Assuming the G⁰ distribution clutter background, an automatic block-to-block censoring CFAR (ABC-CFAR) detector is proposed based on VI-CFAR for high resolution SAR image in nonhomogeneous environments. Firstly the Variability Index (VI) statistic is used to censor the blocks in the local reference window in order to reject the non-homogeneous ones in which there exists interfering target samples. Then the Mean Ratio (MR) statistic is utilized to select and combine the homogeneous blocks which have the same distribution, in order to solve background clutter censoring problem in clutter edge situation. At last, with the selected blocks, the distribution parameters of the background clutter are estimated, and then the binary detection is implemented in the Block Under Test (BUT). Using the real SAR image data including ground vehicle targets, the experimental results show that the proposed ABC-CFAR detector has robust detection performance and false alarm regulation property in multi-target and clutter edge nonhomogeneous environment.

A method for the design of transmit waveform of MIMO radar based on the dynamic range constraint in weight amplitude of basic beam is proposed. Firstly, a set of the transmit waveforms is constructed which can fulfill constant-modulus constraints. Further, the control of the difference between different elemental average transmit power is converted into the optimal of the weights dynamic of basic beam. What’s more, a pattern synthesis of the basic beam with the constraint of weight amplitude dynamic range based on the sequential cone program is proposed. The optimal model can be slack as a series sub-convex optimization problem. And the second order cone program of the sub-convex and its iteration technique based on the sequential cone program is given subsequently. Lastly, the weighted coefficients of basic beam of transmit beampattern is computed via second order cone program. The efficiency and validity are verified by the simulation results．

The output Signal-to-Clutter-plus-Noise Ratio (SCNR) of traditional airborne MIMO radar STAP decreases because of the transmit power dispersion. To solve this problem, a MIMO-STAP method based on Transmit Beamspace (TB)-TRi-Iterative Algorithm (TRIA) is proposed. Firstly, the signal model of the TB-based MIMO radar STAP is established, and the optimizing criterion for designing the TB weight matrix is proposed to focus all transmit power within the desired spatial sector. Then, the Clutter-to-Noise Ratio (CNR) of the TB-based MIMO radar is analyzed to show its relationship with the total transmit power. The theoretical derivation is further provided to illustrate that the CNR of the TB-based MIMO radar is reduced compared with that of the traditional MIMO radar with uniform omni-directional transmission. Furthermore, in order to decrease the training sample requirement and the computational complexity of the TB-based MIMO-STAP, the TRIA is utilized to resolve the reduced-dimension weight vectors. The theoretical analysis and simulation results show that, through the corresponding tri-iterative reduced-dimension processing, the TB-based MIMO-STAP can achieve the improvement of the output SCNR, compared to the traditional MIMO-STAP with uniform omni-directional transmission. Moreover, the computational burden is further decreased. Therefore, the proposed TB-TRIA method has great value for engineering application.

In order to overcome the detection performance degradation of the existing detection method when the target and sea clutter is hard to distinguish, an improved target detection method based on mono fractal characteristics is proposed. Firstly, for getting the Intrinsic Mode Function (IMF) after reconstruction, the original signal is decomposed by using Empirical Mode Decomposition (EMD), then the spectrum of target bin and sea clutter bin after denoising is gained by using Fast Fourier Transform (FFT), Mono-Hurst exponents are calculated and the target is detected by nonparametric detector. The results show that, although target and sea clutter is hard to distinguish from frequency spectrum, but their Mono-Hurst exponents is different in scale-invariant interval, compared with original detection method in frequency domain, the proposed method can achieve good detection performance.

