With the development of micro-satellite technology, traditional monolithic satellites can be replaced by micro-satellite clusters to achieve high flexibility and dynamic reconfiguration capability. For satellite clusters based on the frequency division-code division multiple access (FD-CDMA) communication system, the inter-satellite ranging precision is usually constrained due to the influence of multi-address interference (MAI). The multi-user detection (MUD) is a solution to MAI, which can be divided into two categories: the linear detector (LD) and the non-linear detector (NLD). The general idea of the LD is aiming to make a better decision during the symbol decision process by using the information of all channels. However, it is not beneficial for the signal phase tracking precision. Instead, the principle of the NLD is to rebuild the interference signal and cancel it from the original one, which can improve the ranging performance at the expense of considerable delays. In order to enable simultaneous ranging and communication and reduce multi-node ranging performance degradation, this paper proposes an NLD scheme based on a delay locked loop (DLL), which simplifies the receiver structure and introduces no delay in the decision process. This scheme utilizes the information obtained from the interference channel to reconstruct the interference signal and then cancels it from the original delayed signal. Therefore, the DLL input signal-to-interference ratio (SIR) of the desired channel can be significantly improved. The experimental results show that with the proposed scheme, the standard deviation of the tracking steady error is decreased from 5.59 cm to 3.97 cm for SIR = 5 dB, and 13.53 cm to 5.77 cm for SIR = -5 dB, respectively.

In last few years, guided image fusion algorithms become more and more popular. However, the current algorithms cannot solve the halo artifacts. We propose an image fusion algorithm based on fast weighted guided filter. Firstly, the source images are separated into a series of high and low frequency components. Secondly, three visual features of the source image are extracted to construct a decision graph model. Thirdly, a fast weighted guided filter is raised to optimize the result obtained in the previous step and reduce the time complexity by considering the correlation among neighboring pixels. Finally, the image obtained in the previous step is combined with the weight map to realize the image fusion. The proposed algorithm is applied to multi-focus, visible-infrared and multi-modal image respectively and the final results show that the algorithm effectively solves the halo artifacts of the merged images with higher efficiency, and is better than the traditional method considering subjective visual consequent and objective evaluation.

This paper presents augmented input estimation (AIE) for multiple maneuvering target tracking. Multi-target tracking (MTT) is based on two main parts, data association and estimation. In data association (DA), the best observations are assigned to the considered tracks. In real conditions, the number of observations is more than targets and also locations of observations are often so scattered that the association between targets and observations cannot be done simply. In this case, for general MTT problems with unknown numbers of targets, we present a Markov chain MonteCarlo DA (MCMCDA) algorithm that approximates the optimal Bayesian filter with low complexity in computations. After DA, estimation and tracking should be done. Since in general cases, many targets can have maneuvering motions, then AIE is proposed to cover both the non-maneuvering and maneuvering parts of motion and the maneuver detection procedure is eliminated. This model with an input estimation (IE) approach is a special augmentation in the state space model which considers both the state vector and the unknown input vector as a new augmented state vector. Some comparisons based on the Monte-Carlo simulations are also made to evaluate the performances of the proposed method and other older methods in MTT.

In order to solve the problem of ambiguous acquisition of BOC signals caused by its property of multiple peaks, an unambiguous acquisition algorithm named reconstruction of sub cross-correlation cancellation technique (RSCCT) for BOC(kn, n) signals is proposed. In this paper, the principle of signal decomposition is combined with the traditional acquisition algorithm structure, and then based on the method of reconstructing the correlation function. The method firstly gets the sub-pseudorandom noise (PRN) code by decomposing the local PRN code, then uses BOC(kn, n) and the sub-PRN code cross-correlation to get the sub cross-correlation function. Finally, the correlation peak with a single peak is obtained by reconstructing the sub cross-correlation function so that the ambiguities of BOC acquisition are removed. The simulation shows that RSCCT can completely eliminate the side peaks of BOC (kn, n) group signals while maintaining the narrow correlation of BOC, and its computational complexity is equivalent to sub carrier phase cancellation (SCPC) and autocorrelation side-peak cancellation technique (ASPeCT), and it reduces the computational complexity relative to BPSK-like. For BOC(n, n), the acquisition sensitivity of RSCCT is 3.25 dB, 0.81 dB and 0.25 dB higher than binary phase shift keying (BPSK)-like, SCPC and ASPeCT at the acquisition probability of 90%, respectively. The peak to average power ratio is 1.91, 3.0 and 3.7 times higher than ASPeCT, SCPC and BPSK-like at SNR = – 20 dB, respectively. For BOC(2n, n), the acquisition sensitivity of RSCCT is 5.5 dB, 1.25 dB and 2.69 dB higher than BPSK-like, SCPC and ASPeCT at the acquisition probability of 90%, respectively. The peak to average power ratio is 1.02, 1.68 and 2.12 times higher than ASPeCT, SCPC and BPSK-like at SNR = – 20 dB, respectively.

