Multi-view laser radar (ladar) data registration in obscure environments is an important research field of obscured target detection from air to ground. There are few overlap regions of the observational data in different views because of the occluder, so the multi-view data registration is rather difficult. Through indepth analyses of the typical methods and problems, it is obtained that the sequence registration is more appropriate, but needs to improve the registration accuracy. On this basis, a multi-view data registration algorithm based on aggregating the adjacent frames, which are already registered, is proposed. It increases the overlap region between the pending registration frames by aggregation and further improves the registration accuracy. The experiment results show that the proposed algorithm can effectively register the multi-view ladar data in the obscure environment, and it also has a greater robustness and a higher registration accuracy compared with the sequence registration under the condition of equivalent operating efficiency.
Ultrafast optoelectronic technology has been widely used in terahertz time domain spectrum, terahertz imaging technology, terahertz communication and so on, and great progress has been achieved in the past two decade. Recently, this innovative technology has been applied in radio metrology and supplied a potential and hopeful method to solve the existent challenges of calibration devices and equipments with bandwidth up to 100 GHz. This paper generally summarizes the emerging applications of the ultrafast optoelectronic technology in radio metrology. The main applications of this technology in calibrating broadband sampling oscilloscopes, the high-speed photodiodes and calibrating the electrical pulse generators are emphasized, and the testing of monolithic microwave integrated circuits is also presented.
The state prediction based on the unscented Kalman filter (UKF) for nonlinear stochastic discrete-time systems with linear measurement equation is investigated. Predicting future states by using the information of available measurements is an effective method to solve time delay problems. It not only helps the system operator to perform security analysis, but also allows more time for operator to take better decision in case of emergency. In addition, predictive state can make the system implement real-time monitoring and achieve good robustness. UKF has been popular in state prediction because of its advantages in handling nonlinear systems. However, the accuracy of prediction degrades notably once a filter uses a much longer future prediction. A confidence interval (CI) is proposed to overcome the problem. The advantages of CI are that it provides the information about states coverage, which is useful for treatment-plan evaluation, and it can be directly used to specify the margin to accommodate prediction errors. Meanwhile, the CI of prediction errors can be used to correct the predictive state, and thereby it improves the prediction accuracy. Simulations are provided to demonstrate the effectiveness of the theoretical results.
An adaptive repetitive control scheme is presented for a class of nonlinearly parameterized systems based on the fuzzy basis function network (FBFN). The parameters of the fuzzy rules are tuned with adaptive schemes. To attenuate chattering effectively, the discontinuous control term is approximated by an adaptive PI control structure. The bound of the discontinuous control term is assumed to be unknown and estimated by an adaptive mechanism. Based on the Lyapunov stability theory, an adaptive repetitive control law is proposed to guarantee the closed-loop stability and the tracking performance. By means of FBFNs, which avoid the nonlinear parameterization from entering into the adaptive repetitive control, the controller singularity problem is solved. The proposed approach does not require an exact structure of the system dynamics, and the proposed controller is utilized to control a model of permanent-magnet linear synchronous motor subject to significant disturbances and parameter uncertainties. The simulation results demonstrate the effectiveness of the proposed method.
A novel tunable-quality-factor (tunable-Q) contourlet transform for geometric image representation is proposed. The Laplacian pyramid in original contourlet decomposes a signal into channels that have the same bandwidth on a logarithmic scale, and is not suitable for images with different behavior in frequency domain. We employ a new tunable-Q decomposition defined in the frequency domain by which one can flexibly tune the bandwidth of decomposition channels. With an acceptable redundancy, this tunable-Q contourlet is also anti-aliasing and its basis is sharply localized in the desired area of frequency and spatial domain. Our experiments in nonlinear approximation and denoising show that the contourlet using a better-suitable quality factor can achieve a more promising performance and often outperform wavelets and the previous contourlets both in visual quality and in terms of peak signal-to-noise ratio.
Opportunistic array radar (OAR) is a new generation radar system based on the stealth of the platform, which can improve the modern radar performance effectively. Designing the orthogonal code sets with low autocorrelation and cross-correlation is a key issue for OAR. This paper proposes a novel hybrid genetic algorithm (HGA) and designs the polyphase orthogonal code sets with low autocorrelation and cross-correlation properties, which can be used in the OAR system. The novel algorithm combines with simulated annealing (SA) and genetic algorithm (GA), adds in keeping best individuals and competition in small scope, and introduces grey correlation evaluation to evaluate fitness function. These avoid the premature convergence problem existed in GA and enhance the global searching capability. At last, the genetic results are optimized to obtain the best solution by using greedy algorithm. The simulation results show that the proposed algorithm is effective for the design of orthogonal phase signals used in OAR systems.
