A new Joint Blind Source Separation (J-BSS) algorithm is proposed based on joint diagonalization of fourth-order cumulant tensors. This algorithm constructs first a set of fourth-order tensors by computing the fourth-order cross cumulant of the multiset signals. Then, based on the Jacobian successive rotation strategy, the highly nonlinear optimization problem of joint tensor diagonalization is transformed into a series of simple sub-optimization problems, each admitting a closed form solution. The multiset mixing matrices are hence updated via alternating iterations, which diagonalize jointly the data tensors. Simulation results show that the proposed algorithm has nice convergence pattern and higher accuracy than existing BSS and J-BSS algorithms of a similar type. In addition, the algorithm works well in a real-world application to fetal ECG separation.

To solve the problem of spatial parameter estimation of multi-frequency hopping signals, the sparsity in spatial domain of frequency hopping signals is used to realize the Direction Of Arrival (DOA) estimation based on Sparse Bayesian Learning (SBL). First, the spatial discrete grid is constructed and the offset between the actual DOA and the grid points is modeled into it. The data model of the uniform linear array with multiple frequency hopping signals is established. Then the posterior probability distribution of the sparse signal matrix is obtained by the SBL theory, and the line sparsity of the signal matrix and the offset is controlled by the hyperparameters. Finally, The expectation maximization algorithm is used to iterate the hyper parameters, and the maximum posteriori estimation of the signal matrix is obtained to complete the DOA estimation. Theoretical analysis and simulation experiments show that this method has good estimation performance and can adapt to less snapshots.

Under Single Measurement Vector (SMV) and low Signal-to-Noise Ratio (SNR) conditions, the sparse reconstruction method can improve the estimation accuracy of Time Of Arrival (TOA). However, the existing reconstruction algorithms have some mistakes and missing in the selection of sparse support set elements, which leads to limited estimation accuracy. In order to solve this problem, this paper proposes an algorithm based on sparse reconstruction Loop Matching Pursuit (LMP), which improves the estimation accuracy of the direct path. The algorithm first establishes a sparse representation model of channel impulse response. Then, under the premise of having obtained initial support set, the elements in the support set are removed cyclically. In addition, according to the maximum value of the current residual within the product, the remaining elements are used to match and add the new elements until the residual product is the same. Finally, the estimate of the TOA is obtained using the relationship between the time delay value and the sparse support set. The simulation results show that the proposed algorithm has higher estimation accuracy than the traditional sparse reconstruction time delay estimation algorithm. At the same time, based on the USRP platform, the effectiveness of the proposed algorithm is verified by the actual signal.

In view of the problem of low accuracy and mutual information in left and right hand motor imagery-based ElectroEncephaloGram (EEG), a new approach based on Tunable Q-factor Wavelet Transform (TQWT) is proposed to handle with the binary-class motor imagery EEGs. Firstly, the TQWT is utilized to decompose the filtered EEG signal. Then, several sub-band signals are extracted and followed by calculating their energy, AutoRegressive (AR) model coefficients and fractal dimension. Finally, a Linear Discriminant Analysis (LDA) classifier is used to classify these EEGs. Two Graz datasets of BCI Competition 2003 and 2005 are employed to verify the proposed method. The maximum accuracy of classifying EEGs of four subjects is 88.11%, 89.33%, 77.13% and 78.80%, respectively, and the maximum mutual information is 0.95, 0.96, 0.43 and 0.45. The high accuracies and mutual information demonstrate eventually the effectiveness of the proposed method.

The existing Direct Position Determination (DPD) algorithm of Coherently Distributed (CD) sources rely on the distribution model of CD sources with huge computation cost, which is not practical. To improve further the localization performance, a novel DPD algorithm of CD sources that profits from the characteristics of noncircular signals is proposed based on the symmetric shift invariance of the centrosymmetric array. With the parameterization assumption of CD sources, the direct position determination model is firstly constructed by combining the characteristics of noncircular signals. Then, it is proved that for any centrosymmetric array, the generalized steering vector of CD sources has the property of symmetric shift invariance. Base on this characteristic, the positions of CD sources are directly estimated by fusing the information of all observation stations with no need to consider the distribution model, which reduces the dimension of the parameter to be estimated. Simulation results validate that, compared with the existing localization algorithms of CD sources, the proposed algorithm improves the localization accuracy, and avoids the dependence on the distribution model of CD sources, which is of great practical value.

