The root multiple signal classification (root-MUSIC) algorithm is one of the most important techniques for direction of arrival (DOA) estimation. Using a uniform linear array (ULA) composed of M sensors, this method usually estimates Lsignal DOAs by finding roots that lie closest to the unit circle of a (2M−1)-order polynomial, where L

In cooperative localization with sparse communication networks, an agent maybe only receives part of locating messages from the others. It is difficult for the receiver to utilize the part instead of global knowledge. Under the extended Kalman filtering, the utilization of the locating message is maximized by two aspects: the locating message generating and multi-locating messages fusing. For the former, the covariance upper-bound technique, by introducing amplification coefficients, is employed to remove the dependency of locating messages on the global knowledge. For the latter, an optimization model is setup; the covariance matrix determinant of the receiver’s state estimate, expressed as a function of the amplification coefficients, is selected as the optimization criterion, under linear constraints on the amplification coefficient characteristics and the communication connectivity. Using the optimization solution, the local optimal state of the receiver
agent is obtained by the weighting fusion. Simulation with seven agents is shown to evaluate the effectiveness of the proposed algorithm.

A quality of service (QoS) guaranteed cross-layer resource allocation algorithm with physical layer, medium access control (MAC) layer and call admission control (CAC) considered simultaneously is proposed for the full IP orthogonal frequency division multiple access (OFDMA) communication system, which can ensure the quality of multimedia services in full IP networks. The algorithm converts the physical layer resources such as subcarriers, transmission power, and the QoS metrics into equivalent bandwidth which can be distributed by the base station in all three layers. By this means, the QoS requirements in terms of bit error rate (BER), transmission delay and dropping probability can be guaranteed by the cross-layer optimal equivalent bandwidth allocation. The numerical results show that the proposed algorithm has higher spectrum efficiency compared to the existing systems.

This paper proposes a new signal noise level estimation approach by local regions. The estimated noise variance is applied as the threshold for an improved empirical mode decomposition (EMD) based signal denoising method. The proposed estimation method can effectively extract the candidate regions for the noise
level estimation by measuring the correlation coefficient between noisy signal and a Gaussian filtered signal. For the improved EMD based method, the situation of decomposed intrinsic mode function (IMFs) which contains noise and signal simultaneously are taken into account. Experimental results from two simulated signals and an X-ray pulsar signal demonstrate that the proposed method can achieve better performance than the conventional EMD and wavelet transform (WT) based denoising methods.

Fountain codes are considered to be a promising coding technique in underwater acoustic communication (UAC) which is challenged with the unique propagation features of the underwater acoustic channel and the harsh marine environment. And Luby transform (LT) codes are the first codes fully realizing the digital fountain concept. However, in conventional LT encoding/decoding algorithms, due to the imperfect coverage (IC) of input symbols and short cycles in the generator matrix, stopping sets would occur and terminate the decoding. Thus, the recovery probability is reduced, high coding overhead is required and decoding delay is increased.
These issues would be disadvantages while applying LT codes in underwater acoustic communication. Aimed at solving those issues, novel encoding/decoding algorithms are proposed. First, a doping and non-uniform selecting (DNS) encoding algorithm is proposed to solve the IC and the generation of short cycles problems. And this can reduce the probability of stopping sets occur during decoding. Second, a hybrid on the fly Gaussian elimination and belief propagation (OFG-BP) decoding algorithm is designed to reduce the decoding delay and efficiently utilize the information of stopping sets. Comparisons via Monte Carlo simulation confirm that the proposed schemes could achieve better overall decoding performances in comparison with conventional schemes.

In order to apply compressive sensing in wireless sensor network, inside the nodes cluster classified by the spatial correlation, we propose that a cluster head adopts free space optical communication with space division multiple access, and a sensor node uses a modulating retro-reflector for communication. Thus while a random sampling matrix is used to guide the establishment of links between head cluster and sensor nodes, the random linear projection is accomplished. To establish multiple links at the same time, an optical space division multiple access antenna is designed. It works in fixed beams switching mode and consists of optic lens with a large field of view (FOV), fiber array on the focal plane which is used to realize virtual channels segmentation, direction of arrival sensor, optical matrix switch and controller. Based on the angles of nodes’ laser beams, by dynamically changing the route, optical matrix switch actualizes the multi-beam full duplex tracking receiving and transmission. Due to the structure of fiber array, there will be several fade zones both in the focal plane and in lens’ FOV. In order to lower the impact of fade zones and harmonize multi-beam, a fiber array adjustment is designed. By theoretical, simulated and experimental study, the antenna’s qualitative feasibility is validated.