A new type of estimator is developed for the satellite formation to track and inspect on-orbit targets. The follower satellite in the formation works without relative sensors, and its target pointing commands are derived based on relative orbital dynamics. The centralized estimator based on truth measurement is designed, however, this estimator is proved unstable because of the lack of necessary measurement information. After that, an alternative estimator based on pseudo measurement is designed, and its observability and controllability are analyzed to qualitatively evaluate the convergence performance. Finally, an on-orbit target inspection scenario is numerically simulated to verify the performance of the estimator based on pseudo measurement.
For the robustness problem of open-loop P-type iterative learning control under the influence of measurement noise which is inevitable in actual systems, an adaptive adjustment algorithm of iterative learning nonlinear gain matrix based on error amplitude is proposed and two nonlinear gain functions are given. Then with the help of Bellman-Gronwall lemma, the robustness proof is derived. At last, an example is simulated and analyzed. The results show that when there exists measurement noise, the proposed learning law adjusts the learning gain matrix on line based on error amplitude, thus can make a compromise between learning convergence rate and convergence accuracy to some extent: the fast convergence rate is achieved with high gain in initial learning stage, the strong robustness and high convergence accuracy are achieved at the same time with small gain in the end learning stage, thus better learning results are obtained.
Recently, the code division multiple access (CDMA) waveform exists in the large area across the world. However, when using the CDMA system as the illuminator of opportunity for the passive bistatic radar (PBR), there exists interference not only from the base station used as the illuminator of opportunity but also from other base stations with the same frequency. And because in the CDMA system, the signal transmitted by each base station is different, using the direct signal of one base station can not cancel the interference from other base stations. A CDMA-based PBR using an 8-element linear array antenna as both the reference antenna and surveillance antenna is introduced. To deal with the interference in this PBR system, an adaptive temporal cancellation algorithm is used to remove the interference from the base station used as the illuminator of opportunity firstly. And then a robust adaptive beamformer is used to suppress the interference from other base stations. Finally, the preliminary experiment results demonstrate the feasibility of using CDMA signals as a radar waveform.
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.
An efficient compensation scheme combining a timedomain Gaussian elimination (GE) channel estimator and a frequency-domain GE equalizer is proposed for orthogonal frequency division multiplexing (OFDM) systems with frequencydependent in-phase and quadrature-phase (IQ) imbalances at both transmitter and receiver. Compared with the traditional least square and least mean square compensation schemes, the proposed compensation scheme achieves the same bit error rate as the ideal IQ branches by using only two training OFDM symbols instead of about 20 OFDM symbols.
A performance improvement model of research and development (R&D) institutions based on evolutionary game and Bayesian network is proposed. First, the nature and performance factors of new R&D institutions are systematically analyzed, the appropriate factor model is found, and the sharing of performance benefits between institutions and employees, the change in distribution proportion, and the risk of institutional improvement and employee cooperation are considered. Second, based on the mechanism improvement and employee cooperation, the payment matrix is given and evolutionary game analysis is carried out to obtain a stable and balanced institutional improvement probability and employee cooperation probability. These two probability values are substituted into the Bayesian network model of performance improvement of new R&D institutions, and the posterior probability of performance improvement is predicted by Bayesian network reasoning and diagnosis to find effective improvement measures. Finally, practical case analysis is given to verify the effectiveness and practicability of the proposed method.
The comprehensive evaluation of six properties for equipment product is an important basis for their quality control, and their correlative relationship among six properties will affect their quality level. To understand their correlative relationship among six properties, this paper firstly combines group evaluation with decision-making trial and evaluation laboratory (DEMATEL) model, and develops the optimization model based on group consensus to form six influent relationship matrices. Secondly, group consensus matrix is used to design super network hierarchy matrix, and the weights of six properties with relevant environment is also proposed. Thirdly, the elimination and choice translating reality (ELECTRE) model is used to make comprehensive evaluation, and an example is used to compare the results under two kinds of conditions, and illustrate the effect of the weights of six properties on the priority of equipment products.
Space time trellis coding (STTC) techniques have been proposed to achieve both diversity and coding gains in multiple input multiple output (MIMO) fading channels. But with more transmit antennas STTCs suffer from the design difficulty and complexity increasing. This paper proposes a scheme, named parallel concatenated space time trellis codes (PC-STTC), to achieve the tradeoff between the performances and complexity of STTCs for a large number of transmit antennas. Simulation results and complexity comparison are provided to demonstrate the performance and superiority of the proposed scheme over conventional schemes in fast fading channels in low signal-to-noise ratio (SNR) regions. And an EXIT (extrinsic information transform) chart is given to analyze the iterative convergence of the proposed scheme. It shows that PC-STTC has better iterative convergence in low SNR regions.
