The waveform characteristics of the navigation signals of Global Navigation Satellite Systems (GNSSs) will be of vital importance for signal quality, which plays an imperative and direct role in achieving high performance of GNSS services. These traditional methods for evaluating evil waveforms mainly deal with the amplitude and width of simple modulated signals such as Phase Shift Keying (PSK) signals. However, no research is done on the influences of waveform asymmetry on tracking errors and ranging errors. Based on the traditional thread models, such as Thread Model A (TMA), Thread Model B (TMB) and Thread Model C (TMC), adopted by International Civil Aviation Organization (ICAO), this paper provides a new extended general thread model suitable for new Binary Offset Carrier (BOC) modulated signals. Then a new evil waveform analysis method, Waveform Rising and Falling Edge Symmetry (WRaFES) Method, is proposed. The effects of WRaFES model are analyzed in detail in terms of time domain, correlation peak and S curve bias. Finally, by taking the B1Cd signal of the first modernized BeiDou navigation satellite System (BDS) experimental satellite named M1-S as an example, tested results of WRaFES model and correlation curves are shown in detail. Results show that the proposed methods could be able to analyze the asymmetry of signal deformation and its impact on ranging performance with high accuracy. The research brings about a new reference for new satellite navigation signal evaluation and signal system optimized design. In addition, it can  provide valuable suggestions and technical supports for GNSS users to choose reasonable receivers’ correlator spacing.

In order to improve the measurement accuracy of Micro Electro Mechanical Systems (MEMS) gyroscopes, the influence of measurement noise on them is suppressed. The error characteristics of a certain type of MEMS gyroscope are analyzed. A strong tracking self-feedback model based on Recursive Least Square (RLS) multiple wavelet decomposition reconstruction is proposed to establish a new soft threshold function. Since the model processed data has partial singular values, an improved median filtering algorithm is proposed. For the problem of gyro zero-bias noise, a zero-bias stability suppression algorithm is proposed. In this paper, the algorithm model is described in detail, and the experimental data of the train attitude measurement system in a project research are applied to the algorithm model. The test experiments are divided into static and dynamic groups. The results show that the algorithm reduces the noise in the signal, suppresses effectively the random drift of the MEMS gyroscope and improves the accuracy of the attitude calculation. The feasibility and effectiveness of this method are affirmed to remove the signal noise of the gyroscope output and improve the accuracy of the use.

An accurate wideband beampattern synthesis method based on the space-time structure is proposed. Making use of the property that the magnitude response can be translated into linear function under the condition of conjugate symmetric weights, the beampattern synthesis problem is transformed into the convex optimization problem. The weights of space-time structure can be obtained by utilizing the principle of relationship between the two structures, after the weights of space-frequency structure is calculated by the interior point method. The proposed method can realize the wideband beampattern synthesis accurately, meanwhile ensuring the linear phase characteristic of the array response. Simulation results demonstrate the effectiveness of the proposed method.

For the target DOA estimation under active deception jamming environment with limited samples, a novel DOA estimation method based on the combination of Adaptive Polarization Filter(APF) and Block Sparse Bayesian Learning(BSBL) algorithm is proposed. First, the interference energy is suppressed using APF. Then, the proposed method constructs a sparse Bayesian model under active deception jamming environment. The target DOA is estimated using the BSBL algorithm based on the neighbor time sampling correlation. Simulated and measured data processing results prove that the proposed method reduces the influence of interference on the BSBL algorithm, and has higher spatial resolution and higher angle measurement accuracy, comparing with the method based on the combination of APF and subspace-based DOA algorithms or maximum likelihood DOA algorithm.

SMeared SPectrum (SMSP) jamming has lots of coupling in time and frequency domain with Linear Frequency Modulated (LFM) radar signals, which has good jamming performance. This paper proposes a signal processing method for countering SMSP jamming in information domain. According to the formulation and characteristics of SMSP signal, the jamming dictionary is changed automatically, the frequency modulation rate of LFM and SMSP signal is matcheal at the same time, the compressed sampling model is consructed and reconstruction of signal is carried out based on convex optimization. Finally, the recognition of jamming signal and extraction of radar signal are achieved. Pei type fractional Fourier decomposition method is used in construction of redundant dictionary. Modulation and demodulation between time and frequency domain are avoided in this method, which leads to improvement in fewer iteration times and higher arithmetic speed.

