A new algorithm is proposed for SAR image registration based on monogenic signal theory. A monogenic phase congruency function is constructed using the characteristics to detect feature points in SAR image. On the other hand, a monogenic vector is composed by the 3-triple components to solve the problem of feature points matching in reference SAR image and sensed SAR image. Diverse SAR image data are used for verification and the experiment results indicate that the proposed algorithm has a good performance of SAR image registration.

In remote sensing image, there is significant difference between the scales of different objects, so any single-scale segmentation can barely produce satisfying result. This paper argues that appropriate segmentation scale can be selected according to the visual complexity of scene. Based on the Watson visual model, a method is proposed to calculate the complexity used for adapting the scale of the Statistical Region Merging (SRM). In addition, the SRM is improved with dynamic merging mode and extended to multi-band image. The experiments demonstrate that the performance of the proposed adaptive scale segmentation is better than any single-scale segmentation.

Rotating Fan-beam SCATterometer (RFSCAT) is a new radar scatterometer system for ocean surface vector wind measurement. Compared with other available scatterometers, RFSCAT can provide more combination of azimuth and incidence angles for a single surface resolution cell. To achieve the required wind vector accuracy, radar scatterometry measurement of backscattering coefficient (σ⁰) must be calibrated within a few tenths of a decibel. In this paper, the method for external calibration of RFSCAT is proposed, based on the system parameters of the scatterometer onboard the Chinese French Oceanography SATellite (CFOSAT), and is verified by simulations. Then QuikSCAT L2A data and SIR of several large homogenous areas are analyzed to check the stability and azimuthal dependence of the (σ⁰) over these areas. A new calibration mask is generated and will be used as a reference for the calibration of RFSCAT.

In this paper, related issues of SAR retrieving ocean surface wind in near shore are discussed thoroughly. First, a method of estimating ocean surface wind direction in near shore is proposed, which uses wind direction of closed ocean surface to estimate needed ocean surface wind direction in near shore based on minimum distance principle. Second, a retrieval method of ocean surface wind speed in near shore using ENVISAT/ASAR IM imaging mode PRI data is given, which Geophysical Model Function (GMF) models’ capability is compared and a segment retrieval algorithm of ocean surface wind speed is proposed. They constitute an integral method of SAR retrieving ocean surface wind in near shore. By experiment and comparison, the effectiveness and rationality of above mentioned method are verified.

MIMO radar imaging using ISAR technique combines the space sampling of MIMO radar and the time sampling of ISAR techniques, which can save many antenna elements and improve image resolution. However, there will be the nonequivalent relationship between the space sampling and time sampling, when parameters fail to be matched. By analyzing the effect of nonequivalent relationship between space and time in detail, this paper obtains the number and locations of false targets. Analytical formulas to calculate the peak to false ratio is deduced, and amplitude attenuation of targets is quantitatively analyzed. On this basis, further discussion gives some characteristics of images for targets in the special locations. Finally, simulations are performed to verify these analyses and formulas.

A new Inverse Synthetic Aperture Radar (ISAR) target recognition method with the fusion of Gabor magnitude and phase feature is proposed. Firstly, the corresponding Gabor Magnitude Maps (GMMs) and Gabor phase information are obtained by convolving the ISAR image with multi-scale and multi-orientation Gabor filters. Secondly, each GMM is divided into several non-overlapping rectangular units, and the histogram of unit is computed and combined as the magnitude histogram feature. Thirdly, the local Gabor phase pattern is obtained by combining quadrant bit coding with local XOR pattern, and the block histogram feature is extracted from the local Gabor phase pattern. Then, the fusion of the Gabor magnitude and phase feature is used as the feature of ISAR image. Finally, five-type aircraft models are classified by using a nearest neighbor classifier with χ² as a dissimilarity measure in the computed feature space. The recognition method is tested on ISAR data simulated from Greco electromagnetic soft ware. Compared with other recognition methods, the numerical results show that the proposed method is effective and has higher recognition performance.

