In most literature about joint direction of arrival (DOA) and polarization estimation, the case that sources possess different power levels is seldom discussed. However, this case exists widely in practical applications, especially in passive radar systems. In this paper, we propose a joint DOA and polarization estimation method for unequal power sources based on the reconstructed noise subspace. The invariance property of noise subspace (IPNS) to power of sources has been proved an effective method to estimate DOA of unequal power sources. We develop the IPNS method for joint DOA and polarization estimation based on a dual polarized array. Moreover, we propose an improved IPNS method based on the reconstructed noise subspace, which has higher resolution probability than the IPNS method. It is theoretically proved that the IPNS to power of sources is still valid when the eigenvalues of the noise subspace are changed artificially. Simulation results show that the resolution probability of the proposed method is enhanced compared with the methods based on the IPNS and the polarimetric multiple signal classification (MUSIC) method. Meanwhile, the proposed method has approximately the same estimation accuracy as the IPNS method for the weak source.

In X-ray pulsar-based navigation, strong X-ray background oise leads to a low signal-to-noise ratio (SNR) of the bserved profile, which consequently makes it very difficult to btain an accurate pulse phase that directly determines the navigation recision. This signifies the necessity of denoising of the bserved profile. Considering that the ultimate goal of denoising s to enhance the pulse phase estimation, a profile denoising lgorithm is proposed by fusing the biorthogonal lifting wavelet ransform of the linear phase characteristic with the thresholding echnique. The statistical properties of X-ray background noise fter epoch folding are studied. Then a wavelet-scale dependent hreshold is introduced to overcome correlations between avelet coefficients. Moreover, a modified hyperbola shrinking unction is presented to remove the impulsive oscillations of the bserved profile. The results of numerical simulations and real ata experiments indicate that the proposed method can effectively mprove SNR of the observed profile and pulse phase estimation accuracy, especially in short observation durations. And it also outperforms the Donoho thresholding strategy normally used in combination with the orthogonal discrete wavelet transform.

A deep space multi-file delivery protocol (DSMDP) based on LT codes is proposed to reduce the influence of long delay and a high bit error rate (BER) in deep space communication. The protocol increases sending redundancy by LT codes to improve the success rate of file delivery, and adopts different protective strategies for different situations of packet loss. At the same time, the multi-file united delivery strategy is adopted to make full use of the retransmission time to reduce the end-toend transmission delay. Furthermore, the protocol determines the quantity of encoded packets according to the feedback for controlling redundancy. The simulation results show that the proposed protocol can significantly reduce the transmission delay of files, which would be effectively suitable for deep space communication environment of high BER and long delay.

The influence of cells groupings factor to the performance of the cells groupings time-shift pilot scheme is researched for the multiple cells large scale antennas systems (LSAS). The former researches have confirmed that the cells groupings time-shift pilots scheme is effective to reduce inter-cell interference, especially pilot contamination, which results from the pilot reuse in adjacent cells. However, they have not specified reasonable cells groupings factor, which plays a critical role in the general performance of the LSAS. Therefore, this problem is researched in details. The time for reverse-link data transmission will be compressed, when the groupings factor surpasses a certain range. Thus it is not always beneficial to increase the cells groupings factor without limitation. Furthermore,a reasonable cells groupings factor is deduced from the perspective of optimization to enhance the system performance. Simulations verify the proposed cell grouping factor.

 The Q-ary low-density parity-check (LDPC) coded high order partial response continuous phase modulation (PR-CPM) with double iterative loops is investigated. This scheme shows significant improvements in power and bandwidth efficiency, but at the expense of long iterative decoding delay and computational complexity induced by the improper match between the demodulator and the decoder. To address this issue, the convergence behavior of Q-ary LDPC coded CPM is investigated for the Q=2 and Q>2 cases, and an optimized design method based on the extrinsic information transfer chart is proposed to improve the systematic iterative efficiency. Simulation results demonstrate that the proposed method can achieve a perfect tradeoff between iterative decoding delay and bit error rate performance to satisfy real-time applications.