Velocity estimation of moving targets is a key part of ground moving target imaging and positioning in airborne single-antenna high-resolution SAR system. In order to solute the defects of traditional algorithms, such as high computation brought by searching and interpolation and low reliability caused by range cell migration, a novel method based on least square fitting of echo sequence is proposed. Range changes between adjacent echo sequences are extracted using envelope correlation, and coefficients of range change equation are obtained by least square linear fitting, from which radial velocity and along-track velocity can be derived. Compared with the traditional algorithms, the new method has less computation and can work without RCMC. The mathematical model is presented and the principle of parameter selection is provided, and accuracy, computation and applicable conditions of the algorithm are analyzed. The effectiveness of the proposed algorithm is validated by simulation and real data.

The target location accuracy is an important technical parameter of airborne SAR system, thus the target location of airborne SAR image is important for application. The precision of the moving parameters directly influences the precision of the SAR image target location algorithm based on the Range-Doppler (RD) model. The locating accuracy is greatly affected if the navigation accuracy of the airborne platform is limited. To solve this problem, an airborne SAR image target location algorithm based on RD model parameter refining is proposed. Using the matching points of airborne SAR image matching with the reference image, the moving parameters are refined with better accuracy, and the locating accuracy is improved. The experiments show that the proposed algorithm is effective.

Due to the 2-D vacancies with serious motion errors when processing 10 GHz ultra-wideband microwave photonic-based SAR, current motion error estimation methods directly estimating with phase error can not obtain correct estimation result in this paper. An ultra-high resolution SAR motion error estimation method jointing envelope and phase is proposed, which can realize accurate estimation of motion error without inertial information. Firstly, the approximate 3-D motion error is obtained by applying the Least Squares Algorithm (LSA) and the Gradient Descent Algorithm (GDA) to the envelope information extracted by the Range Alignment Algorithm (RAA) before Range Curve Migration Correction (RCMC). Then, phase-based motion error estimation is performed on the data after rough compensation and RCMC. After eliminating the azimuth variant phase error, the 2-D space-variant phase error estimation method is used to obtain accurate estimation of residual motion error. Processing of simulated data and real data acquired from vehicle-borne microwave photonic-based radar validates the effectiveness of the proposed method.

The modern spaceborne SAR system requires both high resolution and wide swath, and the conventional single channel spaceborne SAR system has a contradiction between the two important indexes, the azimuth multichannel method is proposed and used to solve the above problem. Based on the analysis of the azimuth multichannel echo model and the characteristics of the Relax algorithm, a spaceborne SAR High Resolution Wide Swath (HRWS) imaging method is proposed, and the iterative process of the new method is described in detail. By the simulation of point target echo, and comparing with the traditional azimuth multichannel HRWS reconstruction methods, the reliability and effectiveness of the proposed method are verified.

With the increasing complexity of radar signals, it is more and more difficult to extract features of the real sequences, but when they are transformed to a symbol sequence, it is usually easier to mine the effective feature parameters. Therefore, a radar signal recognition method based on Multi-Scale Information Entropy (MSIE) is proposed. Firstly, the radar signal is transformed into symbolic sequence by Symbolic Aggregate approXimation (SAX) algorithm under different character number scales. Then, the information entropy of each symbol sequence is combined to form the MSIE feature vector. Finally, the k-Nearest Neighbor (k-NN) is used as a classifier to realize the classification and identification of radar signals. The simulation results of 6 typical radar signals show that using the proposed method the correct recognition rate of different radar signals is greater than 90% when Signal to Noise Ratio (SNR) is 5 dB, and better performance can be obtaned conpared with the traditional identification method based on complexity characteristics (box-dimension and sparseness).

As the bandwidth of the traditional TEM cell can not satisfy the growing demand for broadband, a broadband electromagnetic radiation device working from DC to 6 GHz is designed based on the coaxial structure. According to circuit principle and impedance matching of the transmission line, the device adopts the taper transition structure between the N connector and circular coaxial connected, which achieves the advantages of good impedance matching. The device is simulated by the CST software, and has been fabricated and measured. The simulated results show that S11 is better than –10 dB in the frequency range of DC-6 GHz. Due to the machining error, test results are slightly biased at individual frequencies, which have good consistency with the simulated results and demonstrate the desirable transmission performance of the radiation device. The design has great application value in electromagnetic radiation system.