As a powerful distributed data processing mechanism, MapReduce supports abundant parallel applications that process massive data on computer clusters. To process the massive data efficiently and correctly, a rational design for the MapReduce procedure is desired. An irrational MapReduce procedure can cause great waste of computing resources and even paralyze the exe-cution system. With the wide application of MapReduce, the unavoidable drawback of irrational MapReduce procedures becomes increasingly serious. To solve this problem, a method for verifying the rationality of a MapReduce procedure before executing it on a computer cluster is proposed. This method constructs the rationality criteria for MapReduce, and then studies an automatic approach for modelling MapReduce with an executable model object Petri net (OPN). Finally, the approaches for automatically identifying the rationality criteria by analyzing the consequence of model execution is developed. The results from extensive case studies demonstrate that the proposed method is feasible and effective.

The basic idea of multi-class classification is a disassembly method, which is to decompose a multi-class classification task into several binary classification tasks. In order to improve the accuracy of multi-class classification in the case of insufficient samples, this paper proposes a multi-class classification method combining K-means and multi-task relationship learning (MTRL). The method first uses the split method of One vs. Rest to disassemble the multi-class classification task into binary classification tasks. K-means is used to down sample the dataset of each task, which can prevent over-fitting of the model while reducing training costs. Finally, the sampled dataset is applied to the MTRL, and multiple binary classifiers are trained together. With the help of MTRL, this method can utilize the inter-task association to train the model, and achieve the purpose of improving the classification accuracy of each binary classifier. The effectiveness of the proposed approach is demonstrated by experimental results on the Iris dataset, Wine dataset, Multiple Features dataset, Wireless Indoor Localization dataset and Avila dataset.

Space-time coding radar has been recently proposed and investigated. It is a radar framework which can perform transmit beamforming at the receiver. However, the range resolution decreases when the number of the transmit element increases. A subarray-based space-time coding (sub-STC) radar is explored to alleviate the range resolution reduction. For the proposed radar configuration, an identical waveform is transmitted and it introduces a small time offset in different subarrays. The multi-dimensional ambiguity function of sub-STC radar is defined by considering resolutions in multiple domains including the range, Doppler, angle and probing direction. Analyses on properties of the multi-dimensional ambiguity function of the sub-STC radar with regard to the spatial coverage, resolution performance and low sidelobes are also given. Results reveal that the range re-solution and low sidelobes performance are improved with the proposed approach.

The statistical characterization of radar range cells with the target signals is much more distinct than that of the range cells with noise-only signals. Hence, the quasi-optimal detection principle based on the characteristic parameters of echo signals is adopted to develop a detector of range-spread targets in Gaussian noise. Firstly, the characteristic parameters of the return signals in the entire range profiles of radar are investigated. Secondly, the clustering analysis of the characteristic parameter matrix is discussed to extract the test statistic of echoes. Finally, the probabilities of detection and false alarm of the proposed detector are provided. Theoretical analysis shows that the proposed detector does not need the prior knowledge about the spatial distribution of the target scattering centers in practical scenarios, and it is simple and robust even in low signal-to-noise ratio (low-SNR) scenarios. Monte Carlo (MC) simulations reveal that the detection performance of the proposed detector outperforms the conventional detectors.

The weapons system portfolio selection problem arises at the equipment demonstration stage and deals with the military application requirements. Further, the contribution rate of the system is one of the important indicators to evaluate the role of a system, which can facilitate the weapons system portfolio selection. Therefore, combining the system contribution rate with system portfolio selection is the focus of this study. It also focuses on calculating the contribution rates of multiple equipment systems with various types of capabilities. The contribution rate is measured by establishing a hierarchical multi-criteria value model from three dimensions. Based on the value model, the feasible portfolios are developed under certain cost constraints and the optimal weapons system portfolios are obtained by using the classification optimization selection strategy. Finally, an illustrative example is presented to verify the feasibility of the proposed model.

In a system of systems (SoS), resilience is an important factor in maintaining the functionality, stability, and enhancing the operation effectiveness. From the perspective of resilience, this paper studies the importance of the SoS, and a resilience-based importance measure analysis is conducted to provide suggestions in the design and optimization of the structure of the SoS. In this paper, the components of the SoS are simplified as four kinds of network nodes: sensor, decision point, influencer, and target. In this networked SoS, the number of operation loops is used as the performance indicator, and an approximate algorithm, which is based on eigenvalue of the adjacency matrix, is proposed to calculate the number of operation loops. In order to understand the performance change of the SoS during the attack and defense process in the operations, an integral resilience model is proposed to depict the resilience of the SoS. From different perspectives of enhancing the resilience, different measures, parameters and the corresponding algorithms for the resilience importance of components are proposed. Finally, a case study on an SoS is conducted to verify the validity of the network modelling and the resilience-based importance analysis method.

