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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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 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.

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 overview on nonlinear reconfigurable flight control approaches that have been demonstrated in flight-test or highfidelity simulation is presented. Various approaches for reconfigurable flight control systems are considered, including nonlinear dynamic inversion, parameter identification and neural network technologies, backstepping and model predictive control approaches. The recent research work, flight tests, and potential strength and weakness of each approach are discussed objectively in order to give readers and researchers some reference. Finally, possible future directions and open problems in this area are addressed.

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 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 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.

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.

This paper presents an approach to the challenging issue of passive source localization in shallow water using a mobile short horizontal linear array with length less than ten meters. The short array can be conveniently placed on autonomous underwater vehicles and deployed for adaptive spatial sampling. However, the use of such small aperture passive sonar systems makes it difficult to acquire sufficient spatial gain for localizing long-range sources. To meet the requirement, a localization approach that employs matched-field based techniques that enable the short horizontal linear array is used to passively localize acoustic sources in shallow water. Furthermore, the broadband processing and inter-position processing provide robustness against ocean environmental mismatch and enhance the stability of the estimation process. The proposed approach’s ability to localize acoustic sources in shallow water at different signal-to-noise ratios is examined through the synthetic test cases where the sources are located at the endfire and some other bearing of the mobile short horizontal linear array. The presented results demonstrate that the positional parameters of the estimated source build up over time as the array moves at a low speed along a straight line at a constant depth.

A global convergent algorithm is proposed to solve
bilevel linear fractional-linear programming,which is a special
class of bilevel programming.In our algorithm,replacing the lower
level problem by its dual gap equaling to zero,the bilevel linear
fractional-linear programming is transformed into a traditional sin-
gle level programming problem,which can be transformed into a
series of linear fractional programming problem.Thus,the modi-
fied convex simplex method is used to solve the infinite linear
fractional programming to obtain the global convergent solution of
the original bilevel linear fractional-linear programming.Finally,an
example demonstrates the feasibility of the proposed algorithm.

This paper studies the effect of amplitude-phase errors on the antenna performance. Via builting on a worst-case error tolerance model, a simple and practical worst error tolerance analysis based on the chaos-genetic algorithm (CGA) is proposed. The proposed method utilizes chaos to optimize initial population for the genetic algorithm (GA) and introduces chaotic disturbance into the genetic mutation, thereby improving the ability of the GA to search for the global optimum. Numerical simulations demonstrate that the accuracy and stability of the worst-case analysis of the proposed approach are superior to the GA. And the proposed algorithm can be used easily for the error tolerant design of antenna arrays.

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.  

Artificial neural network has been used successfully to develope the automatic spike extraction. In order to address some of the problems before the wireless transmission of the implantable chip, the automatic spike sorting method with low complexity and high efficiency is proposed based on the hybrid neural network with the principal component analysis network (PCAN) and normal boundary response (NBR) self-organizing mapping (SOM) network classifier. An automatic PCAN technique is used to reduce the dimension and eliminate the correlation of the spike signal. The NBR-SOM network performs the spike sorting challenge and improves the classification performance. The experimental results show that based on the hybrid neural network, the spike sorting method achieves the accuracy above 97.91% with signals containing five classes. The proposed NBR-SOM network classifier is to further improve the stability and effectiveness of the classification system.

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.

According to the delay property, linear time-delay (LTD) systems can be classified as LTD systems with dependent delays (LTD DD) and LTD systems with independent delays (LTD ID). This paper reveals that the stability condition for LTD ID systems can be applied to LTD DD systems, a sufficient stability condition for LTD DD systems is derived from it, while only fewer of the LTD DD systems can satisfy the stability condition due to the very strict limitation for the delays of the LTD DD systems. To solve the problem, based on two-dimensional (2-D) hybrid polynomials, some sufficient conditions for stability of LTD DD systems are proposed. Examples show that the proposed stability test algorithms are simple and valid.

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.

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.

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.

A combination method of optimization of the background value and optimization of the initial item is proposed. The sequences of the unbiased exponential distribution are simulated and predicted through the optimization of the background value in grey differential equations. The principle of the new information priority in the grey system theory and the rationality of the initial item in the original GM(1,1) model are fully expressed through the improvement of the initial item in the proposed time response function. A numerical example is employed to illustrate that the proposed method is able to simulate and predict sequences of raw data with the unbiased exponential distribution and has better simulation performance and prediction precision than the original GM(1,1) model relatively.

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.