Based on the fabricated 12-element cavity-backed microstrip sector cylinder array, a novel hybrid alternate projection algorithm (HAPA), which combines analytical method with numerical techniques effectively, is proposed for synthesizing the pattern of practical conformal array. The algorithm applies the variable direction aperture projection method with mutual coupling correction techniques to provide the good initial excitations of elements to the enhanced alternate projection algorithm (EAPA). In order to do
further optimization, which improves the convergent speed of the algorithm significantly. Finally, the HAPA has been applied to the fabricated sector cylinder array with mutual coupling considered. The results of synthesized patterns, such as low sidelobe with null points formed pattern, beam scanning with low sidelobe pattern and the shaped beam pattern are presented. It demonstrates the validity of HAPA in practical conformal array synthesis.

On the basis of scale invariant feature transform (SIFT)  descriptors, a novel kind of local invariants based on SIFT sequence scale (SIFT-SS) is proposed and applied to target classification. First of all, the merits of using an SIFT algorithm for target classification are discussed. Secondly, the scales of SIFT descriptors are sorted by descending as SIFT-SS, which is sent to a support vector machine (SVM) with radial based function (RBF) kernel in order to train SVM classifier, which will be used for achieving target classification. Experimental results indicate that the SIFT-SS algorithm is efficient for target classification and can obtain a higher recognition rate than affine moment invariants (AMI) and multi-scale auto-convolution (MSA) in some complex situations, such as the situation with the existence of noises and occlusions. Moreover, the computational time of SIFT-SS is shorter than MSA and longer than AMI.

Target tracking is one of the applications of wireless sensor networks (WSNs). It is assumed that each sensor has a limited range for detecting the presence of the object, and the network is sufficiently dense so that the sensors can cover the area of interest. Due to the limited battery resources of sensors, there is a tradeoff between the energy consumption and tracking accuracy. To solve this problem, this paper proposes an energy efficient tracking algorithm. Based on the cooperation of dispatchers, sensors in the area are scheduled to switch their working mode to track the target. Since energy consumed in active mode is
higher than that in monitoring or sleeping mode, for each sampling interval, a minimum set of sensors is woken up based on the select mechanism. Meanwhile, other sensors keep in sleeping mode. Performance analysis and simulation results show that the proposed algorithm provides a better performance than other existing approaches.

The paper proposes a decentralized concurrent transmission strategy in shared channels based on an incomplete information game in Ad Hoc networks. Based on the nodal channel quality, the game can work out a channel gain threshold, which decides the candidates for taking part in the concurrent transmission. The utility formula is made for maximizing the overall throughput based on channel quality variation. For an achievable Bayesian Nash equilibrium (BNE) solution, this paper further prices the selfish players in utility functions for attempting to improve the channel gain one-sidedly. Accordingly, this game allows each node to distributedly decide whether to transmit concurrently with others depending on the Nash equilibrium (NE). Besides, to make the proposed game practical, this paper next presents an efficient particle swarm optimization (PSO) model to fasten the otherwise very slow convergence procedure due to the large computational complexity. Numerical results show the proposed approach is feasible to increase concurrent transmission opportunities for active nodes and the convergence can be swiftly obtained with a few of iteration times by the proposed PSO algorithm.

The symbol-error-rate (SER) and power allocation for hybrid cooperative (HC) transmission system are investigated. Closed-form SER expression is derived by using the moment generating function (MGF)-based approach. However, the resultant SER contains an MGF of the harmonic mean of two independent random variables (RVs), which is not tractable in SER analysis. We present a simple MGF expression of the harmonic mean of two independent RVs which avoids the hypergeometric functions used commonly in previous studies. Using the simple MGF, closed-form SER for HC system with M-ary phase shift keying (M-PSK) signals is provided. Further, an approximation as well as an upper bound of the SER is presented. It is shown that the SER approximation is asymptotically tight. Based on the tight SER approximation, the power allocation of the HC system is investigated. It is shown that the optimal power allocation does not depend on the fading parameters of the source-destination (SD) channel and it only depends on the source-relay (SR) and relay-destination (RD) channels. Moreover, the performance gain of the power allocation depends on the ratio of the channel quality between RD and SR. With the increase of this ratio, more performance gain can be acquired.

Cognitive radio (CR) technology is considered to be an effective solution to allocate spectrum resources, whereas the primary users of a network do not fully utilize available frequency bands. Spectrum auction framework has been recognized as an effective way to achieve dynamic spectrum access. From the perspective of spectrum auction, multi-band multi-user auction provides a new challenge for spectrum management. This paper proposes an auction framework based on location information for multi-band multi-user spectrum allocation. The performance of the proposed framework is compared with that of traditional auction framework based on a binary interference model as a benchmark. Simulation results show that primary users will obtain more total ystem revenue by selling their idle frequency bands to secondary users and the spectrum utilization of the proposed framework is more effective and fairer.