To solve the problems of complex feature extraction process and low characteristic expressiveness of traditional remote sensing image classification methods, a high resolution remote sensing image classification method based on deep convolution neural network and multi-kernel learning is proposed. Firstly, the deep convolution neural network is constructed to train the remote sensing image data set to learn the outputs of two fully connected layers, which are taken as two high-level features of remote sensing images. Then, the multi-kernel learning is used to train the kernel functions for these two high-level features, so that they can be mapped to the high dimensional space, where these two features are fused adaptively. Finally, with the combined features, a remote sensing image classifier based on Multi-Kernel Learning-Support Vector Machine (MKL-SVM) is designed for remote sensing image classification. Experimental results show that compared with the existing deep learning based remote sensing classification methods, the proposed algorithm achieves improved results in terms of classification accuracy, error, and Kappa coefficient. On the experimental test set, the above three indicators reach 96.43%, 3.57%, and 96.25% respectively, and satisfactory results are obtained.

When multi-objective evolutionary clustering algorithms are applied to image segmentation, the image pixels are always utilized to be clustered. It results in a long running time. In addition, due to not considering the image region information, the image segmentation effect is not ideal. In order to improve the segmentation effect and time efficiency of the multi-objective evolutionary clustering algorithm, the image region information and some supervised information are introduced into multi-objective evolutionary clustering. Then a multi-objective evolutionary semi-supervised fuzzy clustering image segmentation algorithm driven by image region information is presented. First, the region information of the image is obtained through the super-pixel strategy. Second, two novel fitness functions are designed by introducing the supervised information and region information. Third, the multi-objective evolutionary strategy is used to optimize these two fitness functions to obtain an optimal solution set. Finally, an optimal solution evaluation index with region information and supervision information is constructed and utilized to select an optimal solution from the optimal solution set. Experimental results show the proposed algorithm outperforms comparison methods in segmentation performance and running efficiency.

Comparing with K-means, Fuzzy logic is introduced in Fuzzy C-Means to handle the information between clusters. It can obtain better cluster results. However, fuzzy logic makes observations could belong to more than just one cluster, which results FCM is especially sensitivity to the noisy and outlier and has poor generalization performance. So a Rrobust Fuzzy C-Means clustering integrated Between-cluster Information algorithm (RBI-FCM) is proposed. Taking advantage of the sparsity of K-means, RBI-FCM helps to reduce the interactions among different clusters and improve the separability of sample points which locate in the adjacent domains of different clusters. Beside minimizing the inner-cluster scattering condition, RBI-FCM considers the between-cluster information. The generalization performance of RBI-FCM can be improved. An effective iterative algorithm for solving the model is designed in this paper. The experimental results show that the RBI-FCM improves the robustness of FCM and reduce effectively its sensitivity to size-imbalance and differences on the distribution of clusters of FCM. The great clustering result is obtained.

The goal of Image Quality Assessment (IQA) research is to simulate the Human Visual System’s (HVS) perception process of assessing image quality and construct an objective evaluation algorithm that is as consistent as the subjective evaluation result. Many existing algorithms are designed based on local structural similarity, but human subjective perception of images is a high-level, semantic process, and semantic information is essentially non-local, so image quality assessment should take the non-local information of the image into consideration. This paper breaks through the classical framework based on local information, and proposes a framework based on non-local information. Under the proposed framework, an image quality assessment algorithm based on non-local gradient is also presented. This algorithm predicts image quality by measuring the similarity between the non-local gradients of reference image and the distorted image. The experimental results on the public test database TID2008, LIVE, and CSIQ show that the proposed algorithm can obtain better evaluation results.