The existing algorithms of ultra-wideband through-the-wall radar sparse imaging mostly adopt point target model. Also the regularization parameter of sparse optimization can not be adjusted adaptively, and the ghost imaging can be produced if the targets are not positioned at the pre-discretized grid location. To deal with the above issues, an adaptive sparse imaging algorithm based on Bayesian evidence framework is proposed, which represents sparsely the scene with the point targets and the extended targets by combination of appropriate dictionaries, and maximizes hierarchically the likelihood?function of all parameters as well. The first-level inference of the Bayesian, combined with conjugate gradient algorithm, is adopted to estimate the sparse representation coefficients of the combined dictionaries. The second-level inference of the Bayesian is adopted to estimate the regularization parameter as well as the targets’ off-grid shifts. Therefore, the problem can be solved through iterative optimizating the parameter setting. The simulation and experimental results show that the proposed method can not only adaptively enhance the characteristics of both the point targets and the extended targets, but also mitigate ghosts caused by off-grid targets.

Ground-based radar imaging based on time-division multiplexing MIMO can be used in many important applications, such as application to landslide monitoring in place of synthetic aperture radar imaging. For efficient high-resolution imaging of the ground-based radar based on time-division multiplexing MIMO, an imaging algorithm based on Inverse Fast Fourier Transform (IFFT) pulse compression and beamforming is proposed. High range resolution is obtained by stepped frequency continuous wave technology and high azimuth resolution is obtained by MIMO technology. The range compression of radar data is realized by IFFT and the cross-range compression of radar data is realized by beamforming algorithm. Furthermore, phase discontinuity problem of received signal caused by MIMO antenna arrays is appropriately corrected in the algorithm, both efficiency of this algorithm and imaging quality are also improved. A numerical simulation proves feasibility of this imaging algorithm according to the practical parameters in monitoring and imaging scenario of landslide, and the proposed imaging algorithm has good theoretical performance when it is applied to landslide monitoring.

Quadrature Compressive Sampling (QuadCS) is an efficient Analog-to-Information Conversion (AIC) system to sample band-pass analog signals at sub-Nyquist rates. The QuadCS can be widely used in radar and communication systems to acquire sub-Nyquist samples of inphase and quadrature components. However, for wideband or ultra-wideband pulsed radars, it is often impractical to reconstruct Nyquist samples of full-range echoes in real-time because of huge storage and computational loads. Based on the characteristics of QuadCS system, an approximate scheme is proposed to transform the QuadCS measurement matrix into a matrix with a special banded structure. With the banded matrix, a segment-sliding reconstruction method is adopted to perform real-time reconstruction. Simulation results show that with a reasonable approximation of the measurement matrix, the proposed reconstruction scheme achieves nearly optimal reconstruction performance with a significant reduction of data storage and computational time.

For traditional radar systems, target detection and target tracking are usually conducted separately. However, at the tracking stage, the target location information (tracking information) can be obtained, which can be fed back to the target detector to improve detection performance. Therefore, in this paper, an integrated detection and tracking algorithm with constant track false alarm rate is proposed. Firstly, a predicted gate is established according to the tracking information and target dynamic model. Then according to a prescribed track false alarm rate, the frame false alarm rate, i.e., the probability that there exists at least one false alarm in the predicted region, is calculated. Finally, detection thresholds in the predicted region are adjusted according to the frame false alarm rate, and target detection process is accomplished. Simulation results indicate that the proposed algorithm can significantly improve target detection probability and extend the tracking distance, and meanwhile it can guarantee that the target track can be terminated with a high probability when the target disappears suddenly.

Interferometric Inverse Synthetic Aperture Radar (InISAR) images are widely used for 3-D reconstruction of maneuvering targets in a short observation time. InISAR system is always used for target classification and recognition. Nevertheless, the image plane depends on the target’s own motions and its relative position with respect to the radar. To overcome this problem, this paper proposes an InISAR system containing two baseline perpendicular to each other. The echo signal received by each antenna is focused by the respective reference range. The target angular velocity and Euler angle are estimated by using the interferometric phase with the least square method. Then, the scatterers 3-D coordinates are estimated. Simulation experiments show the effectiveness and robustness of the proposed method.