An imaging algorithm based on compressed sensing (CS) for the multi-ship motion target is presented. In order to reduce the quantity of data transmission in searching the ships on a large sea area, both range and azimuth of the moving ship targets are converted into sparse representation under certain signal basis. The signal reconstruction algorithm based on CS at a distant calculation station, and the Keystone and fractional Fourier transform (FRFT) algorithm are used to compensate range migration and obtain Doppler frequency. When the sea ships satisfy the sparsity, the algorithm can obtain higher resolu- tion in both range and azimuth than the conventional imaging algorithm. Some simulations are performed to verify the reliability and stability.

Based on the cognitive radar concept and the ba- sic connotation of cognitive skywave over-the-horizon radar (SWOTHR), the system structure and information processing mechanism about cognitive SWOTHR are researched. Among them, the hybrid network system architecture which is the distributed conguration combining with the centralized cognition and its soft/hardware framework with the sense-detection integration are proposed, and the information processing frame based on the lens principle and its information processing ow with receive-transmit joint adaption are designed, which build and parse the work law for cognition and its self feedback adjustment with the lens focus model and ve stages information processing sequence. After that, the system simulation and the performance analysis and comparison are provided, which initially proves the rationality and advantages of the proposed ideas. Finally, four important development ideas of future SWOTHR toward “high frequency intelligence information pro- cessing system” are discussed, which are scene information fusion, dynamic recon gurable system, hierarchical and modular design, and sustainable development. Then the conclusion that the cognitive SWOTHR can cause the performance improve- ment is gotten.

As synthetic aperture radar (SAR) has been widely used nearly in every field, SAR image de-noising became a very important research field. A new SAR image de-noising method based on texture strength and weighted nuclear norm minimization (WNNM) is proposed. To implement blind de-noising, the accurate estimation of noise variance is very important. So far, it is still a challenge to estimate SAR image noise level accurately because of the rich texture. Principal component analysis (PCA) and the low rank patches selected by image texture strength are used to estimate the noise level. With the help of noise level, WNNM can be expected to SAR image de-noising. Experimental results show that the proposed method outperforms many excellent de-noising algorithms such as Bayes least squares-Gaussian scale mixtures (BLS-GSM) method, non-local means (NLM) filtering in terms of both quantitative measure and visual perception quality.

For multi-channel synthetic aperture radar (SAR) systems, since the minimum antenna area constraint is eliminated, wide swath and high resolution SAR image can be achieved. However, the unavoidable array errors, consisting of channel gain-phase mismatch and position uncertainty, significantly degrade the performance of such systems. An iteration-free method is proposed to simultaneously estimate position and gain-phase errors. In our research, the steering vectors corresponding to a pair of Doppler bins within the same range bin are studied in terms of their rotational relationships. The method is based on the fact that the rotational matrix only depends on the position errors and the frequency spacing between the paired Doppler bins but is independent of gain-phase error. Upon combining the projection matrices corresponding to the paired Doppler bins, the position errors are directly obtained in terms of extracting the rotational matrix in a least squares framework. The proposed method, when used in conjunction with the self-calibration algorithm, performs stably as well as has less computational load, compared with the conventional methods. Simulations reveal that the proposed method behaves better than the conventional methods even when the signal-to-noise ratio (SNR) is low.

Electromagnetic detection satellite (EDS) is a type of Earth observation satellite (EOS). Satellites observation and data down-link scheduling plays a significant role in improving the efficiency of satellite observation systems. However, the current works mainly focus on the scheduling of imaging satellites, little work focuses on the scheduling of EDSes for its specific requirements. And current works mainly schedule satellite resources and data down-link resources separately, not considering them in a globally optimal perspective. The EDSes and data down-link resources are scheduled in an integrated process and the scheduling result is searched globally. Considering the specific constraints of EDS, a coordinate scheduling model for EDS observation tasks and data transmission jobs is established and an algorithm based on the genetic algorithm is proposed. Furthermore, the convergence of our algorithm is proved. To deal with some specific constraints, a solution repairing algorithm of polynomial computing time is designed. Finally, some experiments are conducted to validate the correctness and practicability of our scheduling algorithms.