A novel collaborative beamforming algorithm is proposed in a wireless communication system with multiple transmitters and one receiver. All transmitters take part in the collaboration and the weighted message is transmitted simultaneously. In order to maximize the beamforming gain, the transmitters use one bit feedback information to adjust the phase offset. It tracks the direction in which the signal strength at the receiver can increase. The directional search and perturbation theory is used to achieve the phase alignment. The feasibility of the proposed algorithm is proved both experimentally and theoretically. Simulation results show that the proposed algorithm can improve the convergent speed of the phase alignment.

 A direction of arrival (DOA) estimation algorithm is proposed using the concept of sparse representation. In particular, a new sparse signal representation model called the smoothed covariance vector (SCV) is established, which is constructed using the lower left diagonals of the covariance matrix. DOA estimation is then achieved from the SCV by sparse recovering, where two distinguished error limit estimation methods of the constrained optimization are proposed to make the algorithms more robust. The algorithm shows robust performance on DOA estimation in a uniform array, especially for coherent signals. Furthermore, it significantly reduces the computational load compared with those algorithms based on multiple measurement vectors (MMVs). Simulation results validate the effectiveness and efficiency of the proposed algorithm.

An iterative physical optics (IPO) model is proposed to solve extra large scale electric electromagnetic (EM) scattering from randomly rough surfaces. In order to accelerate the convergence of the IPO model, the forward-backward methodology and its modification with underrelaxation iteration are developed to simulate the rough surface scattering; the local iteration methodology and the fast far field approximation (FaFFA) in the matrix-vector product are proposed to reduce greatly the computational complexity. These techniques make Monte Carlo simulations possible. Thus, the average Doppler spectra of backscattered signals obtained from the simulations are compared for different incident angles and sea states. In particular, the simulations show a broadening of the Doppler spectra for a more complicated sea state at a low grazing angle (LGA).

This paper addresses the problem of service composition in military organization cloud cooperation (MOCC). Military service providers (MSP) cooperate together to provide military resources for military service users (MSU). A group of atom services, each of which has its level of quality of service (QoS), can be combined together into a certain structure to form a composite service. Since there are a large number of atom services having the same function, the atom service is selected to participate in the composite service so as to fulfill users’ will. In this paper a method based on discrete particle swarm optimization (DPSO) is proposed to tackle this problem. The method aims at selecting atom services from service repositories to constitute the composite service, satisfying the MSU’s requirement on QoS. Since the QoS criteria include location-aware criteria and location-independent criteria, this method aims to get the composite service with the highest location-aware criteria and the best-match location-independent criteria. Simulations show that the DPSO has a better performance compared with the standard particle swarm optimization (PSO) and genetic algorithm (GA).

One important mission of the strategic defense is to develop an integrated, layered ballistic missile defense system (BMDS). Considering the problem of assigning limited defense weapons to incoming ballistic missiles, we illustrate how defense weapons, ballistic missiles, kill probability and effectiveness of defense (ED) are interrelated and how to understand this relationship for achieving the best allocation plan. Motivated by the queueing theory, in which the available resources are not sufficient to satisfy the demands placed upon them at all times, the layered deployed defense weapon is modeled as a queueing system to shoot Poisson arrived targets. Simultaneously, examples, of optimum intercepts allocation problems under different constraints are presented. The four theorems determine the allocation rules of intercepts to targets that maximize ED or minimize the cost to achieve a required ED.

