A modified unscented particle filtering scheme for nonlinear tracking is proposed, in view of the potential drawbacks (such as, particle impoverishment and numerical sensitivity in calculating the prior) of the conventional unscented particle filter (UPF) confronted in practice. Specifically, a different derivation of the importance weight is presented in detail. The proposed method can avoid the calculation of the prior and reduce the effects of the impoverishment problem caused by sampling from the proposal distribution. Simulations have been performed using two illustrative examples and results have been provided to demonstrate the validity of the modified UPF as well as its improved performance over the conventional one.
The code tracking loop is a key component for user positioning. The pseudorange information of BeiDou B1 signals has been fused and changed for vector tracking, so a correlation output model for complex scenarios is designed to prevent the propagation of error and valuate the signal performance. The relevant software and hardware factors that affect the output are analyzed. A single channel time-division multiplexing (TDM) method for multicorrelation data extraction is proposed. Statistical characteristics of the correlation output data for both vector and scalar structures are evaluated. Simulation results show that correlation outputs for both structures follow normal or Chi-squared distributions in normal conditions, and the Gamma distribution in harsh conditions. It is shown that a tracking model based on the multi-channel fusion hardly changes the probability distribution of the correlation output in the normal case, but it reduces the ranging error of the code loop, and hence the tracking ability of the code loop for weak signals is improved. Furthermore, vector tracking changes the pseudorange characteristics of channels anytime, and affects the mutual correlation outputs of the code loops in the abnormal case. This study provides a basis for the subsequent design of autonomous integrity algorithms for vector tracking.
The network reliability is difficult to be evaluated because of the complex relationship among the network components. It can be quite different for different users running different applications on the same network. This paper proposes a new concept and a model of application reliability. Different from the existing models that ignores the effects of applications, the proposed application reliability model considers the effects of different applications on the network performance and different types of network faults and makes the analysis of network components relationship possible. This paper also provides a method to evaluate the application reliability when the data flow satisfies Markov properties. Finally, a case study is presented to illustrate the proposed network reliability model and the analysis method.
To study multi-radio multi-channel (MR-MC) Ad Hoc networks based on 802.11, an efficient cross-layer routing protocol with the function of joint channel assignment, called joint channel assignment and cross-layer routing (JCACR), is presented. Firstly, this paper introduces a new concept called channel utilization percentage (CUP), which is for measuring the contention level of different channels in a node’s neighborhood, and deduces its optimal value for determining whether a channel is overloaded or not. Then, a metric parameter named channel selection metric (CSM) is designed, which actually reflects not only the channel status but also corresponding node’s capacity to seize it. JCACR evaluates channel assignment by CSM, performs a local optimization by assigning each node a channel with the smaller CSM value, and changes the working channel dynamically when the channel is overloaded. Therefore, the network load balancing can be achieved. In addition, simulation shows that, when compared with the protocol of weighted cumulative expected transfer time (WCETT), the new protocol can improve the network throughput and reduce the end-to-end average delay with fewer overheads.
A novel gain-scheduled switching control method for the longitudinal motion of a flexible air-breathing hypersonic vehicle (FAHV) is proposed. Firstly, velocity and altitude are selected as scheduling variables, a polytopic linear parameter varying (LPV) model is developed to represent the complex nonlinear longitudinal dynamics of the FAHV. Secondly, based on the obtained polytopic LPV model, the flight envelope is divided into four smaller subregions, and four gain-scheduled controllers are designed for these parameter subregions. Then, by the defined switching characteristic function, these gain-scheduled controllers are switched in order to guarantee the closed-loop FAHV system to be asymptotically stable and satisfy a given tracking error performance criterion. The condition of gain-scheduled switching controller synthesis is given in terms of linear matrix inequalities (LMIs) which can be easily solved by using standard software packages. Finally, simulation results show the effectiveness of the presented method.
