Parameter adjustment that maximizes the energy efficiency of cognitive radio networks is studied in this paper where it can be investigated as a complex discrete optimization problem. Then a quantum-inspired bacterial foraging algorithm (QBFA) is proposed. Quantum computing has perfect characteristics so as to avoid local convergence and speed up the optimization of QBFA. A proof of convergence is also given for this algorithm. The superiority of QBFA is verified by simulations on three test functions. A novel parameter adjustment method based on QBFA is proposed for resource allocation of green cognitive radio. The proposed method can provide a globally optimal solution for parameter adjustment in green cognitive radio networks. Simulation results show the proposed method can reduce energy consumption effectively while satisfying different quality of service (QoS) requirements.

This paper addresses the problem of joint angle and delay estimation (JADE) in a multipath communication scenario. A low-complexity multi-way compressive sensing (MCS) estimation algorithm is proposed. The received data are firstly stacked up to a trilinear tensor model. To reduce the computational complexity, three random compression matrices are individually used to reduce each tensor to a much smaller one. JADE then is linked to a low-dimensional trilinear model. Our algorithm has an estimation performance very close to that of the parallel factor analysis (PARAFAC) algorithm and automatic pairing of the two parameter sets. Compared with other methods, such as multiple signal classification (MUSIC), the estimation of signal parameters via rotational invariance techniques (ESPRIT), the MCS algorithm requires neither eigenvalue decomposition of the received signal covariance matrix nor spectral peak searching. It also does not require the channel fading information, which means the proposed algorithm is blind and robust, therefore it has a higher working efficiency. Simulation results indicate the proposed algorithm have a bright future in wireless communications.

Coordinated multi-point (CoMP) transmission is put forward in the long term evolution-advanced (LTE-A) system to improve both average and cell-edge throughput. CoMP-joint processing (JP) scheme can get a larger cell-edge throughput and a lower bit error rate (BER) than the CoMP-coordinated beamforming (CB) scheme, but it also has higher complexity due to data sharing. A hybrid coordinated strategy with parameter α, which indicates the proportion of users employing the CoMP-JP scheme, is proposed to apply the CoMP-JP scheme to improve the poorer communication quality of cell edge and employ the CoMP-CB scheme for other users to enhance average throughput and spectral efficiency. This paradigm selects users defined by the certain threshold of signal to interference plus noise power ratio (SINR) corresponding to the parameter α to the CoMP-JP scheme. This paper compares the BER performance between the block diagonalization (BD) based precoding and the linear precoders by maximizing signal to leakage and noise ratio (SLNR), and also indicates that the SLNR based precoding algorithm gets lower BER than the BD based precoding algorithm with certain signal-to-noise ratios (SNRs). Finally, this paper discusses that the system performance is partially affected by the percentage of CoMP-JP users and concludes that 50% of users sorted to communicate under the CoMP-JP scheme will reach the best system performance.

Orthogonal frequency division multiplexing (OFDM) produces a high peak-to-average power ratio (PAPR) that adversely affects high-speed OFDM data transmission. In order to reduce the high PAPR, an efficient nonlinear companding transform (NCT) function is proposed. With the proposed NCT function, the compression and expansion weights can be applied independently with suitably chosen function parameter values. As a result, the proposed function can easily maintain the average signal power approximately unchanged during the companding process. In this regard, the proposed function is superior to previously proposed schemes. Also, the simulations show the outstanding PAPR reduction performance of the proposed function. It is demonstrated that the proposed scheme performs well with nonlinear transmitter amplifiers and delivers superior error performance, compared with error function and exponential function based schemes.

In order to obtain the robust high-resolution beamforming, a high order cross sensor processing (CSP) approach is developed. According to the relation ship between the target bearing and the phase difference of each element receiving signal, this method exploits the property that the same diagonal of covariance matrix with the same phase difference and obtains (2M−1)(N−1) virtual elements (N is the original array number) by executing M order CSP. The extended virtual elements can effectively increase the physical aperture of linear array, reduce the main lobe width of beam-forming, and improve the bearing resolution. The CSP method accumulates the data on the same sub-diagonal of the covariance matrix, which can decrease the impact of background noise on beam-forming. The theoretical analysis and experimental results both show that this method has high resolution in bearing estimation, compared with the MUSIC method, which has better robustness under the lower signal-to-noise ratio (SNR).

The method of processing of the non-stationary casual processes with the use of nonparametric methods of the theory of decisions is considered. The use of such methods is admissible in telemetry systems in need of processing at real rate of time of fast-changing casual processes in the conditions of aprioristic uncertainty about probabilistic properties of measured process.