For low-speed underwater vehicles, the ocean currents has a great influence on them, and the changes in ocean currents is complex and continuous, thus whose impact must be taken into consideration in the path planning. There are still lack of authoritative indicator and method for the cooperating path planning. The calculation of the voyage time is a difficult problem in the time-varying ocean, for the existing methods of the cooperating path planning, the computation time will increase exponentially as the autonomous underwater vehicle (AUV) counts increase, rendering them unfeasible. A collaborative path planning method is presented for multi-AUV under the influence of time-varying ocean currents based on the dynamic programming algorithm. Each AUV cooperates with the one who has the longest estimated time of sailing, enabling the arrays of AUV to get their common goal in the shortest time with minimum time difference. At the same time, they could avoid the obstacles along the way to the target. Simulation results show that the proposed method has a promising applicability.

Multiple earth observing satellites need to communicate with each other to observe plenty of targets on the Earth together. The factors, such as external interference, result in satellite information interaction delays, which is unable to ensure the integrity and timeliness of the information on decision making for satellites. And the optimization of the planning result is affected. Therefore, the effect of communication delay is considered during the multi-satellite coordinating process. For this problem, firstly, a distributed cooperative optimization problem for multiple satellites in the delayed communication environment is formulized. Secondly, based on both the analysis of the temporal sequence of tasks in a single satellite and the dynamically decoupled characteristics of the multi-satellite system, the environment information of multi-satellite distributed cooperative optimization is constructed on the basis of the directed acyclic graph (DAG). Then, both a cooperative optimization decision making framework and a model are built according to the decentralized partial observable Markov decision process (DEC-POMDP). After that, a satellite coordinating strategy aimed at different conditions of communication delay is mainly analyzed, and a unified processing strategy on communication delay is designed. An approximate cooperative optimization algorithm based on simulated annealing is proposed. Finally, the effectiveness and robustness of the method presented in this paper are verified via the simulation.

Due to the importance and role of systems engineering in space mission developments, optimization of Omid’s systems engineering as a milestone to its current and future generations is focused. In this regard systems engineering management organization as the basis of optimization work flow in the conceptual design phase is proposed for improvement. To improve the systems engineering management, an agile enhanced organization chart is developed that defines various system duties. This is a type of concurrent engineering approach that promotes direct communication and data interchange between the team members. Due to the importance of decision making in the conceptual design phase, two design matrices are constructed that portray merits of various design options in terms of improved satellite life as well as specific choices of remote sensing capability for the Omid second generation (Omid-2). Conceptual design optimization is explored considering several structural objectives as well as optimal solar energy absorption utilizing a multiple criteria decision making approach. The Eigenvector method is utilized to formulate the objective function via expert judgment. This approach is robust with respect to designer probable miss-judgment. The optimized version of Omid-2 turned out to be a passive Z-axis spin stabilized satellite made of hexagonal honeycomb configuration with carbon-epoxy side panels and Aluminum bottom plate.

 The control problem for single-input single-output (SISO) systems in the presence of mixed uncertainties, both stochastic and deterministic uncertainties, is considered. The stochastic uncertainties are modeled as exogenous noises, while the deterministic uncertainties are time invariant and appear as the unknown parameters which lie in a bounded interval. Based on a subdivision for the continuous interval, a robust adaptive controller is designed. The controller can not only realize the system output to track the desired output, but also learn a more accurate interval which contains the true value of the unknown parameter with a learning error given in advance. An example is given finally to demonstrate the effectiveness of the proposed method.

In this paper, a novel fuzzy sliding mode control (FSMC) guidance law with terminal constraints of miss distance, impact angle and acceleration is presented for a constant speed missile against the stationary or slowly moving target. The proposed guidance law combines the sliding mode control algorithm with a fuzzy logic control scheme for the lag-free system and the first-order lag system. Through using Lyapunov stability theory, we prove the sliding surface converges to zero in finite time. Furthermore, considering the uncertain information and system disturbances, the guidance gains are on-line optimized by fuzzy logic technique. Numerical simulations are performed to demonstrate the performance of the FSMC guidance law and the results illustrate the validity and effectiveness of the proposed guidance law.