The one-Step Generalized Likelihood Ratio Test (1S-GLRT) and two-Step GLRT (2S-GLRT) are proposed for the problem of adaptive detection for airborne radar with jamming in partially homogeneous environments. Basing on these detectors, the Reduced-Rank GLRT (RR-GLRT) and Diagonally Loaded (DL-GLRT) are introduced in the situation of low sample support. All the novel detectors can effectively suppress the jamming and detect the targets. Moreover, if the number of the training data is low, the RR-GLRT and DL-GLRT can achieve higher Probabilities of Detection (PD’s) than those of the 1S-GLRT and 2S-GLRT. Both the 1S-GLRT and 2S-GLRT possess the Constant False Alarm Rate (CFAR) property, while the RR-GLRT and DL-GLRT are asymptotically CFAR. The effectivity of the novel multichannel space-time adaptive detectors is verified with computer simulation.

The FRaction Fourier Transform (FRFT) is optimal in the detection and parameter estimation of Linear Frequency Modulation (LFM) signal, but it is suboptimal for Linear Frequency Modulation Continuous Wave (LFMCW). LFMCW signal is a periodic extension of an LFM signal. In virtue of?the energy congregation of FRFT for LFM signal and the coherent integrator in signal processing, this paper formulates a new detection and parameter algorithm for LFMCW signal, called the periodic FRFT. The theory analysis and simulation results show the proposed algorithm is asymptotically optimal in the detection and parameter estimation of LFMCW signal along with increasing observation time, and better than traditional FRFT algorithm.

The conventional characteristics analysis methods for moving target in Frequency Modulation Continuous Wave (FMCW) SAR all carry out in space-domain, leading to approximated and partial results. Therefore, a novel analysis approach based on an accurate two-dimensional spectrum is proposed in the paper. The approach takes accurately the continuous movements of antenna and moving targets during pulse transmission and reception into account and derives an analytical slant range model for moving targets in FMCW SAR, as well as an accurate two-dimensional spectrum. Based on the spectrum, the characteristics of moving target are discussed in detail, including the effects caused by 2-D velocities of moving target. Compared with its counterparts, it is more accurate and robust. Simulation experiments verify the reasonableness and effectiveness of the proposed method. 

A novel algorithm for estimation of Azimuth Direction Of Departure (ADOD), Azimuth Direction Of Arrival (ADOA) and Elevation Direction Of Arrival (EDOA) based on bistatic MIMO radar with electromagnetic vector sensors is presented. The linear transmit array composes of multiple scalar sensors with uniform distribution, while the 2D receive array consist of several electromagnetic vector sensor subarrays. Each subarray contains six orthogonally oriented but spatially noncollocating dipoles/loops. With the application of tensor decomposition, the transmit/receive array manifolds are estimated. From the former, a group of multi-targets’ DODs are calculated with an ESPRIT algorithm. An improved vector cross product direction finding algorithm is presented to estimate the targets’ 2D-DOAs, based on the 2D receive array constructed with a presented arrangement of subarrays. The proposed array configuration has great advantage, in spatial aperture extending to refine the estimation accuracy, and in reducing mutual coupling. Corresponding algorithm avoid peak searching and parameter pairing processes. Simulation results are presented to verify the effectiveness of the proposed method.

The parameter estimation performance for the moving bistatic MIMO radar is studied. First, the bistatic MIMO radar signal model with both the transmit and receive arrays being moving is given and the general Cramer- Rao Bound (CRB) expression for the parameters of multi-target is derived. Then, the closed-form CRBs for Direction Of Departure (DOD) and Direction Of Arrival (DOA) of single target are obtained. The impact of some parameters on the CRB is analyzed when both the transmit and receive array are moving. Theoretical analyses and computer simulations show that: the DOD/DOA estimation performance will be improved as the velocity of the moving transmitter/receiver increases, while the other angle (DOA/DOD) estimation performance will degrade. When the aspect angle of moving transmitter/receiver is vertical to the DOD/DOA and the other site is parallel to the DOA/DOD, the best DOD/DOA estimation performance is obtained.