A method which makes use of structure information abstracted from hyperspectral data via low-rank matrix recovery for hyperspectral image classification is proposed in this paper. The principle of maximizing structure information based on Structural Similarity Index Measurement (SSIM) is proposed to restrain the process of matrix recovery as well, which facilitates the separation of the signal and the noise. The experiments show that the proposed algorithm can effectively eliminate the non-linear noise in hyperspectral image and abstract the low-rank characteristics of hyperspectral image, which achieves better performance in classification.

The imbalance between sample categories in traffic sign detection often results in the weakening of classification detection performance. To overcome this problem, a traffic sign detection method is proposed based on regions of interest and Histogram of Oriented Gradient and Multi-radius Block Local Binary Pattern (HOG-MBLBP) features. First, the color enhancement technology is used to segment and extract the regions of interest of the traffic signs captured in the natural background. Then HOG-MBLBP fusion features are extracted from traffic signs sample library. Moreover, genetic algorithm is used to optimize the parameters of Support Vector Machine (SVM) through cross-validation so as to train and promote SVM classifier performance. Finally, extracted HOG-MBLBP features of interest region images are put into the trained SVM multi-classifiers for further accurate detection and localization. By this method, the paper achieves the purpose of excluding false positives area. The experiments are carried out on the self-built Chinese traffic sign sample library, experimental results show that the proposed method can achieve 99.2% of classification accuracy, and the confusion matrix results also show the superiority of the proposed method.

Tracking effects of algorithms using color information are easily interfered by background clustering, illumination and scale changes, which can result in tracking failure. To solve these problems, an efficient model is proposed to project original RGB color space to a more robust color spaceColor Names (CN) feature space. Furthermore, objects are represented by background weighted color names histogram, and thus the similar background patches around the target are suppressed. Moreover, a two-step tuning way is adapted to estimate the scale by coarse tuning with gradient ascent and fine tuning with constrained items. Back-forward scale check is also used to ensure the precision of scale estimation. 5 representative videos are chosen to examine the proposed algorithms with four others. The results show that the proposed approach is robust to illumination variation, shadows, background clustering, and scale changes. The central distance error and tracking accuracy of the proposed approach also outperform the contrast algorithms.

The classical digital image stabilization system usually suffers from the problems of low precision and hardly real time processing. This paper presents a fast and robust digital image stabilization method. The sub-pixel phase correlation is used for local registration, and the global motion is collaboratively obtained according to the consistency of local motions, which can decrease the calculation while maintain the precision and tolerable stabilization range. The stationary and consistency assessments of intentional movement are proposed, which are used as feedbacks to adjust the parameter online for Kalman filter. Finally, according to the shaking character of frames, the adjacent frames are stored selectively to save storage resources and better compensation effect is achieved. Experiments show that the proposed method is accurate and robust, and can be used for real-time digital image stabilization products.

To deal with the problem that the Bag-Of-Visual-Words (BOVW) model discards image spatial structure, a new method based on the Hessian sparse coding for image retrieval is introduced. First, the n-words model is built in order to obtain the local feature representation. The n-words model can establish a high-level description using a series of visual word sequences to represent an image. The experiments are performed from n=1 to n=5 to seek the proper n. Second, the Hessian sparse coding formulation is acquired by incorporating the Hessian energy function into the standard sparse coding formulation. Finally, using the obtained n-words sequences as the encoding features, the optimal Hessian coefficients are calculated through the feature-sign search algorithm. The similarity is computed and the retrieval results are returned. The experiments are performed on the two datasets, the results show that the proposed new method for image retrieval outperforms the BOVW model and existent methods.

To solve the problem that the second-order fuzzy function can not deal with related noise, as well as the problem of large computation based on fourth-order cumulants joint estimation algorithm, this paper proposes a new joint estimation algorithm of TDOA and FDOA by using wavelet thresholding denoising method combined with characteristics of non-circular signals. The method operates firstly wavelet thresholding denoising for the received signal, then constructs conjugate fuzzy function, and finally two-dimensional search is made to obtain the time difference and frequency difference parameters. The simulation experimental results under different signal-to- noise ratio show that the proposed algorithm can not only suppress correlated noise, but also has relatively lower computational complexity and also can make accurate estimation under low signal-to-noise ratio.