To improve the detection accuracy and execution efficiency of the existing Random-Valued Impulse Noise (RVIN) detectors, a fast training-based RVIN detection algorithm is implemented by constructing a more descriptive feature vector and training a detection model with more accurate nonlinear mapping. On the one hand, multiple Rank-Ordered Logarithmic absolute Deviation (ROLD) statistics are extracted and combined with a statistical value reflecting the edge characteristics in the form of feature vector to describe how RVIN-like the center pixel of a patch is. The description ability of the feature vector is improved significantly while the computational complexity is just increased in small amount. On the other hand, an RVIN prediction model (RVIN detector) is obtained by training a Deep Belief Network (DBN) to map the feature vectors to noise labels, which is more accurate than the shallow prediction model. Extensive experimental results show that, compared with the existing RVIN detectors, the proposed one has better performance in terms of detection accuracy and execution efficiency.

Focusing on the problem that convolutional auto-encoder network based anomaly detection ignores time information, a novel anomaly detection model based on Bayesian fusion of spatial-temporal stream is proposed. A convolution auto-encoder network is used in spatial stream model to reconstructs video frames, and a convolutional Long Short-Term Memory (LSTM) encoder-decoder network is used to reconstruct short-term optical sequence in the temporal stream model. Then, the reconstruction errors under spatial and temporal stream are calculated separately. Meanwhile, an adaptive thresholds is designed to obtain the reconstruction binary error maps. Finally, the Bayesian fusion strategy is developed to combine the reconstruction error of spatial and temporal stream to obtain the final fusion reconstruction error map based on which the abnormal behavior can be determined. Experimental results show that the proposed algorithm is superior to the existing anomaly detection algorithms in UCSD and Avenue datasets.

In the light of the disadvantages that Jousselme’s evidential distance function can not describe the local conflicting information of evidence well and can not measure the conflict of high conflicting evidence accurately, an improved Jousselme’s evidential distance function is proposed. In the new function, Jousselme’s evidence distance function is improved by using the non-coincidence degree, which can better describe the local conflict of evidence, so that the conflict measure result of evidence varies proportionally with the value of the non-coincidence degree and the scope of its change. Secondly, an improved fusion conflict measure function is constructed based on the conflict coefficient and the new improved Jousselme’s evidential distance function. On this basis, the weight coefficient formula of focal element is improved, and the local multi-dimensional conflicting information is assigned proportionately. Theoretical and application analysis results show that the new algorithm is a kind of evidence fusion algorithm with wide applicability and good anti-jamming performance.

In the standard application mapping problem, it is assumed that the communicating traffic of a task is a fixed value. In the real applications, the communication traffic is uncertain due to the time-varying and bursty characters. Therefore, it has the practical significance modeling the task with communicating traffic uncertainty. Given the interval flow and a conservation factor, the robust application mapping problem is formulated by a min-max model, and then solved by a revised Tabu-based algorithm (Tabu-RAM) in this paper. The algorithm is verified under five benchmark instances. As the experimental results show, under the standard application scenarios, the Tabu-RAM performs better than other methods proposed in the literature. In addition, under the application scenarios with uncertain tasks, experimental results show that the Tabu-RAM performs better and more stable than the traditional tabu algorithm.

An adaptive method of limiter design is proposed to suppress impulsive noise. With a purpose of maximizing the efficacy function, the proposed method searches for optimal thresholds of clipper and blanker, via adaptive line search. Firstly, based on analysis on the relationship between the efficacy and the nonlinearity, the key problem of optimization is proposed. Then, since the calculation of efficacy is hard, an adaptive algorithm based on linear search approach is developed based on linear search to optimize the efficacy. Considering the noise distribution is unknown, the proposed method employs the nonparametric kernel density estimation and works robustly in the presence of estimation error. Finally, numeric simulations demonstrate that the proposed method can obtain the optimal performance of clippers and blankers successfully. In the processing of real atmospheric noise from unknown distribution, the proposed method achieves the best detection performance when combining nonparametric kernel density estimation approach.

Constructions of Gaussian integer periodic complementary sequences are presented in this paper. Based on the relationship between periodic complementary sequences and difference families, the sufficient condition of the existence of Gaussian integer periodic complementary sequences is proposed at first, then Gaussian integer periodic complementary sequences with degree 2 are constructed directly. To extend the number of Gaussian integer complementary sequences, Gaussian integer complementary sequences with degree 4 are constructed based on mappings. Compared with binary complementary sequences, there are more Gaussian integer complementary sequences, as a result, the presented methods will propose an abundance of complementary sequences for communication systems.