Bi-direction Long Short-Term Memory (BLSTM) model is widely used in large scale acoustic modeling recently. It is superior to many other neural networks on performance and stability. The reason may be that the BLSTM model gets complicated structure and computation with cell and gates, taking more context and time dependence into account during training. However, one of the biggest problem of BLSTM is overfitting, there are some common ways to get over it, for example, multitask learning, L2 model regularization. A method of spatial smoothing is proposed on BLSTM model to relieve the overfitting problem. First, the activations on the hidden layer are reorganized to a 2-D grid, then a filter transform is used to induce smoothness over the grid, finally adding the smooth information to the objective function, to train a BLSTM network. Experiment results show that the proposed spatial smoothing way achieves 4% relative reduction on Word Error Ratio (WER), when adding the L2 norm to model, which can lower the relative WER by 8.6% jointly.

To solve the problem of the joint blind channel estimation and symbol detection for SIMO-OFDM systems, a PARAllel FACtor (PARAFAC) analysis model of the receive data matrix is established. Then, with the full row rank characteristic of the discrete Fourier transform matrix and the singular value decomposition of the receiving data matrix, a closed method is proposed for joint blind channel estimation and symbol detection. The proposed method has low computational complexity because it has no iteration. Furthermore, by the simultaneously calculated of channel and signals, the proposed method can avoid the performance reduction of signal estimation caused by channel estimation error. Simulation results show that the proposed method has lower computational complexity and better estimation performance compared with traditional methods.

The transmit radios would severely interfere the receive radios, only if they are simultaneously operating in the same tactical command vehicle. Considering this problem, the RF interference cancellation method for multi-transmits and single-receive co-vehicle radios, based on Multi-Channel Least Mean Square (MCLMS) algorithm, is proposed. Firstly, the analysis indicates that the situation of N-transmits and M-receives co-vehicle radios is the equivalent of M case of N-transmits and single-receive, by which the RF interference cancellation model of multi-transmits and single-receive is constructed. Secondly, the RF interference cancellation method based on MCLMS algorithm is presented, and the performance of this method is analyzed to obtain the mathematical relation expression between Mutual-Interference Cancellation Ratio (MICR) and transmit radio number N, convergence factor

. Finally, the simulations demonstrate the validity of the theory result, and indicate that the mutual-interference between transmit radios and receive radios is efficiently suppressed to enhance the electromagnetic compatibility of communication command vehicle.

The high-density characteristic of base stations in Ultra-Dense Networks (UDN) brings serious inter-cell interference. It is the current research hotspot that Coordinated Multiple-Points Joint Transmission (CoMP-JT) is applied to UDN for interference management. The impact of base station density on network performance with CoMP-JT is analyzed. Firstly, the probability density function of the distance between the base station and the user in 3D space is derived using the stochastic geometric method. It provides the cooperation mechanism’s basis for CoMP-JT that selecting the multiple base stations closest to the user to joint transmission. Then, the downlink interference model is carried out based on the bounded dual-slope path loss model, and the downlink coverage probability and network area spectrum efficiency are further derived. Thereafter, the impact of the parameters such as the number of cooperating base stations and the base station density on the performance of the system is investigated. Numerical simulations show that when the number of cooperative base stations is 2, the downlink coverage probability increases by 10%, and the network area spectral efficiency achieves a gain of 2 to 3 times. When the number of cooperating base stations is 3, the cost-effectiveness ratio is better, and the density of base stations that maximizes the network area spectral efficiency under CoMP-JT can be obtained. This paper provides theoretical support for the deployment of base stations in next-generation mobile communication networks.

As an efficient anti-interference technique, Luby Transform (LT) codes are applied to cognitive radio systems for reliable data transmission of secondary users. Encoding and decoding are critical issue for the anti-interference performance of LT codes. To improve the reliability and speed of data transmission, a novel encoding and decoding method Combined Poisson Robust Soliton Distribution-Hierarchical (CPRSD-H) for LT codes is proposed to apply to cognitive radio systems. In the process of encoding, the encoder first produces encoded symbols and generator matrix based on CPRSD, and then uses column vectors corresponding to degree–1 and degree–2 in the generator matrix to carry dual information: the relationship between the degree–1 and degree–2 encoded symbols and their connected input symbols; and part of the original data. Contrarily, in the decoding process, the decoder first uses the Belief Propagation (BP) algorithm to decode by the first information, and then correct some unrecovered bits by the second information. Simulation results show that the proposed method CPRSD-H and application to cognitive radio systems can significantly reduce the Bit Error Rate (BER) of LT codes, the goodput performance of secondary users and the encoding and decoding speed of LT codes.

