Impressive advances in space technology are enabling complex missions, with potentially significant and long term impacts on human life and activities. In the vision of future space exploration, communication links among planets, satellites, spacecrafts and crewed vehicles will be designed according to a new paradigm, known as the disruption tolerant networking. In this scenario, space channel peculiarities impose a massive reengineering of many of the protocols usually adopted in terrestrial networks; among them, security solutions are to be deeply reviewed, and tailored to the specific space requirements. Security is to be provided not only to the payload data exchanged on the network, but also to the telecommands sent to a spacecraft, along possibly differentiated paths. Starting from the secure space telecommand design developed by the Consultative Committee for Space Data Systems as a response to agency-based requirements, an adaptive link layer security architecture is proposed to address some of the challenges for future space networks. Based on the analysis of the communication environment and the error diffusion properties of the authentication algorithms, a suitable mechanism is proposed to classify frame retransmission requests on the basis of the originating event (error or security attack) and reduce the impact of security operations. An adaptive algorithm to optimize the space control protocol, based on estimates of the time varying space channel, is also presented. The simulation results clearly demonstrate that the proposed architecture is feasible and
efficient, especially when facing malicious attacks against frame transmission.

Recoverability of block-sparse signals by convex relaxation methods is considered for the underdetermined linear model. In previous works, some explicit but pessimistic recoverability results which were associated with the dictionary were presented. This paper shows the recoverability of block-sparse signals are associated with the block structure when a random dictionary is given. Several probability inequalities are obtained to show how the recoverability changes along with the block structure parameters, such as the number of nonzero blocks, the block length, the dimension of the measurements and the dimension of the blocksparse representation signal. Also, this paper concludes that if the block-sparse structure can be considered, the recoverability of the signals will be improved. Numerical examples are given to illustrate the availability of the presented theoretical results.

To improve the low tracking precision caused by lagged filter gain or imprecise state noise when the target highly maneuvers, a modified unscented Kalman filter algorithm based on the improved filter gain and adaptive scale factor of state noise is presented. In every filter process, the estimated scale factor is used to update the state noise covariance Qk, and the improved filter gain is obtained in the filter process of unscented Kalman filter (UKF) via predicted variance P_k|k−1, which is similar to the standard Kalman filter. Simulation results show that the proposed algorithm provides better accuracy and ability to adapt to the highly maneuvering target compared with the standard UKF.

When calculating electromagnetic scattering using method of moments (MoM), integral of the singular term has a significant influence on the results. This paper transforms the singular surface integral to the contour integral. The integrand is expanded to Taylor series and the integral results in a closed form. The cut-off error is analyzed to show that the series converges fast and only about 2 terms can agree well with the accurate result. The comparison of the perfect electric conductive (PEC) sphere’s bi-static radar cross section (RCS) using MoM and the accurate method validates the feasibility in manipulating the singularity. The error due to the facet size and the cut-off terms of the series are analyzed in examples.

Matrix inversion is a critical part in communication, signal processing and electromagnetic system. A flexible and scalable very long instruction word (VLIW) processor with clustered architecture is proposed for matrix inversion. A global register file (RF) is used to connect all the clusters. Two nearby clusters share a local register file. The instruction sets are also designed for the VLIW processor. Experimental results show that the proposed VLIW architecture takes only 45 latency to invert a 4 × 4 matrix when running at 150 MHz. The proposed design is roughly five times faster than the DSP solution in processing speed.

An improved algorithm for multi-polarization reconstruction from compact polarimetry (CP) is proposed. According to two fundamental assumptions in compact polarimetric reconstruction, two improvements are proposed. Firstly, the four-component model-based decomposition algorithm is modified with a new volume
scattering model. The decomposed helix scattering component is then used to deal with the non-reflection symmetry condition in compact polarimetric measurements. Using the decomposed power and considering the scattering mechanism of each component, an average relationship between co-polarized and
cross-polarized channels is developed over the original polarization state extrapolation model. E-SAR polarimetric data acquired over the Oberpfaffenhofen area and JPL/AIRSAR polarimetric data acquired over San Francisco are used for verification, and good reconstruction results are obtained, demonstrating the effectiveness of the proposed algorithm.

Angular glint is a significant electromagnetic (EM) scattering signature of extended radar targets. Based on the adaptive cross approximation (ACA) algorithm accelerated method of moments (MoM) and the plane incident wave assumption, the narrowband, wideband and newly developed high-resolution range profile
(HRRP) based angular glint calculation formulations are derived and applied to arbitrarily shaped three-dimensional (3D) perfectly electrically conducting (PEC) objects. In addition, the near-field angular glint is emphasized, which is of great importance for radarseeker applications. Furthermore, with the HRRP based angular glint, an approach to rigorously determine range resolution cells which own relatively smaller angular glint is provided. Numerical results are presented with new findings to demonstrate the usefulness of the developed formulations.