This paper proposes an application of compressive imaging systems to the problem of wide-area video surveillance systems. A parallel coded aperture compressive imaging system and a corresponding motion target detection algorithm in video using compressive image data are developed. Coded masks with random Gaussian, Toeplitz and random binary are utilized to simulate the compressive image respectively. For compressive images, a mixture of the Gaussian distribution is applied to the compressed image field to model the background. A simple threshold test in compressive sampling image is used to declare motion objects. Foreground image retrieval from underdetermined measurement using the total variance optimization algorithm is explored. The signal-to-noise ratio (SNR) is employed to evaluate the image quality recovered from the compressive sampling signals, and receiver operation characteristic (ROC) curves are used to quantify the performance of the motion detection algorithm. Experimental results demonstrate that the low dimensional compressed imaging representation is sufficient to determine spatial motion targets. Compared with the random Gaussian and Toeplitz mask, motion detection algorithms using the random binary phase mask can yield better detection results. However using the random Gaussian and Toeplitz phase mask can achieve high resolution reconstructed images.
Intercepted signal blind separation is a research topic with high importance for both military and civilian communication systems. A blind separation method for space-time block code (STBC) systems is proposed by using the ordinary independent component analysis (ICA). This method cannot work when specific complex modulations are employed since the assumption of mutual independence cannot be satisfied. The analysis shows that source signals, which are group-wise independent and use multi-dimensional ICA (MICA) instead of ordinary ICA, can be applied in this case. Utilizing the block-diagonal structure of the cumulant matrices, the JADE algorithm is generalized to the multidimensional case to separate the received data into mutually independent groups. Compared with ordinary ICA algorithms, the proposed method does not introduce additional ambiguities. Simulations show that the proposed method overcomes the drawback and achieves a better performance without utilizing coding information than channel estimation based algorithms.
The elements of network profile are proposed. Based on the network traffic distribution model, the network profile includes the application request rate, the branch transfer probability, the ratio of application requests, and the probability distribution of the requested objects. Based on the evaluation method of network performance reliability, four simulation cases are constructed in OPNET software, and the results show the four elements of profile have impacts on the network reliability.
For the improvement of accuracy and better faulttolerant performance, a global position system (GPS)/vision navigation (VISNAV) integrated relative navigation and attitude determination approach is presented for ultra-close spacecraft formation flying. Onboard GPS and VISNAV system are adopted and a federal Kalman filter architecture is used for the total navigation system design. Simulation results indicate that the integrated system can provide a total improvement of relative navigation and attitude estimation performance in accuracy and fault-tolerance.
A robust digital receiver based on a matched filter (MF) is proposed for the radio frequency identification (RFID) reader system to enhance the reliability of signal processing in the electronic product code (EPC) sensor network (ESN). The performance of the proposed receiver is investigated by examining the anti-collision algorithm in the EPC global Class1 Generation2 protocol. The validity and usefulness are demonstrated by both computer simulations and experiments. Based on the verification results, comparing with the conventional zero crossing detector (ZCD) based receiver, the proposed receiver is very robust against strong amplitude distortions and considerable frequency deviations happening on the backscattered signal from a passive tag.
Task-oriented networked information system is an integrated information system which builds on multi-satellite networking to accomplish one or more tasks. In the background of emergency relief for applications, system working flow and response process are analyzed, and a timeliness effectiveness evaluation index system is constructed at multi-task level. The effectiveness is a measurement of promptness of information return. In evaluation process, system performance and tasks are associated, then an evaluation model based on efficacy function is established, and different evaluation criteria are selected for different tasks. A distributed simulation system is constructed, and the execution of task is decomposed. The simulation platform provides a comprehensive data source for evaluation. The results are easy to compare with each other, which reflects system time efficiency in different satellites networks and provides actual systems with basis and reference for design and application.
An improved block matching approach to fast disparity estimation in machine vision applications is proposed, where the matching criterion is the sum of the absolute difference (SAD). By evaluating the lower bounds, which become increasingly tighter for the matching criteria, the method tries to successively terminate unnecessary computations of the matching criteria between the reference block in one image and the ineligible candidate blocks in another image. It also eliminates the ineligible blocks as early as possible, while ensuring the optimal disparity of each pixel. Also, the proposed method can further speed up the elimination of ineligible candidate blocks by efficiently using the continuous constraint of disparity to predict the initial disparity of each pixel. The performance of the new algorithm is evaluated by carrying out a theoretical analysis, and by comparing its performance with the disparity estimation method based on the standard block matching. Simulated results demonstrate that the proposed algorithm achieves a computational cost reduction of over 50.5% in comparision with the standard block matching method.