A new approach called the robust structured total least squares (RSTLS) algorithm is described for solving location inaccuracy caused by outliers in the single-observer passive location. It is built within the weighted structured total least squares (WSTLS) framework and improved based on the robust estimation theory. Moreover, the improved Danish weight function is proposed according to the robust extremal function of the WSTLS, so that the new algorithm can detect outliers based on residuals and reduce the weights of outliers automatically. Finally, the inverse iteration method is discussed to deal with the RSTLS problem. Simulations
show that when outliers appear, the result of the proposed algorithm is still accurate and robust, whereas that of the conventional algorithms is distorted seriously.

At present, most of the passive radar system researches utilize FM radios, TV broadcasts, navigation satellites, etc. as illuminators. The transmitted signals are not specifically designed radar waveforms. In this work, the frequency agile, phased array air surveillance radar (ASR) is used as the illuminator of opportunity to detect the weak target. The phased array technology can help realize beam agility to track targets from different aspects simultaneously. The frequency agility technology is
widely employed in radar system design to increase the ability of anti-jamming and increase the detection probability. While the frequency bandwidth of radar signals is usually wide and the range resolution is high, the range cell migration effect is obvious during the long time integration of non-cooperative bistatic radar. In this context, coherent integration methods are not applicable. In this work, a parametric non-coherent integration algorithm based on task de-interweaving is proposed. Numerical experiments verify that this is effective in weak target detection.

In order to improve the measurement accuracy of weapons, ultra-wideband (UWB) inverse synthetic aperture radar (ISAR) imaging of high-speed moving targets in short-range is studied. In the scheme, because the positions of scatters vary with the radar line-of-sight (LOS) angle when the high-speed target is passing through the closest point of the approach, the concept of equivalent scattering point (ESP) is proposed. The Hough transform is employed to implement ESP motion parameter estimation, while the target translational motion is compensated by using the ESP time delay, and the angle spanned by the target is estimated by utilizing geometric relationship of ESP motion. Finally, the twodimensional image of the target is restructured by using the near field time-domain back-projection algorithm. Simulation results confirm that the proposed method is efficient.

This paper mainly revolves the time-frequency image of low probability of intercept (LPI) radar signals and carries out research work on image features selection and extraction and recognition. Since Choi-Williams distribution (CWD) uses the exponential kernel of bilinear generalized class of time-frequency distribution, it has an excellent time-frequency aggregation. And it is suitable for detecting LPI radar signals in a low signal-to-noise ratio (SNR) condition. A radial integration method based on the integral rotating factor is proposed to detect LPI radar signals when the signals’ time-frequency image is obtained. First, the digital image processing method is used to preprocess the LPI radar signals’ time-frequency images after CWD transformation; then, the radial integration method based on the integral rotating factor is used to detect LPI radar signals in the binary images. The analytic results of real data show that the method has a good performance on detecting LPI radar signals in a low SNR condition. Additionally, the method is simple and takes less logic resources and has the potential of real-time detection of LPI radar signals.

The scheduling of earth observation satellites (EOSs) data transmission is a complex combinatorial optimization problem. Current researches mainly deal with this problem on the assumption that the data transmission mode is fixed, either playback or real-time transmission. Considering the characteristic of the problem, a multi-satellite real-time and playback data transmission scheduling model is established and a novel algorithm based on quantum discrete particle swarm ptimization (QDPSO) is proposed. Furthermore, we design the longest compatible transmission chain mutation operator to enhance the performance of the algorithm. Finally, some experiments are implemented to validate correctness and practicability of the proposed algorithm.

Clustering-based sensor-management schemes have been widely used for various wireless sensor networks (WSNs), as they are well suited to the distributive and collaborative nature of WSN. In this paper, a C60-based clustering algorithm is proposed for the specific planned network of space tracking and surveillance system (STSS), where all the sensors are partitioned into 12 clusters according to the C60 (or football surface) architecture, and then a hierarchical sensor-management scheme is well designed. Finally, the algorithm is applied to a typical STSS constellation, and the simulation results show that the proposed method has better target-tracking performance than the nonclustering scheduling method.

The problem of task assignment for multiple cooperating unmanned aerial vehicle (UAV) teams is considered. Multiple UAVs forming several small teams are needed to perform attack tasks on a set of predetermined ground targets. A hierarchical task assignment method is presented to address the problem. It breaks  the original problem down to three levels of sub-problems: target clustering, cluster allocation and target assignment. The first two sub-problems are centrally solved by using clustering algorithms and integer linear programming, respectively, and the third sub-problem is solved in a distributed and parallel manner, using
a mixed integer linear programming model and an improved ant colony algorithm. The proposed hierarchical method can reduce the computational complexity of the task assignment problem considerably, especially when the number of tasks or the number of UAVs is large. Experimental results show that this method is feasible and more efficient than non-hierarchical methods.