The earth observation satellites (EOSs) scheduling problem for emergency tasks often presents many challenges. For example, the scheduling calculation should be completed in seconds, the scheduled task rate is supposed to be as high as possible, the disturbance measure of the scheme should be as low as possible, which may lead to the loss of important observation opportunities and data transmission delays. Existing scheduling algorithms are not designed for these requirements. Consequently, we propose a rolling horizon strategy (RHS) based on event triggering as well as a heuristic algorithm based on direct insertion, shifting, backtracking, deletion, and reinsertion (ISBDR). In the RHS, the driven scheduling mode based on the emergency task arrival and control station time window events are designed to transform the long-term, large-scale problem into a short-term, small-scale problem, which can improve the schedulability of the original scheduling scheme and emergency response sensiti-vity. In the ISBDR algorithm, the shifting rule with breadth search capability and backtracking rule with depth search capability are established to realize the rapid adjustment of the original plan and improve the overall benefit of the plan and early completion of emergency tasks. Simultaneously, two heuristic factors, namely the emergency task urgency degree and task conflict degree, are constructed to improve the emergency task scheduling guidance and algorithm efficiency. Finally, we conduct extensive experiments by means of simulations to compare the algorithms based on ISBDR and direct insertion, shifting, deletion, and reinsertion (ISDR). The results demonstrate that the proposed algorithm can improve the timeliness of emergency tasks and scheduling performance, and decrease the disturbance measure of the scheme, therefore, it is more suitable for emergency task scheduling.

It is unpractical to learn the optimal structure of a big Bayesian network (BN) by exhausting the feasible structures, since the number of feasible structures is super exponential on the number of nodes. This paper proposes an approach to layer nodes of a BN by using the conditional independence testing. The parents of a node layer only belong to the layer, or layers who have priority over the layer. When a set of nodes has been layered, the number of feasible structures over the nodes can be remarkably reduced, which makes it possible to learn optimal BN structures for bigger sizes of nodes by accurate algorithms. Integrating the dynamic programming (DP) algorithm with the layering approach, we propose a hybrid algorithm—layered optimal learning (LOL) to learn BN structures. Benefitted by the layering approach, the complexity of the DP algorithm reduces to O(ρ2^n-1) from O(n2^n-1), where ρ < n. Meanwhile, the memory requirements for storing intermediate results are limited to $O(C_{k^\# }^{{{k^\# } \over 2}})$ from $O(C_n^{{n \over 2}} )$, where k^# < n. A case study on learning a standard BN with 50 nodes is conducted. The results demonstrate the superiority of the LOL algorithm, with respect to the Bayesian information criterion (BIC) score criterion, over the hill-climbing, max-min hill-climbing, PC, and three-phrase dependency analysis algorithms.

A state/event fault tree (SEFT) is a modeling technique for describing the causal chains of events leading to failure in software-controlled complex systems. Such systems are ubiquitous in all areas of everyday life, and safety and reliability analyses are increasingly required for these systems. SEFTs combine elements from the traditional fault tree with elements from state-based techniques. In the context of the real-time safety-critical systems, SEFTs do not describe the time properties and important time-dependent system behaviors that can lead to system failures. Further, SEFTs lack the precise semantics required for formally modeling time behaviors. In this paper, we present a qualitative analysis method for SEFTs based on transformation from SEFT to timed automata (TA), and use the model checker UPPAAL to verify system requirements' properties. The combination of SEFT and TA is an important step towards an integrated design and verification process for real-time safety-critical systems. Finally, we present a case study of a powerboat autopilot system to confirm our method is viable and valid after achieving the verification goal step by step.

Large-scale and diverse businesses based on the cloud computing platform bring the heavy network traffic to cloud data centers. However, the unbalanced workload of cloud data center network easily leads to the network congestion, the low resource utilization rate, the long delay, the low reliability, and the low throughput. In order to improve the utilization efficiency and the quality of services (QoS) of cloud system, especially to solve the problem of network congestion, we propose MTSS, a multi-path traffic scheduling mechanism based on software defined networking (SDN). MTSS utilizes the data flow scheduling flexibility of SDN and the multi-path feature of the fat-tree structure to improve the traffic balance of the cloud data center network. A heuristic traffic balancing algorithm is presented for MTSS, which periodically monitors the network link and dynamically adjusts the traffic on the heavy link to achieve programmable data forwarding and load balancing. The experimental results show that MTSS outperforms equal-cost multi-path protocol (ECMP), by effectively reducing the packet loss rate and delay. In addition, MTSS improves the utilization efficiency, the reliability and the throughput rate of the cloud data center network.