A new troubleshooting algorithm for solving assignment problem based on existing algorithms is proposed, and an analysis on the related theory is given. By applying the new troubleshooting algorithm to the Lagrange relaxation algorithm of the multi-dimensional assignment problem of data association for multi-passive-sensor multi-target location systems, and comparing the simulation results with that of the Hungarian algorithm which is the classical optimal solving algorithm, and the multi-layer ordersearching algorithm which is a sub-optimal solving algorithm, the performance and applying conditions of the new algorithm are summarized. Theory analysis and simulation results prove the effectiveness and superiority of the new algorithm.
A modified pseudo-noise (PN) code regeneration method is proposed to improve the clock tracking accuracy without impairing the code acquisition time performance. Thus, the method can meet the requirement of high accuracy ranging measurements in short time periods demanded by radio-science missions. The tracking error variance is derived by linear analysis. For some existing PN codes, which can be acquired rapidly, the tracking error variance performance of the proposed method is about 2.6 dB better than that of the JPL scheme (originally proposed by Jet Propulsion Laboratory), and about 1.5 dB better than that of the traditional double loop scheme.
To improve the error performance and the resource utilization of cooperative systems, the optimum resource allocation, i.e., power allocation and partner choice, for an adaptive decode-and-forward (DF) cooperative diversity system based on quadrature modulation is investigated. The closed-form expression of the bit error rate (BER) system performance is derived and an optimal power allocation (OPA) algorithm is proposed to optimize the power allocation between the local and relayed signals under the minimum BER criterion. Based on the OPA algorithm, a partner choice strategy is proposed to determine the partner locations specified by various cooperation gains. Simulation results show that the proposed resource optimization algorithms are superior to the unoptimized algorithms by significantly reducing the BER and improving the cooperative gain, which is useful to simplify the practical partner choice process.
Color image super-resolution reconstruction based on the sparse representation model usually adopts the regularization norm (e.g., $L_{1}$ or $L_{2})$. These methods have limited ability to keep image texture detail to some extent and are easy to cause the problem of blurring details and color artifacts in color reconstructed images. This paper presents a color super-resolution reconstruction method combining the $L_{2 / 3}$ sparse regularization model with color channel constraints. The method converts the low-resolution color image from RGB to YCbCr. The $L_{2 / 3}$ sparse regularization model is designed to reconstruct the brightness channel of the input low-resolution color image. Then the color channel-constraint method is adopted to remove artifacts of the reconstructed high-resolution image. The method not only ensures the reconstruction quality of the color image details, but also improves the removal ability of color artifacts. The experimental results on natural images validate that our method has improved both subjective and objective evaluation.
An indoor location system based on multilayer artificial neural network (ANN) with area division is proposed. The characteristics of recorded signal strength (RSS), or signal to noise ratio (SNR) from each available access points (APs), are utilized to establish the radio map in the off-line phase. And in the on-line phase, the two or three dimensional coordinates of mobile terminals (MTs) are estimated according to the similarity between the new recorded RSS or SNR and fingerprints pre-stored in radio map. Although the feed-forward ANN with three layers is sufficient to describe any nonlinear mapping relationship between inputs and outputs with finite discontinuous points, the efficient inputs for better training performances are difficult to be determined because of complex and dynamic indoor environment. Then, the discussion of distance relativity for different signal characteristics and optimal strategies for multi-mode phenomenon avoidance is presented. And also, the feasibility and effectiveness of this method are verified based on the experimental comparison with normal ANN without area division, K-nearest neighbor (KNN) and probability methods in typical office environment.
A new method to construct shift sequence sets is presented. Different shift sequence sets are obtained by changing parameters of the shift sequence. Based on these shift sequence sets, multiple shift distinct array sets with zero-correlation zone (ZCZ) can be obtained by utilizing interleaving technique. It is shown that the resultant ZCZ array sets are optimal or almost optimal with respect to the Tang, Fan, and Matsufuji bound. Compared with previous methods, the proposed method extends the number of shift distinct ZCZ array sets.