Inverse Synthetic Aperture Radar (ISAR) imaging in short coherent processing interval has a great deal of major advantages in application. In this paper, a novel cross-range scaling algorithm for ISAR imaging with limited pulses and short aperture is proposed. Based on the sparsity characteristic of ISAR image, superresolution ISAR imaging can be achieved via Compressive Sensing (CS) theory. Then the Rotation Angle Velocity (RAV) estimation is realized by using 2-D Fast Fourier Transform (2-D FFT) and polar mapping. Meanwhile, the precision of RAV estimation can efficiently be improved by using CS. Finally, the ISAR image is properly rescaled in the range-cross-range domain. Both simulated and real-measured data experimental results confirm the effectiveness of the proposed method.

Multi-channel SAR in azimuth, which can be used to suppress the Doppler ambiguity to get the High-Resolution Wide-Swath (HRWS) SAR images, suffers from the amplitude and phase mismatch among the channels. Based on the analysis of the characteristics of the multi-channel SAR that the energy of radar echoes outside the Doppler bandwidth is much lower than that in the Doppler bandwidth, this paper presents a novel data-based phase error estimation method for HRWS SAR system. With the assumption that the echoes outside the Doppler bandwidth are approximately zero, the estimation of phase biases in the multi-channel HRWS SAR system can be translated into the Constant Module Quadratic Programming (CMQP) issue. The phase bias among the channels can be extracted using the solutions of the classical CMQP optimization methods, such as Eigen-Value Relaxation (EVR), Semi-Definite Relaxation (SDR) and so on. The theoretical analysis reveals that the presented method can be viewed as an adaptive weighted least square estimation of the phase biases among the channels. The performance of presented method is validated with the experiments of simulated multi-channel SAR data.

The distribution of ground clutter in angle-doppler space depends on the information of platform velocity, radar parameters and so on, which is known as the prior information. In this paper a model with matrix form of the ground clutter data is established. Then based on the equivalence between space and time domain, a Space-Time Block Canceller (STBC) for airborne radar to suppress the ground clutter is proposed via designing a space-time non-adaptive filter. Simulation results are given to show that the proposed STBC can be used as an efficient pre-filtering tool before the conventional Moving Target Indication (MTI) processing and the classical reduced- dimension adaptive processing.

This paper proposes a joint cluster and power allocation algorithm for multiple targets tracking in multistatic radar system. By selecting an optimal subset of radars with predetermined size to cluster around each target and implementing the power allocation strategy among those selected radars, this algorithm can achieves better performance under the resource constraints. Firstly, the Bayesian Cramer Rao Lower Bound (BCRLB) is derived. Then, a criterion minimizing the total BCRLB on the mean square error in multiple targets tracking is derived, and the corresponding optimization problem with two independent vectors is solved by cyclic minimization algorithm incorporating gradient projection method. Finally, the validity of the proposed method is demonstrated with the simulation results.

A modulation classification scheme based on the chaotic theory is presented in low SNR condition by taking the advantage of the chaotic oscillator at noise management. The principle and method of the modulation classification to MPSK signal are given, and a new Duffing oscillator model is designed to improve the classification performance. The method only uses the prior information of carrier frequency, so it can be implemented without the parameter estimation of phase offset, symbol rate and noise power. Simulation experiments show that the probability of correct classification is larger than 95% for classifying 3 kinds of MPSK signals respectively when the SNR is as low as -5 dB. It verifies the validity of the algorithm.

Restricted by the current A/D sampling, parameter estimation of sub-sampling wideband Linear Frequency Modulated (LFM) signal has important research value. Based on that the LFM signal has approximate rectangular spectrum, the differentiation spectrum is used to extract the edge of sub-sampling LFM spectrum by Orthogonal Matching Pursuit (OMP) in this paper. It can estimate the initial frequency and final frequency of sub-sampling wideband LFM signal and has higher estimation precision. The effectiveness of this method is confirmed with numerical simulation.