The azimuth resolution improvement problem is solved via a coherent combination of synthetic aperture radar (SAR) images with the quasi-non-overlapped Doppler bandwidth. Prior to the spectra combination, SAR images should be co-registered, while phase biases induced by topography, atmospheric propagation delays and baseline measurement errors should be calibrated. However, the coregistration accuracy suffers from large Doppler decorrelation caused by the quasi-non-overlapped Doppler bandwidth. Furthermore, the method used to estimate phase biases from interferogram of azimuth pre-filtered SAR image pairs will fail when there is no overlapped spectrum. The fringe simulation and maximum sharpness optimization are adopted to deal with the problems. Accordingly, a novel algorithm to coherently synthesize SAR images is presented. The experiment with the Terra SAR X-band (TerraSAR-X) satellite data validates the performance of the presented method.

Target micromotion not only plays an important role in target recognition but also leads to esoteric characteristics in synthetic aperture radar (SAR) imaging. This paper finds out an interesting phenomenon, i.e. the angular extent effect, in micromotion target images formulated by the polar format algorithm. A micromotion target takes on multiple pairs of paired echoes (PEs) around the true point, and each PE extends for an angle which is exactly equal to the angular extent of the synthetic aperture, regardless of the micromotion frequency. The effect is derived and interpreted by using the characteristics of Bessel functions. Then it is demonstrated by simulation experiments of a target with different micromotion frequencies. The revelation and interpretation of the effect is highly beneficial to micromotion-target SAR image understanding as well as target recognition.

The detection performance and the constant false alarm rate behavior of the conventional adaptive detectors are severely degraded in heterogeneous clutter. This paper designs and analyses a knowledge-based (KB) adaptive polarimetric detector in heterogeneous clutter. The proposed detection scheme is composed of a data selector using polarization knowledge and an adaptive polarization detector using training data. A polarization data selector based on the maximum likelihood estimation is proposed to remove outliers from the heterogeeous training data. This selector can remove outliers effectively, thus the training data is purified for estimating the clutter covariance matrix. Consequently, the performance of the adaptive detector is improved. We assess the performance of the KB adaptive polarimetric detector and the adaptive polarimetric detector without a data selector using simulated data and IPIX radar data. The results show that the KB adaptive polarization detector outperforms its non-KB counterparts.

An integer linear bilevel programming problem is firstly transformed into a binary linear bilevel programming problem, and then converted into a single-level binary implicit programming. An orthogonal genetic algorithm is developed for solving the binary linear implicit programming problem based on the orthogonal design. The orthogonal design with the factor analysis, an experimental design method is applied to the genetic algorithm to make the algorithm more robust, statistically sound and quickly convergent. A crossover operator formed by the orthogonal array and the factor analysis is presented. First, this crossover operator can generate a small but representative sample of points as offspring. After all of the better genes of these offspring are selected, a best combination among these offspring is then generated. The simulation results show the effectiveness of the proposed algorithm.

Two interval-valued intuitionistic uncertain linguistic hybrid operators called the induced interval-valued intuitionistic uncertain linguistic hybrid Shapley averaging (I-IIULHSA) operator and the induced interval-valued intuitionistic uncertain linguistic hybrid Shapley geometric (I-IIULHSG) operator are defined. These operators not only reflect the importance of elements and their ordered positions, but also consider the correlations among elements and their ordered positions. Since the fuzzy measures are defined on the power set, it makes the problem exponentially complex. In order to simplify the complexity of solving a fuzzy measure, we further define the induced interval-valued intuitionistic uncertain linguistic hybrid λ–Shapley averaging (I-IIULHλSA) operator and the induced interval-valued intuitionistic uncertain linguistic hybrid λ–Shapley geometric (I-IIULHλSG) operator. Moreover, an approach for multi-attribute group decision making under the interval-valued intuitionistic uncertain linguistic environment is developed. Finally, a numerical example is provided to verify the developed procedure and demonstrate its practicality and feasibility.

Spiking neural P systems with anti-spikes (ASN P systems) are variant forms of spiking neural P systems, which are inspired by inhibitory impulses/spikes or inhibitory synapses. The typical feature of ASN P systems is when a neuron contains both spikes and anti-spikes, spikes and anti-spikes will immediately annihilate each other in a maximal way. In this paper, a restricted variant of ASN P systems, called ASN P systems without annihilating priority, is considered, where the annihilating rule is used as the standard rule, i.e., it is not obligatory to use in the neuron associated with both spikes and anti-spikes. If the annihilating rule is used in a neuron, the annihilation will consume one time unit. As a result, such systems using two categories of spiking rules (identified by (a, a) and (a, ¯a)) can achieve Turing completeness as number accepting devices.

Analysis and design techniques for cooperative flocking of nonholonomic multi-robot systems with connectivity maintenance on directed graphs are presented. First, a set of bounded and smoothly distributed control protocols are devised via carefully designing a class of bounded artificial potential fields (APF) which could guarantee the connectivity maintenance, collision avoidance and distance stabilization simultaneously during the system evolution. The connectivity of the underlying network can be preserved, and the desired stable flocking behavior can be achieved provided that the initial communication topology is strongly connected rather than undirected or balanced, which relaxes the constraints for group topology and extends the previous work to more generalized directed graphs. Furthermore, the proposed control algorithm is extended to solve the flocking problem with a virtual leader. In this case, it is shown that all robots can asymptotically move with the desired velocity and orientation even if there is only one informed robot in the team. Finally, nontrivial simulations and experiments are conducted to verify the effectiveness of the proposed algorithm.