This paper studies the multiple targets’ Direction Of Arrival (DOA) and polarization parameter estimation problem based on the Sparsely Distributed Polarization Sensitive Array (SD-PSA). First of all, the signal model of SD-PSA is set up. Then, by utilizing the space rotational invariance of the proposed array the Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) algorithm is adopted to calculate the signals’ periodic ambiguous but high accuracy DOA estimation. Meanwhile, polarization information and the coarse DOA estimation are derived using the relationship between the sub arrays’ steering vector of the signals. Finally, using the coarse DOA estimation data, the fine and unambiguous estimates of DOA are obtained. In this paper, the adjacent array elements’ spacing of the proposed array is beyond half wavelength of the signal, thus the array extends the two-dimensional physical aperture and reduces the mutual coupling effects to a degree. Accordingly the DOA estimation precision is greatly increased. The simulation results of the algorithm verify the effectiveness of the DOA and polarization estimation.

In order to enhance the accuracy of the classical all-phase FFT/FFT phase-difference frequency estimator, two improvement measures are proposed: no-windowed mode and frequency-shift & compensation. By means of spectral analysis trials and theoretical analysis, it is proved that the no-windowed mode outperforms the windowing mode in highlighting the peak spectral bins of apFFT and FFT, thereby enhancing the estimator’s robustness to noise contamination. By means of frequency-shift & compensation, the no-windowed apFFT and FFT can always work in the state of small frequency deviation, which helps to extract accurate information of phase difference. Simulation results show that, for the proposed estimator, its frequency estimate variance approximates the Cramer-Rao lower bound. Moreover, compared to the Tsui interpolation-based estimator, it also exhibits higher performance of anti-interference in low SNR circumstances, which presents the vast potential for future development.

For Time Difference Of Arrival (TDOA) estimation of multiple sound sources with wide spacing under indoor environment, an unambiguous algorithm based on approximated Kernel Destiny Estimator (KDE) is studied. According to the short-time spectral sparseness of audio signals, the time-frequency bin with energy dominance of a single source is extracted from Coherence Test (CT), then an approximated kernel function constructed of Normalized Cross-Spectrum (NCS) of obtained signals is used to weaken the interference of indoor reverberation with cumulative average, while adding Multi-Stage (MS) to divide the frequency band, the spatial ambiguity with wide spacing can be solved effectively. This algorithm is verified as an unambiguous TDOA estimation algorithm of multi-source under indoor environment by both theoretical derivation and simulation results.

Proxy re-signature has the function of converting signature, and has extensive application prospects, such as cloud storage, data exchange, cross-domain identity authentication and so on. However, most proxy re-signature schemes require expensive bilinear pairing operations, which are not suitable for low-power devices. To improve the performance of proxy re-signature schemes, the security model of a bidirectional sever-aided verification proxy re-signature is presented. Furthermore, a sever-aided verification proxy re-signature scheme is proposed. This scheme is proven to be secure under collusion attacks and adaptive chosen message attacks in the standard model. Analysis results show that the proposed scheme effectively reduces the computation cost of pairing operation, and it greatly reduces computational complexity of signature verification algorithm. The proposed scheme is more efficient than the existing proxy re-signature schemes.