In order to reduce the transmission data in the multi-view video, the number of multi-views can be decreased properly which include texture images and depth maps, and the intermediate virtual views are generated based on Depth Image Based Rendering (DIBR) at terminal for rendering on 3D displays. Generally, the view synthesis increases the high frequency components on the edge due to inaccurate estimation of the depth map, occlusion and synthesis algorithms. Conventional 2D image quality metric is difficult to reflect the virtual view distortion. In this paper, quality evaluation method based on the edge difference is proposed. Based on the analysis of the pixel difference between virtual and original views, each difference pixel is classified and assigned a visual weight, and higher weights applied to the edge pixels. Experiments for multi-view video sequences prove that the results of this metric are in accordance with characteristic of human visual system.

Shape feature is one of the important characteristics which are used to describe images in MPEG7. The descriptor of Cluster of Concentric Discrete Circles (CCDC) can be extracted in a short time. However, the method uses the variance of the ring arc segment as characteristic in its feature function; it is because that the discrete points of inner and outer circle are different. The feature values range of inner and outer circle are also different, and then it leads to produce characteristic covered up phenomenon. For the above problems, the feature functions are redesigned in this paper. The relative arc length of the arc segment or segments is used as a feature. Each ring characterized is expressed by four sub-features, and each feature value is normalized to the range of 0~1, the extraction process is more simple and faster than that of the original CCDC descriptor. The new method is called Improved CCDC (ICCDC). In this paper, all experiments are done by using the MPEG7-CE1-B standard shape database, and the performance is evaluated by the Precision-Recall curve. The experiments show that the performance of the improved CCDC is better than that of the original method. And the retrieval precision is 50% higher than that of the original method, and the time of extracting all the shape in MPEG7-CE1-B is reduced about 25 ms than that of original method.

How to reduce Markov features’ dimensionality while keeping their steganalytic ability is an important issue in the field of steganalysis. This paper generalizes the statistical hypothesis that images are isotropic from spatial domain to Discrete Cosine Transform (DCT) domain, and provides a new design method. The proposed method is suitable for Markov features of different orders with respect to different sources of extraction and it can effectively deduce the dimensionality of Markov features. Concretely, it can reduce the dimensionality of traditional intrablock features by 36% and that of interblock features by 72%. Experimental results show that the proposed method can also enhance the features’ detection ability.

According to the problem of camera jittering under natural environments when detecting moving objects, a background adaptive scheme is proposed in the paper. First, the Harris operator is used to detect corners in the region-of-interest for background and foreground image respectively. A correlation and relaxation method is also applied to a small region to obtain several stable matched points. Then, the camera jitter parameter is estimated with offsets of these matched points and used to recover background image to match against the current image. At last, background difference algorithm based on the multi-resolution pyramid is adopted to detect moving object. It can remove the environment dynamic background noises and some small offset estimation errors caused by image blurring. The proposed algorithm is verified with camera jittering sequence of the public test image and compared with several state-of-the-art algorithms qualitatively and quantitatively. Experimental results demonstrate that the proposed algorithm can solve the problem of camera jittering in natural environment effectively. The detected effect evaluation parameter is better than the current algorithms.

Pedestrian detection is a key ability for a variety of important applications, such as robotics, driver assistance systems and surveillance. This paper presents a fast pedestrian detection based on saliency detection and Histogram of Oriented Gradient - Non-negative Matrix Factorization (HOG-NMF) features. The regions of interest are extracted using the frequency tuned saliency detection and threshold based on entropy. A novel HOG-NMF features that reduce significantly the length of feature vector are proposed. Classification method using intersection kernel SVM offers significant improvements in accuracy over linear SVM with the same computational complexity. Experiments on INRIA dataset show that the proposed method reduces significantly runtime compared with HOG/linear SVM and HOG/RBF-SVM, achieves the satisfactory accuracy.