The convolutive blind source separation can be effectively solved in frequency domain, but blind source separation in frequency domain must solve the problem of ranking ambiguity. A frequency-domain blind source separation sorting algorithm is proposed based on regional growth correction. First, the convolutional mixed signal short-time Fourier transform is used to establish an instantaneous model at each frequency point in the frequency domain for independent component analysis. Based on this, the correlation of the power ratio of the separated signal is used to sort all frequency points one by one replacement. Second, according to the threshold, the sorted result is divided into several small areas. Finally. regional replacement and merging is performed according to the regional growth method, and the correct separation signal is finally obtained. Regional growth correction minimizes the mis-proliferation of frequency sorting and improves separation results. The speech blind source separation experiments are performed in the simulated and real environments respectively. The results show the effectiveness of the proposed algorithm.

In order to achieve service data isolation in advanced metering Infrastructure for water, electricity, gas, and heat Meters and improve the stability and coverage of local data collection network, a network virtualization scheme of Advanced Metering Infrastructure (AMI) is proposed. In this scheme, the end-to-end isolated service data collection channels are constructed utilizing virtual Access Point Name (APN) and Software Defined Network  (SDN) slice technology. The micro-power wireless and low-voltage power line carriers are used to constructed a real-time and reliable local dual mode virtual network. Furthermore, the networking algorithm based on global link-state and hierarchical iterative algorithm are proposed. The simulation and experiments show the packet loss rate and transmission delay of collected data are decreased utilizing the proposed scheme, and business support capability is improved. Moreover, the service data isolation is implemented in AMI for water, electricity, gas, and heat Meters and multiplexing ability of communication network infrastructure is improved.

A non-complete spatial overlapped interference signal model is proposed based on the jamming research against satellite-based positioning receiver for the acquisition of navigation signal. Firstly, the interference model is introduced and analyzed, and the signal fission effect induced by non-complete spatial overlapped interference is demonstrated. Then, the relationship between SINR of satellite-based positioning receiver and spatial overlapped length is derived, and the monotonic relationship of them is deduced. Simulation results suggest that SINR of satellite-based positioning receiver is the monotonically increasing function of spatial overlapped length, and the short-spatial-overlapped interference can restrain the peak amplitude of three dimensional frequency and coded domain correlation, degrading the performance of acquisition of satellite-based positioning receiver.

In Near-Field (NF) applications of Ultra-High-Frequency Radio Frequency IDentification (UHF RFID) systems, due to the structural characteristics of the microstrip tag, the traditional inter-coil mutual impedance expression has a large error in the estimation of the mutual coupling effect such as the frequency shift of the prediction system, and the accuracy is not enough. Firstly, based on the transformer model, the mutual impedance expressions of the NF dense tags are derived from the perspective of radio energy transmission. Then, the electrical parameter values are obtained indirectly by establishing the electromagnetic simulation model combining with the NF inductance coupling tag. Finally, the derivation formula is verified and UHF RFID NF frequency shift is studied from the perspective of environmental factors that affect the mutual impedance between the two tags. The test results show that the derived mutual impedance expression is applied to the frequency offset calculation with error range in 1.6～7.3 MHz when the tags’ spacing is less than 30 mm. The results provide a reference for studying the mutual coupling effect between UHF RFID NF tags based on the mutual impedance between tags.

Circular polarizer is a key component in feed systems with circular polarization in radio astronomy telescope and satellite communication antennas. Conventional polarizers are capable of operating over a maximum bandwidth of 40% with an axial ratio value of 0.75 dB, which is unable to meet the growing demand for wide band applications. In this paper, the design of the wide band quad ridges waveguide polarizer is introduced, and the relationship between the phase constants of two orthogonal principal modes is analyzed. The broadband phase shift characteristics are achieved by employing different horizontal and vertical ridges dimensions. Based on this method, a C-Band polarizer is designed, which operates at 3.625～7.025 GHz, 64% bandwidth. The effects of main parameters on the polarizer performances are studied. A prototype of the polarizer is developed. The measurements of the prototype show that return losses are less than –21 dB for two orthogonal polarizations and the phase difference is 90°±3.8°, the corresponding axial ratio is less than 0.6 dB. Measured and simulated results show good agreements, thus validating the analysis and design methods.