To solve discrete optimization difficulty of the spectrum allocation problem, a membrane-inspired quantum shuffled frog leaping (MQSFL) algorithm is proposed. The proposed MQSFL algorithm applies the theory of membrane computing and quantum computing to the shuffled frog leaping algorithm, which is an effective discrete optimization algorithm. Then the proposed MQSFL algorithm is used to solve the spectrum allocation problem of cognitive radio systems. By hybridizing the quantum frog colony optimization and membrane computing, the quantum state and observation state of the quantum frogs can be well evolved within the membrane structure. The novel spectrum allocation algorithm can search the global optimal solution within a reasonable computation time. Simulation results for three utility functions of  a cognitive radio system are provided to show that the MQSFL spectrum allocation method is superior to some previous spectrum allocation algorithms based on intelligence computing.

The problem of two order statistics detection schemes for the detection of a spatially distributed target in white Gaussian noise are studied. When the number of strong scattering cells is known, we first show an optimal detector, which requires many processing channels. The structure of such optimal detector is complex. Therefore, a simpler quasi-optimal detector is then introduced. The quasi-optimal detector, called the strong scattering cells’ number dependent order statistics (SND-OS) detector, takes
the form of an average of maximum strong scattering cells with a known number. If the number of strong scattering cells is unknown in real situation, the multi-channel order statistics (MC-OS) detector is used. In each channel, a various number of maximums scattered from target are averaged. Then, the false alarm probability analysis and thresholds sets for each channel are given, following the detection results presented by means of Monte Carlo simulation strategy based on simulated target model and three measured targets. In particular, the theoretical analysis and simulation results highlight that the MC-OS detector can efficiently detect range-spread targets in white Gaussian noise.

This paper proposes a modified centralized shifted Rayleigh filter (MCSRF) algorithm for tracking boost phase of ballistic missile (BM) trajectory with a highly nonlinear dynamical model based on bearings-only. This paper contributes three folds. Firstly, the mathematical model of an MCSRF for multiple passive sensors is derived. Then, minimum entropy based onedimensional optimization search to adaptively adjust the probability of the different filters for real time state estimation is deployed. Finally, the unscented transform (UT) is introduced to resolve the asymmetric state estimation problem. Simulation results show that the proposed algorithm can consecutively track the BM precisely during the boost phase. In comparison with the unscented Kalman filter (UKF) algorithm, the proposed algorithm effectively reduces the tracking position and velocity root mean square (RMS) errors, which will make more sense for early precision interception.

A decoupling-estimation signal parameters via rotarional invariance technique (ESPRIT) method is presented for multi-target localization with unknown mutual coupling in bistatic multiple-input multiple-output (MIMO) radar. Two steps are carried out in this method. The decoupling operation between angle and mutual coupling estimates is realized by choosing the auxiliary elements on both sides of the transmit and receive uniform linear arrays (ULAs). Then the ESPRIT method is resilient against the unknown mutual coupling matrix (MCM) and can be directly utilized to estimate the direction of departure (DOD) and the direction of arrival (DOA). Moreover, the mutual coupling coefficient is estimated by finding the solution of the linear constrained optimization problem. The proposed method allows an efficient DOD and DOA estimates with automatic pairing. Simulation results are presented to verify the effectiveness of the proposed method.

The problem of identification of friend-or-foe aircraft in the actual application condition is addressed. A hybrid algorithm combining fuzzy neutral network with probability factor (FNNP), multi-level fuzzy comprehensive evaluation and the Dempster- Shafer (D-S) theory is proposed. This hybrid algorithm constructs a complete process from generating the fuzzy database to the final identification, realizes the identification of friend-or-foe automatically if the training samples or expert’s experience can be obtained, and reduces the effect of uncertainties in the process of identification. At the same time, the whole algorithm can update the identification result with the augment of observations. The performance of the proposed algorithm is assessed by simulations. Results show that the proposed algorithm can successfully deduce the aircraft’s identity even if the observations have measurement errors.

Satellite observation scheduling plays a significant role in improving the efficiency of satellite observation systems. Although many scheduling algorithms have been proposed, emergency tasks, characterized as importance and urgency (e.g., observation tasks orienting to the earthquake area and military conflict area), have not been taken into account yet. Therefore, it is crucial to investigate the satellite integrated scheduling methods, which focus on meeting the requirements of emergency tasks while maximizing the profit of common tasks. Firstly, a pretreatment approach is proposed, which eliminates conflicts among emergency tasks and allocates all tasks with a potential time-window to related orbits of satellites. Secondly, a mathematical model and an acyclic directed graph model are constructed. Thirdly, a hybrid ant colony optimization method mixed with iteration local search (ACO-ILS) is established to solve the problem. Moreover, to guarantee all solutions satisfying the emergency task requirement constraints, a constraint repair method is presented. Extensive experimental simulations show that the proposed integrated scheduling method is superior to two-phased scheduling methods, the performance of ACO-ILS is greatly improved in both evolution speed and solution quality by iteration local search, and ACO-ILS outperforms both genetic algorithm and simulated annealing algorithm.