Due to characteristics of binary code, it is difficult to deal with binary code variable through traditional interval analysis. To figure out this problem, a new interval analysis method of binary code variable based on abstract interpretation is proposed in this paper. Based on abstract interpretation, two concepts which are word-level data interval and bit-level data interval are presented. The word-level data interval is the abstract presentation of numerical value variable and the bit-level data interval is the abstract presentation of bit value variable. Operation methods of word-level data interval and bit-level data interval are constructed. The conversion operations between word-level data interval and bit-level data interval are put forward and corresponding algorithms are presented. The theoretical and experimental results show that the binary code variable interval can be calculated effectively and precisely with the proposed method. 

Intrusion tolerance is a new mechanism used to build secure computer networks. Therefore, it’s very important to analyze and evaluate the security performance of intrusion tolerant networks before intrusion tolerance is absolutely adopted. Thus, a security analysis model of intrusion tolerant systems based on game theory is proposed in this paper. According to the analysis model, the processes between attacking and tolerating intrusions are considered as a two-player zero-sum stochastic game, in which the optimal action strategies and expected payoffs of the two parties are studied. By using the study results, this paper analyzes the availability, confidentiality and integrality of intrusion-tolerance systems from the perspective of mean time to failure. Meanwhile, it analyzes the factors that will affect the attackers’ choices about action strategies, and obtains the relationships between attack will, payoff and action strategy. The results present the underlying interconnections between intrusion tolerant systems and attackers, which will provide helpful references to withstand the network attacks and intrusions.

Lattice reduction algorithms play an important role in the field of cryptanalysis. In this paper, based on the strategies of genetic algorithm, a new lattice reduction algorithm is proposed through the transformation of the initial lattice basis. The new algorithm always can obtain a shorter vector and a higher quality basis compared with the original algorithms. By the new algorithm, some lattice bases of the Shortest Vector Problem (SVP) challenge are experienced and the outputs of the new algorithm can always reach or break the records on the internet which illustrates that the new algorithm behaves well.

This paper analyzes the mechanism of physical layer secrecy coding and calculates the secrecy rate for the Gaussian wiretap channel under the security gap constraint. Furthermore, a classified normalized decoding algorithm with two-dimensional information correction based on Belief Propagation (BP) algorithm and its improvements for the short and medium block length Low Density Parity Check (LDPC) based secrecy codes is presented. The algorithm first utilizes classification according to the absolute values of incoming messages in check nodes. Then it uses 2-dimensional normalization to correct the minimum and sub-minimum values. The 2-dimensional normalized factors can be calculated respectively by using probability and statistic theory in the initialization step. Simulation results show that the proposed algorithm achieves better performance than BP algorithm and normalized BP-based algorithm at the high SNR, but the bit error radio gets close to 0.5 rapidly at the low SNR. It can reduce security gap of LDPC-based secrecy codes with different secrecy rates efficiently.

Interleaving technology plays an important role in most digital communication systems. The data stream is sent to an interleaver that rearranges the data in order to cope with burst errors and improve the reliability of data transmission. Considering the reconstruction of convolutional interleaver in a non-cooperative context, and an algorithm based on the first order cumulant of the data stream is proposed in this paper. The period of the data stream is detected according to the cumulants of the Attached Sync Markers (ASM) and code words. The depth and width of the convolutional interleaver are estimated according to the distributions of the ASMs. The simulation results show that the proposed method provides an optimal performance in the noisy environment and has less computational load than the Gaussian elimination algorithm.

In current energy detection algorithms, the environmental noise level is often assumed as prior information. However, it is a parameter which needs to be measured in practical application. In the situation of using the known free spectrum to measure noise power is impracticably, this paper proposes a novel broadband spectrum energy detection algorithm based on iteration which can make effective detection to the broadband sampling signal which prior information is total blind. This paper also demonstrates that when the initial value of the iterative algorithm is reasonable, the algorithm can get stable convergence. The simulation results show that the algorithm can obtain a very good performance if the length of time observation is more than 20.