According to the previous achievement, the task assignment under the constraint of timing continuity for a cooperative air combat is studied. An extensive task assignment scenario with the background of the cooperative air combat is proposed. The utility and time of executing a task as well as the continuous combat abi- lity are defined. The concept of the matching method of weapon and target is modified based on the analysis of the air combat scenario. The constraint framework is also redefined according to a new objective function. The constraints of timing and continuity are formulated with a new method, at the same time, the task assignment and integer programming models of the cooperative combat are established. Finally, the assignment problem is solved using the integrated linear programming software and the simulation shows that it is feasible to apply this modified model in the cooperative air combat for tasks cooperation and it is also efficient to optimize the resource assignment.

Modeling and reasoning on domain contexts play a key role for adding intelligence to communication services, and the approach of capability-based requirement engineering ensures the scientificity and accuracy of requirement elicita- tion. This paper presents a capability-based context ontology modeling approach for command and control, communication, computer, intelligence, surveillance and reconnaissance (C4ISR) communication. Primarily, a capability-based C4ISR communica- tion meta-concept model and a C4ISR communication context meta-ontology are constructed. The context ontology is established under the constraints of the C4ISR communication context meta-ontology, and furthermore, algorithms are proposed to support context reasoning based on description logic. A case study is presented to demonstrate applicability of the proposed method.service intelligentizing, C4ISR communication, context ontology, context reasoning.

In order to overcome the shortcoming of the classical Hungarian algorithm that it can only solve the problems where the total cost is the sum of that of each job, an improved Hungarian algorithm is proposed and used to solve the assignment problem of serial-parallel systems. First of all, by replacing parallel jobs with virtual jobs, the proposed algorithm converts the serial-parallel system into a pure serial system, where the classical Hungarian algorithm can be used to generate a temporal assignment plan via optimization. Afterwards, the assignment plan is validated by checking whether the virtual jobs can be realized by real jobs through local searching. If the assignment plan is not valid, the converted system will be adapted by adjusting the parameters of virtual jobs, and then be optimized again. Through iterative searching, the valid optimal assignment plan can eventually be obtained. To evaluate the proposed algorithm, the valid optimal assignment plan is applied to labor allocation of a manufacturing system which is a typical serial-parallel system.

In the evaluation of some simulation systems, only small samples data are gotten due to the limited conditions. In allusion to the evaluation problem of small sample data, an interval estimation approach with the improved grey confidence degree is proposed. On the basis of the definition of grey distance, three kinds of definition of the grey weight for every sample element in grey estimated value are put forward, and then the improved grey confidence degree is designed. In accordance with the new concept, the grey interval estimation for small sample data is deduced. Furthermore, the bootstrap method is applied for more accurate grey confidence interval. Through resampling of the bootstrap, numerous small samples with the corresponding confidence intervals can be obtained. Then the final confidence interval is calculated from the union of these grey confidence intervals. In the end, the simulation system evaluation using the proposed method is conducted. The simulation results show that the reasonable confidence interval is acquired, which demonstrates the feasibility and effectiveness of the proposed method.

The problem of the robust fault detection filter design for time-varying delays switched systems is considered in the frame- work of mixed H-/H∞. Firstly, the weighted H∞ performance index is utilized as the robustness performance, and the H_ index is used as the sensitivity performance for obtaining the robust fault detection filter. Then a novel multiple Lyapunov-Krasovskii function is proposed for deriving sufficient existence conditions of the robust fault detection filter based on the average dwell time technique. By introducing slack matrix variable, the coupling between the Lyapunov matrix and system matrix is removed, and the con- servatism of results is reduced. Based on the robust fault detection filter, residual is generated and evaluated for detecting faults. In addition, the results of this paper are dependent on time delays, and represented in the form of linear matrix inequalities. Finally, the simulation example verifies the effectiveness of the proposed method.

The random step maneuver with uniformly distri- buted starting times has the disadvantage that it cannot focus the starting time on the more ef ciency time. It decreases the penetration probability. To resolve this problem, a random step penetration algorithm with normal distribution starting time is proposed. Using the shaping lters and adjoint system method, the miss distance with different starting times can be acquired. According to the penetration standard, the time window ensuring successful penetration can be calculated and it is used as the 3σ bound of the normally distributed random maneuver. Simulation results indicate that the normally distributed random maneuver has higher penetration probability than the uniformly distributed random maneuver.