This paper proposes a class of 4-Dimensional Discrete Systems (4DDSs). Using the eigenvalues of Jacobian matrix of the system at the equilibrium, the?stability of the system at the equilibrium is analyzed. A theorem is set up, which is used to determine whether the class systems are periodic or chaotic. Based on the theorem, a 4DDS is constructed. The 4DDS has positive Lyapunov exponent. Numerical simulations show that the dynamic behaviors of the 4DDS have chaotic attractor characteristics as they expects. Combining the 4DDS with Generalized Synchronization (GS) theorem, an 8-Dimensional GS Chaotic System (8DGSCS) is designed. Using this system, this paper designs a 16 bit string Chaotic Pseudo Random Number Generator (CPRNG). Theoretically the key space of the CPRNG is larger than 2¹²⁴⁵. The FIPS 140-2 test suit/Generalized FIPS 140-2 test suit are used to test the randomness of the 1000-key streams consisting of 20000 bit generated by the CPRNG, Narendra RBG, RC4 PRNG and ZUC PRNG, respectively. The results show that there are 100%/99%, 100%/ 82.9%, 99.9%/98.8% and 100%/97.9% key streams passing the FIPS 140-2 test suit/Generalized FIPS 140-2 test suit, respectively. Numerical simulations show that the different key-streams have 50.004% different codes. The results show that the generated CPRNG has good randomness properties, can better resist the brute attack. The designed CPRNG provides a novel tool for the research and development of cryptography.

Single Carrier-Frequency Domain Equalization (SC-FDE) is a competitive alternative for broadband wireless communication systems, which has attracted wide attention and extensive research. As an effective technical solution to cope with multipath effects, SC-FDE shows low complexity and has low signal peak to average power ratio compared with OFDM signals. In burst SC-FDE systems, channel information is required at the receivers to avoid the performance loss of demodulations. Traditional channel estimation methods based on training sequences are not very suitable for broadband burst SC-FDE system. In this paper, a fine channel estimation method based on the time-domain training sequence is proposed. Channel parameters are obtained with the aid of time domain PN sequence based on maximum likelihood criteria. Besides, the noise suppression process is performed for channel estimation values on account of the channel noise strength. Simulation results demonstrate that the proposed channel estimation method can significantly reduce the Bit Error Rate (BER) of signal reception while maintaining low realization complexity.

Effective interference management is one of research difficulties in dense small cell network. This paper proposes a new algorithm in which inter-cell interference is combined with clustering. And the system achieves maximizing system throughput by the optimal power allocation. According to the degree of interference, the cells are divided into several clusters and the cells with the bigger inter-cell interference are divided in the same cluster. The cells within a cluster share the spectrum and serve the user terminals with cooperation and the cells within different clusters may reuse the spectrum. Simulation results show that the proposed algorithm can effectively decrease the interference and improve system throughput in densely deployed small networks.

Spectrum sensing algorithms based on eigenvalue or spectral density usually use the Gaussian approximated distribution and Tracy-Widom distribution to analyze the test statistic with the presence of the primary user or not respectively, but it is hard to find the analysis expression with unified form. In this paper, a spectrum sensing algorithm is proposed based on spatial spectrum density ratio using a Uniform Linear Array (ULA), and a unified expression for the distribution of test statistic is proposed using the latest research results of order statistics. In this algorithm, the test statistic is established using the maximum and minimum values of the discrete spatial spectrum density. Simulation results show that the performance of the proposed algorithm is about 1.7 dB better than the Maximum-Minimum Eigenvalue (MME) ratio algorithm with the detection probability equal to 0.9. At the same time, the results also verify the accuracy of the theoretical distribution of the test statistic.

This paper considers a multi-user full-duplex relay interference channel where self- interference and user interference exist. Firstly, a self-interference cancellation and interference alignment algorithm based on maximum global mutual information is proposed, and then the concrete algorithm and scheme of self-interference cancellation matrix is given. Furthermore, this paper investigates the feasibility conditions of the signal alignment and interference suppression, so as to analyze the mutual information, the interference plus noise power and the degree of freedom of the system. Theoretical analysis and simulation results show that the proposed algorithm can improve the mutual information and the degree of freedom of the system, and reduce the bit error rate compared with the existing typical full-duplex relay scheme. In addition, the relay only needs to do simple power constraints, and it is not necessary to do complex signal processing which reduces the complexity of signal processing for the whole system.