Eight-Sided Fortress (ESF) is a lightweight block cipher with a generalized Feistel structure, which can be used in resource-constrained environments such as protecting Radio Frequency IDentification (RFID) tags in the internet of things. At present, the research on the security of ESF mainly adopts the impossible differential cryptanalysis. The ability of ESF to resist the related-key impossible differential cryptanalysis is studied based on the characteristics of its S-boxes and key schedule. By constructing an 11-round related-key impossible differential distinguisher, an attack on 15-round ESF is proposed by adding 2-round at the top and 2-round at the bottom. This attack has a time complexity of 2^40.5 15-round encryptions and a data complexity of 2^61.5 chosen plaintexts with 40 recovered key-bit. Compared with published results, the time complexity is decreased and the data complexity is ideal with the number of attack rounds increased.

Group signcryption is a cryptosystem which can realize group signature and group encryption. However, the message sender and receiver of existing group signcryption schemes are basically in the same cryptosystem, which does not meet the needs of the real environment and the public key encryption technology is basically used, public key encryption technology in encrypted long message efficiency is too low. Therefore, this paper proposes a hybrid group signcryption scheme based on heterogeneous cryptosystem from Identity-Based Cryptosystem (IBC) to CertificateLess Cryptosystem (CLC). In the scheme, The Private Key Generator (PKG) in the IBC cryptosystem and Key Generation Center (KGC) in the CLC cryptosystem generate their own system master keys, and group members can only solve signcryption through collaboration, which improves the security of the scheme. Meanwhile, the user can dynamically join the group without changing the group public key and other members’ private key. The scheme uses hybrid signcryption and has the ability to encrypt any long message. It is proved that the scheme satisfies confidentiality and unforgeability in computing the Diffie-hellman hard problem in the random oracle model. Theoretical and numerical analysis shows that the scheme is more efficient and feasible.

In order to meet the diversified service requirements in future mobile communication networks and provide users with customized services while improving network economic efficiency, a resource allocation algorithm of network slicing based on online auction is proposed. The algorithm transforms the service requests of users into the corresponding bidding information according to the service types. For maximizing the social welfare of the auction participants, the slicing resource allocation problem is modeled as a multi-service based online winner determination problem. Combined with the resource allocation and price updating strategy, the optimal resources allocation based on online auction is achieved. The simulation results show that the proposed algorithm can improve the network economic efficiency and satisfy the service requirements of users.

Mobile Edge Computing (MEC) improves the quality of users experience by providing users with computing capabilities at the edge of the wireless network. However, computing offloading in MEC still faces some problems. In this paper, a joint optimization problem of offloading decision and resource allocation is proposed for the computation offloading problem in Ultra-Dense Networks (UDN) with MEC. To solve this problem, firstly, the coordinate descent method is used to formulate the optimization scheme for the offloading decision. Meanwhile, the improved Hungarian algorithm and greedy algorithm are used to allocate the channels to meet the user’s delay requirements. Finally, the problem of minimizing energy consumption is converted into a problem of minimizing power. Then it is converted into a convex optimization problem to get the user’s optimal transmission power. Simulation results show that the proposed scheme can minimize the energy consumption of the system while satisfying the users’ different delay requirements, and improve effectively the performance of the system.

To address the problems of low spectrum utilization and high energy consumption caused by physical impairment in elastic optical networks, a service differentiated energy efficiency routing strategy with Link Impairment-Aware Spectrum Partition (LI-ASP) is proposed. For reducing the nonlinear impairment between different channels, a path weight formula jointly considering the link spectrum state and transmission impairment is designed to balance the load. A modulation level-layered auxiliary graph is constructed according to traffic’s spectrum efficiency and maximum transmission distance. Starting from the highest modulation in the auxiliary graph, the K link-disjoined maximum weight paths are selected for high quality requests, and the K link-disjoined shortest energy efficiency paths are selected for low quality requests. Then, LI-ASP strategy divides spectrum partition according to requests rate ratio. The First-Fit (FF) and Last-Fit (LF) spectrum allocation policies are used to reduce cross-phase modulation between the requests with different rates. The simulation results show that the proposed LI-ASP strategy can reduce the bandwidth blocking probability and energy consumption effectively.