Data envelopment analysis (DEA) is a mathematical programming approach to appraise the relative efficiencies of peer decision-making unit (DMU), which is widely used in ranking DMUs. However, almost all DEA-related ranking approaches are based on the self-evaluation efficiencies. In other words, each DMU chooses the weights it prefers to most, so the resulted efficiencies are not suitable to be used as ranking criteria. Therefore this paper proposes a new approach to determine a bundle of common weights in DEA efficiency evaluation model by introducing a multi-objective integer programming. The paper also gives the solving process of this multi-objective integer programming, and the solution is proven a Pareto efficient solution. The solving process ensures that the obtained common weight bundle is acceptable by a great number of DMUs. Finally a numeral example is given to demonstrate the approach.
The problem of robustifying linear quadratic regulators (LQRs) for a class of uncertain affine nonlinear systems is considered. First, the exact linearization technique is used to transform an uncertain nonlinear system into a linear one and an optimal LQR is designed for the corresponding nominal system. Then, based on the integral sliding mode, a design approach to robustifying the optimal regulator is studied. As a result, the system exhibits global robustness to uncertainties and the ideal sliding mode dynamics is the same as that of the optimal LQR for the nominal system. A global robust optimal sliding mode control (GROSMC) is realized. Finally, a numerical simulation is demonstrated to show the effectiveness and superiority of the proposed algorithm compared with the conventional optimal LQR.
The controllability and observability of networked control systems are studied. Aiming at the networked control system with time-varying delay, the sufficient and necessary conditions for complete controllability and complete observability of the system are presented, respectively. Because of Markov characteristic of the network-induced delay, in terms of stochastic theory, a sufficient and necessary condition for completely mean value controllability of networked control systems is obtained. Further, the conditions that the controllability and observability of networked control systems are equivalent to the initial time-invariant system are given. Controllability and observability realization indexes are also discussed, respectively. The numerical example demonstrates the effectiveness of the proposed theory.
There are many population-based stochastic search algorithms for solving optimization problems. However, the universality and robustness of these algorithms are still unsatisfactory. This paper proposes an enhanced self-adaptive evolutionary algorithm (ESEA) to overcome the demerits above. In the ESEA, four evolutionary operators are designed to enhance the evolutionary structure. Besides, the ESEA employs four effective search strategies under the framework of the self-adaptive learning. Four groups of the experiments are done to find out the most suitable parameter values for the ESEA. In order to verify the performance of the proposed algorithm, 26 state-of-the-art test functions are solved by the ESEA and its competitors. The experimental results demonstrate that the universality and robustness of the ESEA outperform its competitors.
The test selection and optimization (TSO) can improve the abilities of fault diagnosis, prognosis and health-state evaluation for prognostics and health management (PHM) systems. Traditionally, TSO mainly focuses on fault detection and isolation, but they cannot provide an effective guide for the design for testability (DFT) to improve the PHM performance level. To solve the problem, a model of TSO for PHM systems is proposed. Firstly, through integrating the characteristics of fault severity and propagation time, and analyzing the test timing and sensitivity, a testability model based on failure evolution mechanism model (FEMM) for PHM systems is built up. This model describes the fault evolutiontest dependency using the fault-symptom parameter matrix and symptom parameter-test matrix. Secondly, a novel method of inherent testability analysis for PHM systems is developed based on the above information. Having completed the analysis, a TSO model, whose objective is to maximize fault trackability and minimize the test cost, is proposed through inherent testability analysis results, and an adaptive simulated annealing genetic algorithm (ASAGA) is introduced to solve the TSO problem. Finally, a case of a centrifugal pump system is used to verify the feasibility and effectiveness of the proposed models and methods. The results show that the proposed technology is important for PHM systems to select and optimize the test set in order to improve their performance level.