In order to exploit the enhancement of the multiobjective evolutionary algorithm based on decomposition (MOEA/D), we propose an improved algorithm with uniform design (UD), i.e. MOEA/D-UD. Three mechanisms in MOEA/D-UD are modified by introducing an experimental design method called UD. To fully employ the information contained in the domain of the multi-objective problem, we apply UD to initialize a uniformly scattered population. Then, motivated by the analysis of the relationship between weight vectors and optimal solutions of scalar subproblems in the study of MOEA/D with adaptive weight adjustment (MOEA/D-AWA), a new weight vector design method based on UD is introduced. To distinguish real sparse regions from pseudo sparse regions, i.e. discontinuous regions, of the complex Pareto front, the weight vector adjustment strategy in MOEA/D-UD adequately utilizes the information from neighbors of individuals. In the experimental study, we compare MOEA/D-UD with three outstanding algorithms, namely MOEA/D with the differential evolution operator (MOEA/D-DE), MOEA/D-AWA and the nondominated sorting genetic algorithm II (NSGA-II) on nineteen test instances. The experimental results show that MOEA/D-UD is capable of obtaining a well-converged and well diversified set of solutions within an acceptable execution time.

The TODIM (an acronym in Portuguese for interactive and multiple attribute decision making) method is a valuable tool to solve the multiple attribute decision making (MADM) problems considering the behavior of the decision maker (DM), while it cannot be used to handle the problem with unknown weight information on attributes. In this paper, a novel method based on the classical TODIM method is proposed to solve the hybrid MADM problems with unknown weight information on attributes, in which attribute values are represented in four different formats: crisp numbers, interval numbers, triangular fuzzy numbers and trapezoidal fuzzy numbers. Firstly, the positive-ideal alternative and negative-ideal alternative are determined, and the gain and loss matrices are constructed by calculating the gain and loss of each alternative relatived to the ideal alternatives concerning each attribute based on different distance calculation formulas, which may avoid the information missing or information distortion in the process of unifying multiform attribute values into a certain representation form. Secondly, an optimization model based on the maximizing deviation (MD) method, by which the attribute weights can be determined, is established for the TODIM method. Further, the calculation steps to solve the hybrid MADM problems are given. Finally, two numerical examples are presented to illustrate the usefulness of the proposed method, and the results show that the DM’s psychological behavior, attribute weights and the transformed information would highly affect the ranking orders of alternatives.

To ensure success of precise navigation, it is necessary to carry out in-field calibration for the accelerometers in platform inertial navigation system (PINS) before a mission is launched. Traditional continuous self-calibration methods are not fit for fast calibration of accelerometers because the platform misalignments have to be estimated precisely and the nonlinear coupling terms will affect accuracy. The multi-position methods with a “shape of motion” algorithm also have some existing disadvantages: High precision calibration results cannot be obtained when the accelerometer’s output data are used directly and it is difficult to optimize the calibration scheme. Focusing on this field, this paper proposes new fast self-calibration methods for the accelerometers of PINS. A data compression filter is employed to improve the accuracy of parameter estimation because it is impossible to obtain non-biased estimation for accelerometer parameters when using the “shape of motion” algorithm. Besides, continuous calibration schemes are designed and optimized by the genetic algorithm (GA) to improve the observability of parameters. Simulations prove that the proposed methods can estimate the accelerometer parameter more precisely than traditional continuous methods and multi-position methods, and they are more practical to deal with urgent situations than multi-position methods.

This paper presents a gain-scheduling model predictive control (MPC) for linear parameter varying (LPV) systems subject to actuator saturation. The proposed gain-scheduling MPC algorithm is then applied to the lateral control of unmanned airship. The unmanned airship is modeled by an LPV-type system and transformed into a polytopic uncertain description with actuator saturation. By introducing a parameter-dependent state feedback law, the set invariance condition of the polytopic uncertain system is identified. Based on the invariant set, the gain-scheduling MPC controller is presented by solving a linear matrix inequality (LMI) optimization problem. The proposed gain-scheduling MPC algorithm is demonstrated by simulating on the unmanned airship system.

The globally optimal recursive filtering problem is studied for a class of systems with random parameter matrices, stochastic nonlinearities, correlated noises and missing measurements. The stochastic nonlinearities are presented in the system model to reflect multiplicative random disturbances, and the additive noises, process noise and measurement noise, are assumed to be one-step autocorrelated as well as two-step cross-correlated A series of random variables is introduced as the missing rates governing the intermittent measurement losses caused by unfavorable network conditions. The aim of the addressed filtering problem is to design an optimal recursive filter for the uncertain systems based on an innovation approach such that the filtering error is globally minimized at each sampling time. A numerical simulation example is provided to illustrate the effectiveness and applicability of the proposed algorithm.