Taking the attitude control system of micro quad-rotor as a research object, a design scheme of fault estimator based  on generalized Kalman-Yakubovic-Popov (GKYP) lemma is put forward to deal with the problem of estimating multiple actuators malfunctions with couplings. Using an H_ index and an appropriate algorithm, the goal of weakening the coupling can be achieved by limiting the fault frequency to a certain range, then different kinds of actuator faults can be estimated correctly. The simulations demonstrate the reliability and validity of the proposed method.

The consensus problems of multi-agents with timevarying delays and switching topologies are studied. First, assume that an agent receives state information from its neighbors with fixed communication delays and processes its own state information with time-varying self-delay respectively. The state time-delay feedback is introduced into the existing consensus protocol to begenerate an improved protocol. Then a sufficient condition is derived which can make the system with time-varying self-delays achieve the consensus. On this basis, a specific form of consensus equilibrium influenced by the initial states of agents, timedelays and state feedback intensity is figured out. In addition, the multi-agent consensus is considered with time-varying topologies. Finally, simulations are presented to illustrate the validity of theoretical results.

In order to detect and estimate faults in discrete linear time-varying uncertain systems, the discrete iterative learning strategy is applied in fault diagnosis, and a novel fault detection and estimation algorithm is proposed. And the threshold limited technology is adopted in the proposed algorithm. Within the chosen optimal time region, residual signals are used in the proposed algorithm to correct the introduced virtual faults with iterative learning rules, making the virtual faults close to these occurred in practical systems. And the same method is repeated in the rest optimal time regions, thereby reaching the aim of fault diagnosis. The proposed algorithm not only completes fault detection and estimation for discrete linear time-varying uncertain systems, but also improves the reliability of fault detection and decreases the false alarm rate. The final simulation results verify the validity of the proposed algorithm.

In the need of some real applications, such as text categorization and image classification, the multi-label learning gradually becomes a hot research point in recent years. Much attention has been paid to the research of multi-label classification algorithms. Considering the fact that the high dimensionality of the multi-label datasets may cause the curse of dimensionality and will hamper the classification process, a dimensionality reduction algorithm, named multi-label kernel discriminant analysis (MLKDA), is proposed to reduce the dimensionality of multi-label datasets. MLKDA, with the kernel trick, processes the multi-label integrally and realizes the nonlinear dimensionality reduction with the idea similar with linear discriminant analysis (LDA). In the classification process of multi-label data, the extreme learning machine (ELM) is an efficient algorithm in the premise of good accuracy. MLKDA, combined with ELM, shows a good performance in multi-label learning experiments with several datasets. The experiments on both static data and data stream show that MLKDA outperforms multi-label dimensionality reduction via dependence maximization (MDDM) and multi-label linear discriminant analysis (MLDA) in cases of balanced datasets and stronger correlation between tags, and ELM is also a good choice for multi-label classification.

The similarity measure is crucial to the performance of spectral clustering. The Gaussian kernel function based on the Euclidean distance is usually adopted as the similarity measure. However, the Euclidean distance measure cannot fully reveal the complex distribution data, and the result of spectral clustering is very sensitive to the scaling parameter. To solve these problems, a new manifold distance measure and a novel simulated annealing spectral clustering (SASC) algorithm based on the manifold distance measure are proposed. The simulated annealing based on genetic algorithm (SAGA), characterized by its rapid convergence to the global optimum, is used to cluster the sample points in the spectral mapping space. The proposed algorithm can not only reflect local and global consistency better, but also reduce the sensitivity of spectral clustering to the kernel parameter, which improves the algorithm’s clustering performance. To efficiently apply the algorithm to image segmentation, the Nystr¨om method is used to reduce the computation complexity. Experimental results show that compared with traditional clustering algorithms and those popular spectral clustering algorithms, the proposed algorithm can achieve better clustering performances on several synthetic datasets, texture images and real images.

Recovering the low-rank structure of data matrix from sparse errors arises in the principal component pursuit (PCP). This paper exploits the higher-order generalization of matrix recovery, named higher-order principal component pursuit (HOPCP), since it is critical in multi-way data analysis. Unlike the convexification
(nuclear norm) for matrix rank function, the tensorial nuclear norm is still an open problem. While existing preliminary works on the tensor completion field provide a viable way to indicate the low complexity estimate of tensor, therefore, the paper focuses on the low multi-linear rank tensor and adopt its convex relaxation to formulate the convex optimization model of HOPCP. The paper further propose two algorithms for HOPCP based on alternative minimization scheme: the augmented Lagrangian alternating direction method (ALADM) and its truncated higher-order singular value decomposition (ALADM-THOSVD) version. The former can obtain a high accuracy solution while the latter is more efficient to handle the computationally intractable problems. Experimental results on both synthetic data and real magnetic resonance imaging data show the applicability of our algorithms in high-dimensional tensor data processing.