A class of latent ancestral graph for modelling the dependence structure of structural vector autoregressive(VAR) model affected by latent variables is proposed.The graphs are mixed graphs with possibly two kind of edges,namely directed and bidirected edges.The vertex set denotes random variables at dif- ferent times.In Gaussian case,the latent ancestral graph leads to a simple parameterization model.A modified iterative conditional fitting algorithm is presented to obtain maximum likelihood esti- mation of the parameters.Furthermore,a log-likelihood criterion is used to select the most appropriate models.Simulations are performed using illustrative examples and results are provided to demonstrate the validity of the methods.
This paper deals with the problem of stability for systems with delay varying in an interval. A new Lyapunov functional, which makes use of the information of both the lower and upper bounds of the interval time-varying delay, is proposed to derive some new stability criteria. Furthermore, the relationship of the time-varying delay and its lower bound and upper bound is taken into account. As a result, some less conservative delay-dependent stability criteria are obtained without ignoring any useful information in the derivative of Lyapunov functional, which are established in the forms of linear matrix inequalities. Numerical examples are provided to show that the obtained results are better than existing ones.
Redundant technology plays an important role in improving the reliability and fault-tolerance of the airborne avionics systems. A Markov state transition model is introduced to the reliability analysis of the redundant inertial navigation system (RINS) in airborne navigation systems. An information processing mechanism based on difference filtering is put forward to strengthen the consistency between the outputs of the equal-precision inertial navigation system (INS). On this basis, the homologous fault monitoring algorithm is designed to realize the homologous fault monitoring of RINS. The simulation is carried out based on the above algorithms, and the results verify the effectiveness of the proposed fault monitoring algorithm based on difference filtering. Research results have good reference value for the configuration and design of RINS in airborne integrated avionics systems.
A trajectory shaping guidance law based on virtual angle (TSGLBVA) is proposed for a re-entry vehicle with the constraints of terminal impact angles and their time derivatives. In the view of differential properties of the maneuvering trajectory, a virtual angle and a virtual radius are defined. Also, the shaping trajectory of the vehicle is established by the polynomials of the virtual angle. Then, four optimized parameters are selected according to the theorem of parameters transformation presented in this paper. Finally, a convergent variant of the Nelder-Mead algorithm is adopted to obtain the reference trajectory, and a trajectory feedback tracking guidance law is designed. The simulation results demonstrate that the TSGLBVA ensures the re-entry vehicle to impact a target precisely from a specified direction with small terminal load factor command, as well as to obtain a maximum or constrained terminal velocity according to various requirements.
A novel H∞ tracking-based decentralized indirect adaptive output feedback fuzzy controller for a class of uncertain large-scale nonlinear systems is developed. By virtue of the proper filtering of the observation error dynamics, the observer-based decentralized indirect adaptive fuzzy control scheme is presented for a class of large-scale nonlinear systems using the combination of H∞ tracking technique, a fuzzy adaptive observer and fuzzy inference systems. The output feedback and adaptation mechanisms are both robust and implementable indeed owing to their freedom from the unavailable observation error vector. All the signals of the closed-loop largescale system are guaranteed to stay uniformly bounded and the output errors take on H∞ tracking performance. Simulation results substantiate the effectiveness of the proposed scheme.
In the future lunar exploration programs of China, soft landing, sampling and returning will be realized. For lunar explorers such as Rovers, Landers and Ascenders, the inertial navigation system (INS) will be used to obtain high-precision navigation information. INS propagates position, velocity and attitude by integration of sensed accelerations, so initial alignment is needed before INS can work properly. However, traditional ground-based initial alignment methods cannot work well on the lunar surface because of its low rotation rate (0.55?/h). For solving this problem, a new autonomous INS initial alignment method assisted by celestial observations is proposed, which uses star observations to help INS estimate its attitude, gyroscopes drifts and accelerometer biases. Simulations show that this new method can not only speed up alignment, but also improve the alignment accuracy. Furthermore, the impact factors such as initial conditions, accuracy of INS sensors, and accuracy of star sensor on alignment accuracy are analyzed in details, which provide guidance for the engineering applications of this method. This method could be a promising and attractive solution for lunar explorer’s initial alignment.