This paper is devoted to the problem of modeling and adaptive motion/force tracking for a class of nonholonomic dynamic systems with affine constraints (NDSAC): a vertical wheel on a rotating table. Prior to the development of tracking controller, the dynamic model of the wheel in question is derived in a meticulous manner. A continuously differentiable friction model is also considered in the modeling. By exploiting the inherent cascade interconnected structure of the wheel dynamics, an adaptive motion/ force tracking controller is presented guaranteeing that the trajectory tracking errors asymptotically converge to zero while the contact force tracking errors can be made small enough by tuning design parameters. Simulation results are provided to validate the effectiveness of the proposed tracking methodology.

An improved single image dehazing method based on dark channel prior and wavelet transform is proposed. This proposed method employs wavelet transform and guided filter instead of the soft matting procedure to estimate and refine the depth map of haze images. Moreover, a contrast enhancement method based on just noticeable difference (JND) and quadratic function is adopted to enhance the contrast for the dehazed image, since the scene radiance is usually not as bright as the atmospheric light, and the dehazed image looks dim. The experimental results show that the proposed approach can effectively enhance the haze image and is well suitable for implementing on the surveillance and obstacle detection systems.

Shape-from-shading (SFS) is one of the important approaches of 3-D surface reconstruction in computer vision. Since reflectance map equation in SFS is a nonlinear partial differential equation (PDE) with two unknown variables, SFS with one image is ill-posed in mathematical sense. A linear perspective SFS method with two images is proposed to deal with the problem. We assume that two images with different light source directions are captured firstly. Orthogonal projection is not as accurate as perspective one to simulate imaging processes. Two reflectance map equations are established based on the Lambertian model under perspective projection, and the equations are further transformed into one linear PDE. Then the iterative semi-Lagrangian algorithm is used to approximate the solution. Finally, 3-D height values of pixel points in imaging planes are solved by the numerical interpolation method. Experimental results of both hemisphere and complex surfaces show that the proposed method can reconstruct surfaces accurately.

In order to make cloud users get credible, high-quality composition of services, the trust quality of service aware (TQoS-aware) based parallel ant colony algorithm is proposed. Our approach takes the service credibility as the weight of the quality of service, then calculates the trust service quality T-QoS for each service, making the service composition situated in a credible environment. Through the establishment on a per-service T-QoS initialization pheromone matrix, we can reduce the colony’s initial search time. By modifying the pheromone updating rules and introducing two ant colonies to search from different angles in parallel, we can avoid falling into the local optimal solution, and quickly find the optimal combination of global solutions. Experiments show that our approach can combine high-quality services and the improvement of the operational success rate. Also, the convergence rate and the accuracy of optimal combination are improved.

To deal with the demerits of constriction particle swarm optimization (CPSO), such as relapsing into local optima, slow convergence velocity, a modified CPSO algorithm is proposed by improving the velocity update formula of CPSO. The random velocity operator from local optima to global optima is added into the velocity update formula of CPSO to accelerate the convergence speed of the particles to the global optima and reduce the likelihood of being trapped into local optima. Finally the convergence of the algorithm is verified by calculation examples.

With progression of the digital age, the complexity of software continues to grow. As a result, methods to quantitatively assess characteristics of software have attracted significant attention. These efforts have led to a large number of new measures such as coupling metrics, many of which seek to consider the impact of correlations between components and failures on application reliability. However, most of these approaches set the coupling parameters arbitrarily by making assumptions instead of utilizing experimental data and therefore may not accurately capture actual coupling between components of software application. Since the coupling matrix is often set arbitrarily, the existing approaches to assess software reliability considering component correlation fail to reflect the real degree of interaction and relationships among software components. This paper presents an efficient approach to assess the software reliability considering correlated component failures, incorporating software architecture while considering actual internal coupling of software with an efficient approach based on multivariate Bernoulli (MVB) distribution. The unified framework for software coupling measurement is informed by a comprehensive survey of frameworks for objectoriented and procedure-oriented software. This framework enables the extraction of more accurate coupling among components. The effectiveness of this method is illustrated through an experimental study by applying it to a real-time software application.

This paper studies a cold standby repairable system with working vacations and vacation interruption. The repairman’s multiple vacations policy, the working vacations policy and the vacation interruption are considered simultaneously. The lifetime of components follows a phase-type (PH) distribution. The repair time in the regular repair period and the working vacation period follow other two PH distributions at different rates. For this system, the vector-valued Markov process governing the system is constructed. We obtain several important performance measures for the system in transient and stationary regimes applying matrixanalytic
methods. Finally, a numerical example is given to illustrate the results obtained.