Finding the optimal codebook is one of the key problems for interference alignment with limited feedback, it is equivalent to line packing issue in the Grassmannian manifold. Because analytical construction of the optimal codebook is possible only in very special cases, numerical search algorithms or generalized vector quantization algorithms for source coding are often sought to obtain near-optimal codebooks, but these algorithms characterize with poor performance and high complexity. In order to reduce the complexity of codebook construction, a new accelerative Comprehensive Learning Particle Swarm Optimization (CLPSO) algorithm is proposed. The convergence rate during the early period of the algorithm is speeded by studying of the best particle, the convergence rate during the later period is speeded and the performance of the algorithm is improved through reduction the maximum velocity of particles based on the CLPSO algorithm’s advantage of easy implementation, performing well on searching the optimal solution within defined space for non-linear problems, especial for complex multimodal problems. The simulation results show that the new algorithm achieves better performance than Particle Swarm Optimization (PSO), CLPSO and Generalized Lloyd Algorithm (GLA) with low computational?complexity.

For frequency reuse one systems, the inter-cell interference is one of the restricting factors of the system performance. This paper presents a statistical modeling of the path loss of uplink inter-cell interference link from single interference cell. Through the derivation and analysis of the Moment Generating Function (MGF) and the Cumulant Generating Function (CGF), the reliable approximated probability density function of the path loss of interfering link is derived. Based on the investigation of the derived results, it is observed that with the increase of the variance of shadow fading, the distribution of the path loss of interfering link tends to be Gaussian distribution. In addition, the attenuation exponent of the path loss model has great influence on the distribution of the path loss.

Available Opportunistic Routing (OR) works adopted a relay scheme, which is derived from the traditional wireless routing algorithms. In such scheme, each node is assigned with a global metric or next-hop nodes set. In this paper, it is proved that the relay scheme can not always get the optimal performance by counterexample. The OR process is proposed to be regarded as a Markovian chain of different states from the perspective of state transition, where the states denote different set of nodes that have received the packet. Then the OR algorithms are modeled to help to investigate the intrinsic behavior of OR. It can be applied to the scenarios of multicast and multiple collision domains. Based on that, optimal algorithm named Shortest Opportunistic Routing (SOR) is proposed and proved, which can yield the least Expected Transmission Count (ETX) cost in both scenarios of multicast and multiple collision domain. Simulation results verify the superiority of SOR and show that the performance of SOR surpasses previous algorithms. The optimal OR strategy of wireless multi-hop networks can be selected and the minimal end-to-end ETX cost can be yielded by utilizing the proposed SOR.

A Time Window based Unmanned Aerial Vehicle (UAV) task Coalition Formation algorithm (TWCF) is proposed to solve effectively the problem of task assignment under the scenario of multiple targets and UAVs. The main idea of TWCF is to use the free time window for high effective and real time UAV task assignment. At first, the algorithm uses congestion avoidance mechanism to avoid the resource deadlock of real time task assignment. Then, a two stage coalition member selection algorithm is proposed to prosecute emergency tasks. Moreover, the algorithm can keep the computation overhead effectively to fit the real time and low complex requirement of battlefield. The simulation results show that TWCF increases effectively the completion ratio and reduces the task completion time while the computation overhead keeps at low level all the time.

A hybrid cooperative strategy for Heterogeneous Network (HetNet) is proposed to improve its energy efficiency. By studying the two well-known cooperative strategies: Coordinated Multi-Point Joint Processing (CoMP-JP) and Coordinated Beamforming (CoMP-CB), it is found: CoMP-JP can reduce transmit power consumption with large array gain at the cost of high signal processing, backhauling and circuit power consumption; less signal processing and backhauling power is required under CoMP-CB, but with increased transmit power. Inspired by the observation, a hybrid cooperative strategy is presented to effectively reduce circuit power consumption, while fully exploiting advantages of CoMP-JP and CoMP-CB. Simulation results show that the precoder in the proposed strategy outperforms zero-forcing precoders in CoMP-JP and CoMP-CB in terms of energy efficiency. Further, higher spectrum efficiency can be realized with the proposed strategy.