Calibration is a processing procedure for across-track interferometric synthetic aperture radar (InSAR) to achieve an accurate three-dimensional location. A calibration technique, called weighted joint calibration, for the generation of wide-area geocoded digital elevation models (DEMs) is proposed. It calibrates multiple InSAR scenes simultaneously, and allows reducing the number of required ground control points (GCPs) by using tie points (TPs). This approach may ensure the continuity of threedimensional location among adjacent scenes, which is necessary for mosaic and fusion of data coming from different scenes. In addition, it introduces weights to calibration to discriminate GCPs and TPs with different coherences and locations. This paper presents the principles and methodology of this weighted joint calibration technique and illustrates its successful application in airborne In-SAR data.
Aiming at technical difficulties in feature extraction for the inverse synthetic aperture radar (ISAR) target recognition, this paper imports the concept of visual perception and presents a novel method, which is based on the combination of non-negative sparse coding (NNSC) and linear discrimination optimization, to recognize targets in ISAR images. This method implements NNSC on the matrix constituted by the intensities of pixels in ISAR images for training, to obtain non-negative sparse bases which characterize sparse distribution of strong scattering centers. Then this paper chooses sparse bases via optimization criteria and calculates the corresponding non-negative sparse codes of both training and test images as the feature vectors, which are input into k neighbors classifier to realize recognition finally. The feasibility and robustness of the proposed method are proved by comparing with the template matching, principle component analysis (PCA) and nonnegative matrix factorization (NMF) via simulations.
To validate the potential space-time adaptive processing (STAP) algorithms for airborne bistatic radar clutter suppression under nonstationary and non-Gaussian clutter environments, a statistically non-Gaussian, spacetime clutter model in varying bistatic geometrical scenarios is presented. The inclusive effects of the model contain the range dependency of bistatic clutter spectrum and clutter power variation in range-angle cells. To capture them, a new approach to coordinate system conversion is initiated into formulating bistatic geometrical model, and the bistatic non-Gaussian amplitude clutter representation method based on a compound model is introduced. The veracity of the geometrical model is validated by using the bistatic configuration parameters of multi-channel airborne radar measurement (MCARM) experiment. And simulation results manifest that the proposed model can accurately shape the space-time clutter spectrum tied up with specific airborne bistatic radar scenario and can characterize the heterogeneity of clutter amplitude distribution in practical clutter environments.
The angular glint in the near field plays an important role on radar tracking errors. To predict it more efficiently for electrically large targets, a new method based on graphical electromagnetic computing (GRECO) is proposed. With the benefit of the graphic card, the GRECO prediction method is faster and more accurate than other methods. The proposed method at the first time considers the special case that the targets cannot be completely covered by radar beams, which makes the prediction of radar tracking errors more self-contained in practical circumstances. On the other hand, the process of the scattering center extraction is omitted, resulting in possible angular glint prediction in real time. Comparisons between the simulation results and the theoretical ones validate its correctness and value to academic research and engineering applications.
Space-time adaptive processing (STAP) has been proven to be one of the best techniques capable of detecting weak moving targets in strong clutter environment and has been widely applied in airborne ground moving target indication (GMTI) radar. This paper applies an amplitude and phase estimation (APES) approach to two aspects of the STAP algorithm. Firstly, APES is applied to accurately describe the clutter characteristic in angle-Doppler domain. Then, APES is incorporated into the standard STAP algorithm to improve its performance without increasing transmitting/receiving channel and pulse number. The experimental examples show that the detection performance can be improved by using the APES technique, as well as the high computational complexity can be avoided.
The frequency-modulated continuous-wave (FMCW) synthetic aperture radar (SAR) is a light-weight, cost-effective, high-resolution imaging radar, which is suitable for a small flight platform. The signal model is derived for FMCW SAR used in unmanned aerial vehicles (UAV) reconnaissance and remote sensing. An appropriate algorithm is proposed. The algorithm performs the range cell migration correction (RCMC) for continuous non-chirped raw data using the energy invariance of the scaling of a signal in the scale domain. The azimuth processing is based on step transform without geometric resampling operation. The complete derivation of the algorithm is presented. The algorithm performance is shown by simulation results.