China is a flood disaster-prone country, where floods occur frequently every year, from July to August. Therefore, rapid disaster detection and assessment of floods affected areas is of great significance. GF-3 SAR satellite data has obvious advantages of all-day, all-weather imaging characteristics in flood disaster reduction applications because of its active observation technology. For the purpose of rapid water detection in flooding area, a rapid detection method of flood area based on GF-3 single-polarized SAR data is proposed, including SAR preprocessing, flood extraction based on Markov random fields, shadow false alarm removal. Its detecting accuracy is evaluated with manual detection result. The test results show that this method can realize the rapid and accurate extraction of waters in flood disaster area.

The traditional feature-based image matching method has many problems such as many redundant points and low matching accuracy, which can hardly meet the real-time and robustness requirements. In this regard, a fast scene matching method based on Scale Invariant Feature Transform (SIFT) is proposed. In the feature detection phase, FAST (Features from Accelerated Segment Test) is used to detect characteristics in multi-scale, after then, combining with Difference Of Gauss (DOG) operators to filter characteristics again. From this, the feature search process is simplified. In feature matching phase, the affine transformation model is used to simulate the transformation relation and establish the geometric constraint, to overcome the mismatching because of ignoring the geometric information. The experimental results show that the proposed method is superior to the SIFT in efficiency and precision, also has good robustness to light, blur and scale transformation, achieves scene matching better.

Focusing on the issue of heavy decrease of object tracking performance induced by illumination variation, a visual tracking method via jointly optimizing the illumination compensation and multi-task reverse sparse representation is proposed. The template illumination is firstly compensated by the developed algorithm, which is based on the average brightness difference between templates and candidates. In what follows, the candidate set is exploited to sparsely represent the templates after illumination compensation. Subsequently, the obtained multiple optimization issues associated with single template can be recast as a multi-task optimization one related to multiple templates, which can be solved by the alternative iteration approach to acquire the optimal illumination compensation coefficient and the sparse coding matrix. Finally, the obtained sparse coding matrix can be exploited to quickly eliminate the unrelated candidates, afterwards the local structured evaluation method is employed to achieve the accurate object tracking. As compared to the existing state-of-the-art algorithms, simulation results show that the proposed algorithm can improve the accuracy and robustness of the object tracking significantly in the presence of heavy illumination variation.

In order to achieve more suitable night vision fusion images for human perception, a novel night-vision image fusion algorithm is proposed based on intensity transformation and two-scale decomposition. Firstly, the pixel value from the infrared image is used as the exponential factor to achieve intensity transformation of the visible image, so that the task of infrared-visible image fusion can be transformed into the merging of homogeneous images. Secondly, the enhanced result and the original visible image are decomposed into base and detail layers through a simple average filter. Thirdly, the detail layers are fused by the visual weight maps. Finally, the fused image is reconstructed by synthesizing these results. The fused image is more suitable for the visual perception, because the proposed method presents the result in the visual spectrum band. Experimental results show that the proposed method outperforms obviously the other five methods. In addition, the computation time of the proposed method is less than 0.2 s, which meet the real-time requirements. In the fused result, the details of the background are clear while the objects with high temperature variance are highlighted as well.

To solve the problem of the loss in the motion features during the transmission of deep convolution neural networks and the overfitting of the network model, a cross layer fusion model and a multi-model voting action recognition method are proposed. In the preprocessing stage, the motion information in a video is gathered by the rank pooling method to form approximate dynamic images. Two basic models are presented. One model with two horizontally flipping layers is called " non-fusion model”, and then a fusion structure of the second layer and the fifth layer is added to form a new model named " cross layer fusion model”. The two basic models of " non-fusion model” and " cross layer fusion model” are trained respectively on three different data partitions. The positive and negative sequences of each video are used to generate two approximate dynamic images. So many different classifiers can be obtained by training the two proposed models using different training approximate dynamic images. In testing, the final classification results can be obtained by averaging the results of all these classifiers. Compared with the dynamic image network model, the recognition rate of the non-fusion model and the cross layer fusion model is greatly improved on the UCF101 dataset. The multi-model voting method can effectively alleviate the overfitting of the model, increase the robustness of the algorithm and get better average performance.

Considering the possible security problems of directly extending steganographic schemes for gray-scale images to color images, an adaptive distortion-updated  steganography method is put forward based on the Modification Strategy for Color Components (CCMS). First, the correlation between color components and RGB channels is analyzed, and the principle of distortion cost modification is proposed. Moreover, the optimal modification mode is conducted to maintain the statistical correlation of adjacent components. Finally, color image steganography schemes called CCMS are proposed. The experimental results show that the proposed HILL-CCMS and WOW-CCMS make great improvement over HILL and WOW methods under 5 embedding rates in resisting state-of-the-art color steganalytic methods such as CRM and SCCRM.