This paper investigates the problem of controlling a chasing spacecraft (chaser) to track and rendezvous with an uncontrolled target. Based on the actual situation, the torque-free motion of an axisymmetric prolate rigid body is employed to represent the short-term attitude motion of the tumbling target. By taking advantage of the dual quaternion's compact and efficient description of the general rigid motion, the coupled and integrated model of the 6-degree-of-freedom (6-DOF) relative motion between the chaser and the tumbling target is derived in the chaser's body fixed frame after taking full consideration of coordinate transformation. Based on the logarithm of dual quaternion, a sliding mode control (SMC) law based on the exponential reaching law and the conti-nuous relay function is brought forward to address the problem of synchronization control of the 6-DOF relative motion. Simulation results illustrate the effectiveness of the proposed method.

Asymptotic stability of nonlinear fractional order affine systems with bounded inputs is dealt. The main contribution is to design a new bounded fractional order chattering free sliding mode controller in which the system states converge to the sliding surface at a determined finite time. To eliminate the chattering in the sliding mode and make the input controller bounded, hyperbolic tangent is used for designing the proposed fractional order sliding surface. Finally, the stability of the closed loop system using this bounded sliding mode controller is guaranteed by Lyapunov theory. A comparison with the integer order case is then presented and fractional order nonlinear polynomial systems are also studied as the special case. Finally, simulation results are provided to show the effectiveness of the designed controller.

This paper proposes an optimization algorithm based on a multi-loop control system with a neural network controller, in which the objective function that is used is the control plant of each sub-control system. To obtain the global optimization solution from a control plant that has many local minimum points, a transformation function is presented. On the one hand, this approach changes a complex objective function into a simple function under the condition of an unchanged globally optimal solution, to find the global optimization solution more easily by using a multi-loop control system. On the other hand, a special neural network (in which the node function can be simply positioned locally) that is composed of multiple transformation functions is used as the controller, which reduces the possibility of falling into local minimum points. At the same time, a filled function is presented as a control law; it can jump out of a local minimum point and move to another local minimum point that has a smaller value of the objective function. Finally, 18 simulation examples are provided to show the effectiveness of the proposed method.

Products are often subject to dynamic environmental conditions in field use. When stress transition occurs, products may be exposed to instantaneous shocks that result in shock damages to the products, causing a permanent change of the degradation signals. Meanwhile, under some conditions, instantaneous shocks also lead to stress drift, causing a temporary change of the degradation signals. In this paper, a degradation model is proposed to assess the reliability and predict the residual lifetime of products operating in a dynamic environment considering shock damage and stress drift. The model is established based on a Wiener process which combines a stress-dependent degradation rate function, a shock damage function and a stress drift function in response to the dynamic environment. The shock damage function is established as a linear function of the stress transition start level and the stress level increment. The stress drift function is established as the difference value of a specified function at the stress transition start and end levels. A simulation study is presented to demonstrate the application of the model, and a case study for miniature light bulbs is used to validate the effectiveness of the proposed model.

The fast growth of datacenter networks, in terms of both scale and structural complexity, has led to an increase of network failure and hence brings new challenges to network management systems. As network failure such as node failure is inevitable, how to find fault detection and diagnosis approaches that can effectively restore the network communication function and reduce the loss due to failure has been recognized as an important research problem in both academia and industry. This research focuses on exploring issues of node failure, and presents a proactive fault diagnosis algorithm called heuristic breadth-first detection (HBFD), through dynamically searching the spanning tree, analyzing the dial-test data and choosing a reasonable threshold to locate fault nodes. Both theoretical analysis and simulation results demonstrate that HBFD can diagnose node failures effectively, and take a smaller number of detection and a lower false rate without sacrificing accuracy.

Time-limited dispatching (TLD) analysis of the full authority digital engine control (FADEC) systems is an important part of the aircraft system safety analysis and a necessary task for the certification of commercial aircraft and aeroengines. In the time limited dispatch guidance document ARP5107B, a single-fault Markov model (MM) approach is proposed for TLD analysis. However, ARP5107B also requires that the loss of thrust control (LOTC) rate error calculated by applying the single-fault MM must be less than 5% when performing airworthiness certification. Firstly, the sources of accuracy errors in three kinds of MM are analyzed and specified through a case study of the general FADEC system, and secondly a two-fault MM considering maintenance policy is established through analyzing and calculating the expected repair time when two related faults happen. Finally, a specific FADEC system is given to study on the influence factors of accuracy error in the single-fault MM, and the results show that the accuracy error of the single-fault MM decreases with the increase of short or long prescribed dispatch time, and the range values of short time (ST) and long time (LT) are determined to satisfy the requirement of accuracy error within 5%.