To solve the flight safety problem caused by nonlin- ear instabilities (category II pilot induced oscillations, PIOs) of the closed-loop pilot-vehicle system with rate-limiting actuator, the anti- windup (AW) compensation method to avoid category II PIOs is investigated. Firstly, the AW compensation method originally used for controlling input magnitude limited system is introduced, then this method is extended for controlling input rate-limiting system through a circle criterion theorem. Secondly, the establishment of the AW compensator is transformed into the solving of linear matrix inequalities. Finally, an AW compensator establishment algorithm for the closed-loop pilot-vehicle system with the rate-limiting actuator is obtained. The effectiveness of the AW compensation method to avoid category II PIOs is validated by time-domain simulations, and compared with rate-limited feedback (RLF) command rate compensation method. The results show that the AW compensa- tion method can effectively suppress category II PIOs and maintain the nominal performance when the closed-loop pilot-vehicle sys- tem is normal. Unlike the command rate compensator which works upon system uninterruptedly, the AW compensation method affects the closed-loop pilot-vehicle system only when the rate-limiting of actuator is activated, so it is a novel PIO avoidance method.

Accelerating the convergence speed and avoiding the local optimal solution are two main goals of particle swarm optimization (PSO). The very basic PSO model and some variants of PSO do not consider the enhancement of the explorative capability of each particle. Thus these methods have a slow convergence speed and may trap into a local optimal solution. To enhance the explorative capability of particles, a scheme called explorative capability enhancement in PSO (ECE-PSO) is proposed by introducing some virtual particles in random directions with random amplitude. The linearly decreasing method related to the maximum iteration and the nonlinearly decreasing method related to the tness value of the globally best particle are employed to produce virtual particles. The above two methods are thoroughly compared with four representative advanced PSO variants on eight unimodal and multimodal benchmark problems. Experimen- tal results indicate that the convergence speed and solution quality of ECE-PSO outperform the state-of-the-art PSO variants.

A discrete differential evolution algorithm combined with the branch and bound method is developed to solve the in- teger linear bilevel programming problems, in which both upper level and lower level variables are forced to be integer. An integer coding for upper level variables is adopted, and then a discrete differential evolution algorithm with an improved feasibility-based comparison is developed to directly explore the integer solution at the upper level. For a given upper level integer variable, the lower level integer programming problem is solved by the existing branch and bound algorithm to obtain the optimal integer solution at the lower level. In the same framework of the algorithm, two other constraint handling methods, i.e. the penalty function method and the feasibility-based comparison method are also tested. The experimental results demonstrate that the discrete differential evolution algorithm with different constraint handling methods is effective in finding the global optimal integer solutions, but the improved constraint handling method performs better than two compared constraint handling methods.

The problem of recognizing natural scenes, such as water, smoke, fire, wind-blown vegetation and a flock of flying birds, is considered. These scenes exhibit the characteristic dynamic pattern, but have stochastic extent. They are referred to as dynamic texture (DT). In reality, the diversity of DTs on different viewpoints and scales are very common, which also bring great difficulty to recognize DTs. In the previous studies, due to no considering of the deformable and transient nature of elements in DT, the motion estimation method is based on brightness constancy assumption, which seem inappropriate for aggregate and complex motions. A novel motion model based on relative motion in the neighborhood of two-dimensional motion fields is proposed. The estimation of non-rigid motion of DTs is based on the continuity equation, and then the local vector difference (LVD) is proposed to characterize DT local relative motion. Spatiotemporal statistics of the LVDs is used as the representation of DT sequences. Excellent performances of classifying all DTs in UCLA database demonstrate the capability of the proposed method in describing DT.

In the open network environment, malicious attacks to the trust model have become increasingly serious. Compared with single node attacks, collusion attacks do more harm to the trust model. To solve this problem, a collusion detector based on the G- N algorithm for the trust evaluation model is proposed in the open Internet environment. By analyzing the behavioral characteristics of collusion groups, the concept of flatting is defined and the G-N community mining algorithm is used to divide suspicious communities. On this basis, a collusion community detector method is proposed based on the breaking strength of suspicious communities. Simulation results show that the model has high recognition accuracy in identifying collusion nodes, so as to effectively defend against malicious attacks of collusion nodes.

This paper considers the Bayesian and expected Bayesian (E-Bayesian) estimations of the parameter and relia- bility function for competing risk model from Gompertz distribution under Type-I progressively hybrid censoring scheme (PHCS). The estimations are obtained based on Gamma conjugate prior for the parameter under squared error (SE) and Linex loss functions. The simulation results are provided for the comparison purpose and one data set is analyzed.