Because devices’ limited ability and network’s dynamic topology can not ensure persistent high-quality heterogeneous services in Mobile Ad hoc NETwork (MANET), a Dynamic Backup-based Service Contingency Mechanism (DBSCM) is proposed, which includes three stages: service request, select and maintenance. Firstly, the individual and cooperative capability is fully taken into consideration to model the backup index function. Secondly, Time-Prediction based Algorithm (TPA) is proposed to construct robust devices’ set in service selection. Then Global Backup Algorithm (GBA) is proposed to improve execution persistence in service maintenance. Finally, the simulation results demonstrate that the mechanism lowers 30%~45% and 14%~28% about interrupt numbers, improves 10%~40% and 0%~25% about utility and lowers restart-time than Dynamic Monitor Based Service Recovery Strategy (DMBSRS) and Cold Backup Service Replacement Strategy (CBSRS).

In order to speed up the inference algorithm of the link delay distribution based on discrete delay model, a fast inference algorithm based on hierarchy decomposition is proposed. This algorithm decomposes the end-to-end path delay into subtree units by the levels of the topology, and calculates the link delay distribution based on those subtree units. Through the reducing of the redundancy decomposition process of the end-to-end path delay, it can speed up the process of the inference of the link delay distribution. Simulation results show that this algorithm can improve the speed of the link delay distribution without loss of the accuracy.

An ultra-wideband and low RCS (Radar Cross Section) printed antenna is designed and fabricated using the periodically loaded fractal complementary Frequency Selective Surfaces (FSS) on the upper patch and bottom ground plane, respectively. Simulated results show that the relative bandwidth of this novel antenna is significantly broadened from about 2.9% to about 140%, the RCS is reduced effectively to 25.15 dB, and the gain is maitained a high value within the entire band (3.9~22.1 GHz) compared to the original antenna. Experimental data show a reasonably good agreement between the simulation and measured results, and illustrate the validity of the method.

To validate the validity of in human body communication in 2.5~6.0 GHz signal, the path loss characteristic and Specific Absorption Rate (SAR) are investigated by using a 3D ElectroMagnetic (EM) simulator based on Finite Integration Technique (FIT) and a high-resolution 3D electromagnetic model of human body based on CT and MRI segmented images taken from living human males. A numerical statistical model for path loss characteristic is presented. Experiment results substantiate the feasibility and security of human body implant communication in 2.5~6.0 GHz band, and a modificatory classical power law function can well characterize the distance dependent path loss for inside body, the Root-Mean-Square Error (RMSE) of EM simulation and numerical model calculation results is 2.78 dB and 8.30 dB at 2.5 GHz and 6.0 GHz, respectively.

An Angle Of Arrival (AOA) estimation method for Ultra WideBand (UWB) positioning based on a mono-station Hexagonal monopole antenna array is proposed. The AOA information is obtained from the received pulse amplitudes combined with the antenna array beam pattern including dynamic parameters, rather than the time difference of pulses arrival. Performance results are presented in this paper. It shows that an AOA estimation error of less than 1 degree can be achieved with a probability of 96%. Over a location distance of 10 m via IEEE 802.15.4a CM3 channel, the proposed AOA estimation algorithm results in an error of less than 15 cm with a probability of 80%. This UWB system can be simply deployed. It also provides high accuracy, so it is an effective positioning solution.

The nonparametric detection methods for radar target are extensively used in radar system, due to the fact that they can ensure Constant False Alarm Rate (CFAR) for a wide class of input noise distributions and can be easily implemented. In this paper, the detection performance of the Rank Sum (RS) nonparametric detector is investigated analytically for homogeneous background and multiple targets situation, with an assumption of Weibull distributed clutter, for a Swerling II targets model. The mathematical models of detection probability for the RS detector in homogeneous background and multiple targets situation under Weibull assumption are derived. The obtained results show some theoretical and practical values for the design of a radar target detector.