High Efficiency Video Coding (HEVC) provides better compression performance by adopting many new techniques. However these tools also increase the computational complexity of the prediction, which consumes most of encoder computations. This paper proposes an effective splitting algorithm in inter prediction of HEVC. Based on the motion homogeneity of coding unit, a certain threshold is used to decide whether the coding unit should be split into smaller ones. So the unnecessary calculation decreases rapidly. By skipping some specific sub-CUs, the coding complexity is dramatically improved. Experimental results show that the proposed technique can save 46.1% coding time in average with negligible loss of coding efficiency, and the decrease of PSNR is only 0.0418 dB.

In order to improve the compression ratio and quality of the image, combined with the contrast sensitivity characteristics of human vision system and the spectrum characteristics of image in the transform domain, a method is proposed to form the adaptive quantization table in image compression. And according to the JPEG coding algorithm and replacing the quantization table in JPEG, simulations are carried out for three images by programming, whose results are compared with JPEG compression at the same time. The results show that: compared with JPEG compression, under the same compression ratio, average SSIM and PSNR of three decompressed images increase by 1.67% and 4.96% after being compressed using adaptive quantization, respectively. They show that the adaptive quantization based on HVS is a good and practical method.

In order to lower the reliance on single controller in Software Defined Optical Network (SDON), avoid the conflict of different controllers, and improve effectively the survivability on the control plane, the survivable design of SDON control plane based on minimum point covering is proposed. Combined with the constraint of centralized control, the algorithm based on the minimum point covering establishes reliable hierarchical control model, and sets control priority to controllers. The master controller with the highest priority centralized controls the whole net, and the next comes the regional controller, which only intensively controls regional traffic, besides the authority switch which is introduced to control local wavelength in the optical layer owns the lowest level. Meanwhile, the model based on cross-layer information designs survivability redundancy for routing and allocates resource for the control channel. Simulation results show that the proposed strategy can satisfy the request on control delays, and lower the failure probability in the control plane by 30%, thus promoting the network survivability under critical environment.

Many applications in Vehicular Ad-hoc NETworks (VANETs) rely on reliable and efficient broadcast, however, the characters that nodes move quickly and connectivity changes with scenario and time will pose a huge challenge to broadcast protocol designing of VANETs. To cope with the challenge, a broadcast direction based reliable most forward within radius scheme is proposed on the basis of analyzing and verifying that simple most forward within radius scheme has poor reliability. Then, combined with piggybacked acknowledgement mechanism, a new multi-hop broadcast protocol is designed. Compared with simple most forward within radius, reliable most forward within radius can significantly reduce failure rate of relay nodes and improve the single-hop propagation reliability; piggybacked acknowledgement enables the protocol to adapt to poor connectivity scenarios and enhances the reliability of messages spreading though the whole network. The simulation results show that the proposed protocol can achieve high reliability and low redundancy broadcasts.

Traffic flow prediction is a key problem of realizing intelligent transportation technology. Forecasting traffic flow in time and accurately is the precondition to realize the dynamic traffic management. Short -term traffic flow prediction is an important part of traffic flow prediction. In this paper, the Traffic Flow Prediction Based on Combined Model (TFPBCM) based on traffic flow sequence partition and Extreme Learning Machine (ELM) is designed for the short time traffic flow forecasting. The algorithm divides the traffic flow into different patterns along a time dimension by K-means, and then models and forecasts for each pattern by ELM. The proposed algorithm is compared with Back Propagation (BP) and ELM. The combined model algorithm on modeling time is 1/10 of BP, but is 4 times ELM. Its MSE is 1/50 of BP and 1/20 of ELM. The combined model algorithm’s coefficient of detemination (R2  ) is close to 1, so the credibility of the model is higher than others.