In the process of image transmission, the famous JPEG and JPEG-2000 compression methods need more transmission time as it is difficult for them to compress the image with a low compression rate. Recently the compressed sensing (CS) theory was proposed, which has earned great concern as it can compress an image with a low compression rate, meanwhile the original image can be perfectly reconstructed from only a few compressed data. The CS theory is used to transmit the high resolution astronomical image and build the simulation environment where there is communication between the satellite and the Earth. Number experimental results show that the CS theory can effectively reduce the image transmission and reconstruction time. Even with a very low compression rate, it still can recover a higher quality astronomical image than JPEG and JPEG-2000 compression methods.

This paper focuses on the recognition rate comparison for competing recognition algorithms, which is a common problem of many pattern recognition research areas. The paper firstly reviews some traditional recognition rate comparison procedures and discusses their limitations. A new method, the posterior probability calculation (PPC) procedure is then proposed based on Bayesian technique. The paper analyzes the basic principle, process steps and computational complexity of the PPC procedure. In the Bayesian view, the posterior probability represents the credible degree (equal to confidence level) of the comparison results. The posterior probability of correctly selecting or sorting the competing recognition algorithms is derived, and the minimum sample size requirement is also pre-estimated and given out by the form of tables. To further illustrate how to use our method, the PPC procedure is used to prove the rationality of the experiential choice in one application and then to calculate the confidence level with the fixed-size datasets in another application. These applications reveal the superiority of the PPC procedure, and the discussions about the stopping rule further explain the underlying statistical causes. Finally we conclude that the PPC procedure achieves all the expected functions and be superior to the traditional methods.

Mobile cloud computing (MCC) combines mobile Internet and cloud computing to improve the performance of mobile applications. However, MCC faces the problem of energy efficiency because of randomly varying channels. A scheduling algorithm is proposed by introducing the Lyapunov optimization, which can dynamically choose users to transmit data based on queue backlog and channel statistics. The Lyapunov analysis shows that the proposed scheduling algorithm can make a tradeoff between queue backlog and energy consumption in the channel-aware mobile cloud computing system. The simulation results verify the effectiveness of the proposed algorithm.

 View synthesis is an important building block in three dimension (3D) video processing and communications. Based on one or several views, view synthesis creates other views for the purpose of view prediction (for compression) or view rendering (for multi-view-display). The quality of view synthesis depends on how one fills the occlusion area as well as how the pixels are created. Consequently, luminance adjustment and hole filling are two key issues in view synthesis. In this paper, two views are used to produce an arbitrary virtual synthesized view. One view is merged into another view using a local luminance adjustment method, based on local neighborhood region for the calculation of adjustment coefficient. Moreover, a maximum neighborhood spreading strength hole filling method is presented to deal with the micro texture structure when the hole is being filled. For each pixel at the hole boundary, its neighborhood pixels with the maximum spreading strength direction are selected as candidates; and among them, the pixel with the maximum spreading strength is used to fill the hole from boundary to center. If there still exist disocclusion pixels after once scan, the filling process is repeated until all hole pixels are filled. Simulation results show that the proposed method is efficient, robust and achieves high performance in subjection and objection.

For the deficiency that the traditional single forecast methods could not forecast electronic equipment states, a combined forecast method based on the hidden Markov model (HMM) and least square support vector machine (LS-SVM) is presented. The multi-agent genetic algorithm (MAGA) is used to estimate parameters of HMM to overcome the problem that the Baum-Welch algorithm is easy to fall into local optimal solution. The state condition probability is introduced into the HMM modeling process to reduce the effect of uncertain factors. MAGA is used to estimate parameters of LS-SVM. Moreover, pruning algorithms are used to estimate parameters to get the sparse approximation of LS-SVM so as to increase the ranging performance. On the basis of these, the combined forecast model of electronic equipment states is established. The example results show the superiority of the combined forecast model in terms of forecast precision, calculation speed and stability.