The purpose of this paper is to apply inertial technique to string averaging projection method and block-iterative projection method in order to get two accelerated projection algorithms for solving convex feasibility problem. Compared with the existing accelerated methods for solving the problem, the inertial technique employs a parameter sequence and two previous iterations to get the next iteration and hence improves the flexibility of the algorithm. Theoretical asymptotic convergence results are presented
under some suitable conditions. Numerical simulations illustrate that the new methods have better convergence than the general projection methods. The presented algorithms are inspired by the inertial proximal point algorithm for finding zeros of a maximal monotone operator.

The precision of the laser gyro used in tactical missiles is poor because of dithering frequency, actuating by vibration, shock and overload in dynamical environment. This paper introduces the transfer matrix method of the multibody system (MSTMM), establishes the dynamic model of the laser gyro strapdown inertial measure assembly aseismatic system, and analyzes the precision affected by dithering of the laser gyro and shocking of the tactical missile. And the dynamic response of the laser gyro strapdown inertial measure assembly aseismatic system is obtained by simulating the multibody system model. The simulation result indicates a theoretical idea to design the vibration isolation for the laser gyro strapdown inertial measure assembly.

Based on the idea of zeroing the line of sight rate (LOSR), a novel nonlinear differential geometric (DG) law for intercepting the agile target is proposed. In the first part, the DG formulations are utilized to describe the relatively kinematics model of missile and target, and the nonlinear DG guidance (DGG) law is proposed based on the nonlinear control theory to eliminate the influence brought by target. Further, the missile guidance commands are derived to overcome the information loss caused by decoupling condition, the new necessary initial condition is developed to guarantee capture the agile target. Then, the designed nonlinear DGG commands are transformed from an arc-length system to the time domain. A desirable aspect of the designed guidance law is that it does not require rigorous information about target acceleration. Representative numerical results show that the designed guidance law obtain a better performance than the traditional DGG law for agile target.

The tracking and stable control of a typical shipmounted mobile satellite communication system (MSCS) is studied. Unlike the former studies based on simplified single-axis models, a tri-axis nonlinear model including the kinematic and dynamic features of the MSCS is used as the control object. An adaptive robust controller with trajectory planning is designed to deal with large parametric uncertainties and uncertain nonlinearities of the system. A theoretic performance result is given and proved. The designed adaptive robust controller and other two traditional controllers are tested in the comparative simulations under three different situations. The simulation results show the tracking and stable validity of the proposed controller.

This paper investigates the integrated fault detection and diagnosis (FDD) with fault tolerant control (FTC) method of the control system with recoverable faults. Firstly, a quasi-linear parameter-varying (QLPV) model is set up, in which effectiveness factors are modeled as time-varying parameters to quantify actuators and sensors faults. Based on the certainty equivalency principle, replacing the real time states in the nonlinear term of the QLPV model with the estimated states, the parameters and states can be estimated by a two-stage Kalman filtering algorithm. Then, a polynomial eigenstructure assignment (PEA) controller with time-varying parameters and states is designed to guarantee the performance of the system with recoverable faults. Finally, mathematical simulation is performed to validate the solution in a satellite closed-loop attitude control system, and simulation results show that the solution is fast and effective for on-orbit real-time computation.

This paper proposes a particle swarm optimization (PSO) based particle filter (PF) tracking framework, the embedded PSO makes particles move toward the high likelihood area to find the optimal position in the state transition stage, and simultaneously incorporates the newest observations into the proposal distribution in the update stage. In the proposed approach, likelihood measure functions involving multiple features are presented to enhance the performance of model fitting. Furthermore, the multi-feature weights are self-adaptively adjusted by a PSO algorithm throughout the tracking process. There are three main contributions. Firstly, the PSO algorithm is fused into the PF framework, which can efficiently alleviate the particles degeneracy phenomenon. Secondly, an effective convergence criterion for the PSO algorithm is explored, which can avoid particles getting stuck in local minima and maintain a greater particle diversity. Finally, a multi-feature weight self-adjusting strategy is proposed, which can significantly improve the tracking robustness and accuracy. Experiments performed on several challenging public video sequences demonstrate that the proposed tracking approach achieves a considerable performance.

Structure learning of Bayesian networks is a wellresearched but computationally hard task. For learning Bayesian networks, this paper proposes an improved algorithm based on unconstrained optimization and ant colony optimization (U-ACO-B) to solve the drawbacks of the ant colony optimization (ACO-B). In this algorithm, firstly, an unconstrained optimization problem is solved to obtain an undirected skeleton, and then the ACO algorithm is used to orientate the edges, thus returning the final structure. In the experimental part of the paper, we compare the performance of the proposed algorithm with ACO-B algorithm. The experimental results show that our method is effective and greatly enhance convergence speed than ACO-B algorithm.