Many Bayesian learning approaches to the multi-layer perceptron (MLP) parameter optimization have been proposed such as the extended Kalman filter (EKF). This paper uses the unscented Kalman particle filter (UPF) to train the MLP in a selforganizing state space (SOSS) model. This involves forming augmented state vectors consisting of all parameters (the weights of the MLP) and outputs. The UPF is used to sequentially update the true system states and high dimensional parameters that are inherent to the SOSS model for the MLP simultaneously. Simulation results show that the new method performs better than traditional optimization methods.
In order to calculate the cross-correlation of two color images treated as vector in a holistic manner, a rapid vertical/parallel decomposition algorithm for quaternion is resented. The calculation for decomposition is reduced from 21 times to 4 times real number multiplications with the same results. An algorithm for cross-correlation of color images based on decomposition in time domain is put forward, in which some properties pointed out in this paper can be utilized to reduce the computational complexity. Simulation results show the effectiveness and superiority of the proposed method.
The problem of fault detection for linear discrete timevarying systems with multiplicative noise is dealt with. By using an observer-based robust fault detection filter (FDF) as a residual generator, the design of the FDF is formulated in the framework of H∞ filtering for a class of stochastic time-varying systems. A sufficient condition for the existence of the FDF is derived in terms of a Riccati equation. The determination of the parameter matrices of the filter is converted into a quadratic optimization problem, and an analytical solution of the parameter matrices is obtained by solving the Riccati equation. Numerical examples are given to illustrate the effectiveness of the proposed method.
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.
This paper presents a methodology for automatically generating risk scenarios for dynamic reliability applications in which some dynamic characteristics (e.g., the order, timing and magnitude of events, the value of relevant process parameters and initial conditions) have a significant influence on the evolution of the system. The main idea of the methodology is: (i) making the system model "express itself" through simulation by having the model driven by an elaborated simulation engine; (ii) exploiting uniform design to pick out a small subset of representative design points from the space of relevant dynamic characteristics; (iii) for each selected design point, employing a depth-first systematic exploration strategy to cover all possible scenario branches at each branch point. A highly dynamic example adapted from the literature (a chemical batch reactor) is studied to test the effectiveness of the proposed methodology.
For anti-jamming and anti-countermeasure techniques of the sonar receiver, the response characteristics of the automatic gain control (AGC) circuit and the survivability of the prime circuit under strong interference are analyzed by simulations and experiments. An AGC simulation model based on the voltage control amplifier VCA810 prototype is proposed. Then static and dynamic simulations are realized with single frequency signal and linear frequency modulated (LFM) signal commonly used in the active sonar. Based on intense sound pulse (ISP) interference experiments, the real-time response characteristics of each module of the receiver are studied to verify the correctness of the model as well as the simulation results. Simulation and experiment results show that, under 252 dB/20 μs ISP interference, the specific sonar receiver will produce sustained cut top oscillation above 30 ms, which may affect the receiver and block the regular sonar signal.
To diagnose the fault of attitude sensors in satellites, this paper proposes a novel approach based on the Kalman filter of the discrete-time descriptor system. By regarding the sensor fault term as the auxiliary state vector, the attitude measurement system subjected to the attitude sensor fault is modeled by the discrete-time descriptor system. The condition of estimability of such systems is given. And then a Kalman filter of the discretetime descriptor system is established based on the methodology of the maximum likelihood estimation. With the descriptor Kalman filter, the state vector of the original system and sensor fault can be estimated simultaneously. The proposed method is able to estimate an abrupt sensor fault as well as the incipient one. Moreover, it is also effective in the multiple faults scenario. Simulations are conducted to confirm the effectiveness of the proposed method.
This article introduces a fleet composition algorithm for a fleet of intermediate carriers, which should deliver a swarm of miniature unmanned aerial vehicles (mini-UAVs) to a mission area. The algorithm is based on the sequential solution of several knapsack problems with various constraints. The algorithm allows both to form an initial set of required types of intermediate carriers, and to generate a fleet of intermediate carriers. The formation of a fleet of intermediate carriers to solve a suppression of enemy air defense (SEAD) problem is presented to illustrate the proposed algorithm.
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.
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.
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.
Modeling technology has been introduced into software testing field. However, how to carry through the testing modeling effectively is still a difficulty. Based on combination of simulation modeling technology and embedded real-time software testing method, the process of simulation testing modeling is studied first. And then, the supporting environment of simulation testing modeling is put forward. Furthermore, an approach of embedded real-time software simulation testing modeling including modeling of cross-linked equipments of system under testing (SUT), test case, testing scheduling, and testing system service is brought forward. Finally, the formalized description and execution system of testing models are given, with which we can realize real-time, closed loop, and automated system testing for embedded real-time software.