The close relationship between the network security function and the hardware devices causes the static rigidity of the traditional security service mode, which is difficult to meet the various security requirement of future network business development. Based on the features of the Software Defined Networking (SDN), a dynamic composition mechanism is proposed for the Composable Security Service Chain (CSSC). First, the overall framework is introduced, and a mathematical model about the composition problem is established by the vector space and integer programming. Then, a heuristic algorithm is designed for solving the model, and the prototype is achieved in SDN environment. Finally, the results of the experiments show that the proposed algorithm outperforms the compared ones, and the advantage of the CSSC is validated by the simulation.

In order to solve the traffic engineering problems such as low throughput and poor load balancing in high connected data center networks, an OpenFlow based Multipath Transmission (OFMT) mechanism is proposed. By taking advantage of OpenFlow centralized control, OFMT calculates precisely the transmission path for each flow and assigns optimally the traffic flow in all transmission paths, it also executes periodic polling and dynamic scheduling mechanisms to achieve good load balancing and high throughput. The experimental results show that the network throughput is significantly improved in OFMT compared with typical data center transport protocols, and OFMT shortens the flow completion time under the same traffic workloads.

To improve the objectivity of evaluation for electromagnetic environment complexity and its influence on electronic information system, taking communication system as an example, the springhead characters of electromagnetic environment complexity, namely signal to interference and noise ratio, is analyzed. The mapping relation between electromagnetic environment grades and the performance, for different systems including 2FSK, BPSK, QPSK, MSK, and QAM is reconstructed. By putting forward electromagnetic environment margin and signal margin, the atlas of electromagnetic environment complexity for typical data communication system is provided. The atlas provides a direct conversion way from signal intensity, interference and noise intensity to the complexity of the electromagnetic environment. Based on this, polarization information extraction and real time spectrum analysis technique for electronic information system are researched. Then, the system composition for measuring and evaluating electromagnetic environment is presented and the operating principle is also analyzed. Based on computer simulation technique, different effects of four communication systems are given: the electromagnetic environment complexity for communication system using MSK and vertical monopole is III grade; that for 64-QAM and horizontal dipole is V grade; that for QPSK and horn antenna is IV grade; that for 2FSK and aperture antenna is II grade. Simulation results indicate that electromagnetic environment component for different communication systems can be effectively obtained by the above-mentioned measuring system, and electromagnetic environment has different effects on the different communication systems just as electromagnetism complexities for different targets are distinct.

FM based passive radar faces some issues such as time varying bandwidth and RCS glint, a multiple FM joint detection scheme is proposed, and the corresponding experiments with passive radar system developed by Wuhan University are carried out. First, the process flow of multi-FM based Passive Radar is introduced. Then, the signal characteristics of multi-FM for radar application are analyzed, meanwhile, the necessity and advantages of multi-FM scheme are demonstrated. Finally, the practical processing results are presented. Experiment result proves that multiple frequency detection scheme can substantially improve target detection performance, enhanceing the robustness of passive radar system.

Signal to Noise Ratio (SNR) of the echo is critical for the detecting performance of sea ship in GEOsynchronous Synthetic Aperture Radar (GEOSAR) system. To address the low SNR problem of single pulse echo due to ultra-long distance between the satellite and sea ship, specific analysis of the improved SNR with coherent integration and its suffering from 3-D rotations of sea ship is carried out in this paper, corresponding conclusions are drew through processing of simulating and real data.

Brain functional network is introduced to feature extraction of ElectroEncephaloGraphy (EEG), and a novel method is proposed based on Least Absolute Shrinkage and Selection Operator (LASSO)-Granger causality between Region Of Interest (ROI) in the brain, in order to overcome the inherent deficiencies of research methods based on isolated brain region. Firstly, the maximum principal component of ROIs is extracted by Principal Component Analysis (PCA), and then causality values between ROIs are calculated by LASSO-Granger. Finally, the values are used as the input vector for Support Vector Machine (SVM), and then four datasets of BCI Competition IV Dataset 1 are used for classification．Experimental results show that different motor imagery tasks are successfully identified by the method of SVM classifier combined with feature extraction which is based on LASSO-Granger causality between the brain region of interest (ROIs). This method provides a new idea for the study of extracting EEG features.