Considering the problem of object detection of robots in the home environments, a Tree-Double Deep Q Network (TDDQN) based on the attention action strategy is proposed to determine the locations of region proposals. It combines DDQN with hierarchical tree structure. First, DDQN is used to select the best action of current state and obtain the right region proposal with a few actions executed. According to the state obtained after executing the selected action, the above process is repeated to create multiple "best" paths of the hierarchical tree structure. The best region proposal is selected using non-maximum suppression on region proposals that meet the conditions. Experimental results on Pascal VOC2007 and Pascal VOC2012 show that the proposed method based on TDDQN has better detection performance than other methods for region proposals of different numbers, different Intersection-over-Union (IoU) values and objects of different sizes and kinds, respectively.

Key aggregation searchable encryption can not only retrieve ciphertext through keywords, but also can reduce user key management costs and security risks. This paper analyzes a verifiable key aggregation searchable encryption scheme, noting that the scheme does not satisfy keyword guessing attacks, and that unauthorized internal users can guess the private keys of other users. In order to improve the security of the original scheme, a multi-server key aggregation searchable encryption scheme is proposed in the cloud environment. The new scheme not only improves the security of the original solution, but also adds multi-service features, and improves the storage and search efficiency. Therefore, it is more suitable for a one-to-many user environment.

The efficiency of Service Function Chain (SFC) depends closely on where functions are deployed and how to select paths for data transmission. For the problem of SFC deployment in a resource-constrained network, this paper proposes an optimization algorithm for SFC deployment based on the Longest Effective Function Sequence (LEFS). To optimize function deployment and bandwidth requirement jointly, the upper bound of path length is set and relay nodes are searched incrementally on the basis of LEFS until the service request is satisfied. Simulation results show that, the proposed algorithm can balance network resource and optimize the function deploymen rate and bandwidth utilization. Compared with other algorithms, the utilization of network resource decreases 10%, so that more service requests can be supported. What is more, the algorithm has a lower computation complexity and can response to service requests quickly.

During the radio-off periods of  Wireless Sensor Network (WSN) node, the timer Interrupt ReQuest (IRQ) which used to maintain the system clock become an important energy consumption source of Micro Controller Unit (MCU), thus the IRQ frequency has a great influence on WSN node total energy consumption. A timer resolution adjustment method based on Unscented Kalman Filter (UKF) approach is proposed, which switches high and low IRQ frequencies according to the characteristics of the protocol. Being at a low frequency during sleep period, if a node needs to switch to wake-up period, it will first obtain the optimal estimation of the start time of high resolution timing period by UKF, then enter the high resolution timing period after a linear combination of a group of gradual-changing resolution timer IRQ. The simulations of ContikiMAC protocol on the Tmote platform are conducted. When the Radio Duty Cycle (RDC) is 0.53%, the proposed method reduces the total power consumption by 28.85% compared to the original protocol.

Due to the limitation of individual controller’s processing capacity in large-scale complex Software Defined Networks (SDN), an efficient online algorithm for load balancing among controllers based on efficiency range is proposed to improve load balancing among controllers and reduce the propagation delay between a controller and the switch. In the initial static network, the initial set of controllers is selected by a greedy algorithm, then M improved Minimum Spanning Trees (MST) rooted at the initial set of controllers are constructed, so initial M subnets with load balancing are determined. With the dynamic changes of load, for the purpose of making the controller work within efficiency range at any time, several switches in different subnets are reassigned by Breadth First Search (BFS). The initial set of controllers is updated for minimizing propagation delay in the algorithms’ last step. The algorithm is based on the connectivity of intra-domain and inter-domain. Simulation results show that the proposed algorithms not only guarantee the load balancing among controllers, but also guarantee the lower propagation delay. As to compare to PSA algorithm, optimized K-Means algorithm, etc., it can make Network Load Balancing Index (NLBI) averagely increase by 40.65%.

In view of the security of message in ZigBee network node location, a message signature scheme with privacy protection is proposed. The proposed scheme is based on Elliptic Curve Cryptosystem (ECC) without bilinear pairing, and location request message signature algorithm with identity privacy protection and location reference message signature algorithm with coordinate privacy protection are put forward. It is proved theoretically that the proposed scheme can not only resist the various external attacks, such as forgery attack, replay attack, etc., but also has the function of privacy protection and identity tracking. Performance analysis shows that the proposed scheme has the advantages of computing overhead and communication overhead over similar schemes.