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.
The function of the air target threat evaluation (TE) is the foundation for weapons allocation and senor resources management within the surface air defense. The multi-attribute evaluation methodology is utilized to address the issue of the TE in which the tactic features of the detected target are treated as evaluation attributes. Meanwhile, the intuitionistic fuzzy set (IFS) is employed to deal with information uncertainty in the TE process. Furthermore, on the basis of the entropy weight and inclusioncomparison probability, a hybrid TE method is developed. In order to accommodate the demands of naturalistic decision making, the proposed method allows air defense commanders to express their intuitive opinions besides incorporating into the threat features of the detected target. An illustrative example is provided to indicate the feasibility and advantage of the proposed method.
A new method to improve prediction precision of GM(1,1) model with unequal time interval is presented. The grey derivative is multiplied by a parameter to guarantee the time response function satisfying approximately exponential function distribution. To simplify the process of parametric estimation, an approximate value is taken for the multiplied parameter. Then the estimators of coefficient of development and grey action quantity can be derived. At the same time, the principle of the new information priority is also considered. We take the last item of the first-order accumulated generation operator (1-AGO) on raw data sequence as the initial condition in the time response function. Then the new information can be taken full advantage of through the improved initial condition. Some properties of this new model are also discussed. The presented method is actually a combination of improvement of grey derivative and improvement of the initial condition. The results of an example indicate that the proposed method can improve prediction precision prominently.
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.
Although it is known that exact sampling algorithm is easy to construct and less sensitive to noise,the samples distri- bution of the algorithm deviates from the target states distribution due to the local dependent coupling problem.A new algorithm, named exact sampling with directional threshold(ES-DT)is intro- duced.The main advantage of the new algorithm,in comparison with the traditional exact sampling algorithm,is that it can control the sampling with a rejection strategy in Markov chain during the path growth,and closely approach the ideal distribution based on maintaining the target density.Simulation experiments show the effectiveness of the proposed algorithm.
Considering the problem of multiple ballistic missiles tracking of boost-phase ballistic missile defense, a boost-phase tracking algorithm based on multiple hypotheses tracking (MHT) concept is proposed. This paper focuses on the tracking algorithm for hypothesis generation, hypothesis probability calculation, hypotheses reduction and pruning and other sectors. From an engineering point of view, a technique called the linear assignment problem (LAP) used in the implementation of M-best feasible hypotheses generation, the number of the hypotheses is relatively small compared with the total number that may exist in each scan, also the N-scan back pruning is used, the algorithm’s efficiency and practicality have been improved. Monte Carlo simulation results show that the proposed algorithm can track the boost phase of multiple ballistic missiles and it has a good tracking performance compared with joint probability data association (JPDA).
The theoretical implementation aspects of scattered field prediction and angular glint calculation in near-field region are proposed in this work. First of all, a more refined expression of the Green function is developed. In this representation, an expansion center is adopted within the neighborhood of the sources. Then a high-frequency electromagnetic scattering evaluation algorithm is formulated, combining the refined physical optics (PO) and equivalent edge current (EEC) algorithm. The modified method not only retains the conciseness and efficiency of the standard code but also can be directly used in the near field (NF) scattering estimation. Afterwards, two basic concepts of the angular glint are briefly introduced and formulated. The proposed procedure makes preparation for the computation of NF linear deviation. Numerical examples demonstrate the accuracy and efficiency of the NF scattering prediction algorithm. The angular glint characteristics in near-field scenarios are also presented and analyzed in the final section.
An adaptive approach to select analysis window parameters for linear frequency modulated (LFM) signals is proposed to obtain the optimal 3 dB signal-to-noise ratio (SNR) in the shorttime Fourier transform (STFT) domain. After analyzing the instantaneous frequency and instantaneous bandwidth to deduce the relation between the window length and deviation of the Gaussian window, high-order statistics is used to select the appropriate window length for STFT and get the optimal SNR with the right time-frequency resolution according to the signal characteristic under a fixed sampling rate. Computer simulations have verified the effectiveness of the new method.
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.
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.
The nature of adhoc networks makes them vulnerable to security attacks. Many security technologies such as intrusion prevention and intrusion detection are passive in response to intrusions in that their countermeasures are only to protect the networks, and there is no automated network-wide counteraction against detected intrusions. the architecture of cooperation intrusion response based multi-agent is propose. The architecture is composed of mobile agents. Monitor agent resides on every node and monitors its neighbor nodes. Decision agent collects information from monitor nodes and detects an intrusion by security policies. When an intruder is found in the architecture, the block agents will get to the neighbor nodes of the intruder and form the mobile firewall to isolate the intruder. In the end, we evaluate it by simulation.
