the secondary data with signal-free will be collected to estimate the covariance. The performance of the proposed detector is analyzed, however, it is just ancillary. A thorough performance assessment by several numerical examples is also given, which has considered the sense with co-located antennas configure of transmitters and receivers array.The results show that the performance the proposed adaptive detector is better than LJ-GLRT, and the loss can be acceptable in comparison to their non-adaptive counterparts.
Intuitionistic trapezoidal fuzzy numbers and their operational laws are defined. Based on these operational laws, some aggregation operators, including intuitionistic trapezoidal fuzzy weighted arithmetic averaging operator and weighted geometric averaging operator are proposed. Expected values, score function, and accuracy function of intuitionitsic trapezoidal fuzzy numbers are defined. Based on these, a kind of intuitionistic trapezoidal fuzzy multi-criteria decision making method is proposed. By using these aggregation operators, criteria values are aggregated and integrated intuitionistic trapezoidal fuzzy numbers of alternatives are attained. By comparing score function and accuracy function values of integrated fuzzy numbers, a ranking of the whole alternative set can be attained. An example is given to show the feasibility and availability of the method.