With the development of network information system, virus propagation and immunization strategy become one of the hot topics in the field of network security. In this paper, a new virus with hybrid attacking is introduced, which can attack network in two modes. One is to attack and infect the network nodes directly, and the another is to hide itself in the nodes by hiding its viral characteristic. According to its characteristics, this type of virus is defined as " Two-go and One-live” and the corresponding virus propagation model is established. Moreover, the stability of the system is studied by solving the equilibrium points and analyzing the basic reproduction number R₀. Numerical simulations are presented to verify effectiveness and stability of the novel model.

The existing Ring Oscillator (RO) Physical Unclonable Function (ROPUF) design has low reliability and uniqueness, resulting in poor application security. A statistical model for ROPUF is proposed, the factors of reliability and uniqueness are quantitatively analyzed, it is found that the larger delay difference can improve the reliability, and the lower process difference between RO units can improve the uniqueness. According to the conclusion of the model, a dynamic RO unit is designed based on the mesh topological structure. In combination with the frequency distribution characteristics of the RO array, a new frequency sorting algorithm is designed to increase the delay difference and reduce the process variation of the RO unit, thereby improving the reliability and uniqueness of ROPUF. The results show that compared with other improved ROPUF designs, the reliability and uniqueness of the proposed design has significant advantages, which can reach 99.642% and 49.1%, and temperature changes affect minimally them. It is verified by security analysis that the proposed design has strong anti-modeling attack capabilities.

Binary Decision Diagrams (BDD) is a data structure that can be used to describe a digital circuit. By replacing each node in a BDD with a 2-to-1 Multiplexer (MUX), a BDD can be mapped to a digital circuit. An area and delay optimization method on BDD mapped circuit is presented. A traditional Boolean circuit is converted into BDD form, and then diamond structure constructed by nodes is searched in the BDD, corresponding nodes are deleted and control signals of the modified nodes are updated by paths optimization, finally, the result BDD is mapped to a MUX circuit. The proposed method is test by a number of Microelectronics Center of North Carolina (MCNC) Benchmarks. Compared with the classical synthesis tools Sequential Interactive System (SIS) and BDD-based logic optimization System (BDS), the average number of nodes by the proposed methods is 55.8% less than that of BDS, and average circuit’s area and delay are reduced by 39.3% and 44.4% than that of the SIS, respectively.

A high precision common mode level insensitive sample and hold front-end circuit for charge domain pipelined Analog-to-Digital Converter (ADC) is proposed. The sample and hold circuit can be used to compensate the common mode charge errors caused by the variation of input common mode level in charge domain pipelined ADCs. Based on the proposed sample and hold circuit, a 14-bit 210 MS/s charge domain pipelined ADC is designed and realized in a 1P6M 0.18 μm CMOS process. Test results show the 14-bit 210 MS/s ADC achieves the signal-to-noise ratio of 71.5 dBFS and the spurious free dynamic range of 85.4 dBc, with 30.1 MHz input single tone signal at 210 MS/s, while the ADC core consumes the power consumption of 205 mW and occupies an area of 3.2 mm².

Radar communication integration realizes radar detection and communication transmission via shared hardware equipment, the integration is easier to be integrated, miniaturized and utilize effectively spectrum compared with traditional individual radar and communication devices. This paper systematically introduces the principles and characteristics of radar communication integration, presents the urgent problems need to be solved within integration investigation, starting from typical radar communication integration signal based on Linear Frequency Modulation (LFM), this paper reviews comprehensively the related research on radar communication integration and primarily summarizes the research developments of Orthogonal Frequency Division Multiplexing (OFDM) and Multi-Input Multi-Output (MIMO) techniques in critical directions including waveform design, signal processing and integrated system conception. Finally, the potential developing trend and significant application scenario in military and civilian intelligent transportation field of radar communication integration is analyzed.

Marine oil spill pollution is a serious threat to the marine ecological environment, human life and economic development. Synthetic Aperture Radar (SAR) becomes one of the main technologies for marine oil film detection because of its all-weather and high sensitivity observation capability. This article first introduces the research progress of oil film detection technology on single polarimetric, fully polarimetric and compact polarimetric SAR technologies, based on the basic principle of SAR oil slick detection. Then the main difficulties and challenges encountered in the current research are analyzed. Finally, the broad prospects for the future development of this technology are forecasted.