Equivalent Number of Looks (ENL) is an important parameter in statistical modelling of multi-look Polarimetric SAR (PolSAR) data. In some automated applications of PolSAR images, it is necessary to estimate the ENL in an unsupervised way without any manual intervention. The existing unsupervised estimation of ENL can not obtain accurate estimates for the images with high heterogeneity. To address this issue, a novel unsupervised estimation method is proposed here. It combines the mixture elimination and clustering based on texture, which reduces the effect of two main heterogeneity factors, mixture and texture. The validity of this method is evaluated with simulated and real data of different complexity.
A method is proposed to analyze and assess the SAR resolution, which considers the statistical characteristics of the distributed targets and the number of multi-look. The joint probability density function of two points on the amplitude radio, phase difference, correlation coefficient, and the number of multi-look is deduced from the SAR complex scattering echo signal. Based on the study of the SAR separable probability and the PDF, a mathematical model is built that reflects the mathematical relationship between the SAR resolution and the statistical properties of the targets and the number of multi-look. Considering the condition that the points can be separated, the influence that the statistical properties of SAR distributed targets and multi-look processing have on SAR resolution is discussed. From the numerical results, when the distance between targets is less than the traditional resolution, the targets also can be distinguished; and when the distance is larger than the traditional resolution, the targets may not be distinguished. The effect of multi-look processing is equivalent to increasing the relevance between the targets, and it also has great influence on SAR resolution. The separable probability can reflect the resolution performance correctly and is important for designing and evaluating the SAR system.
A frequency-domain block Range-Doppler-Space Least-Mean-Square (RDS-LMS) algorithm is proposed for the cancellation of the Doppler spread of clutter for airborne passive radar. To avoid the cancellation of targets in the clutter Doppler band with the cancellation of clutter, this algorithm uses the spatial dependence of the clutter Doppler frequency and cancels clutter along the clutter ridge. With the frequency-domain block implementation, the iteration of adaptive processing is reduced and FFT can be employed. Hence, the computational load is reduced. Simulation results based on experimental data show that the proposed algorithm is able to cancel clutter effectively and more importantly it has slight influence on targets in clutter Doppler band. For example, for targets with radial velocity greater than 10 m/s, signal-to-noise ratio (SNR) loss is within 1 dB. Computational complexity analyses show that the frequency-domain block implementation reduces the computational load 42 times and according to the real-time implemented Frequency-Domain Block LMS (FBLMS) algorithm in ground based passive radar, the proposed algorithm needs 771 ms to process 1 s data with the help of parallel processing of Graphic Processing Unit (GPU) and can satisfy the need for the real-time implementation of airborne passive radar clutter cancellation.
The conventional correction method based on invariable TEC is no longer applicable to GEOSAR, because the temporal variability of ionosphere exits in synthetic aperture time of GEOSAR. In this paper, a correction method based on 4-D electronic density is put forward. This method is validated using actual data of ionosphere and the results indicate that this method can correct the ionospheric effects induced by temporal variability on GEOSAR effectively.
In bistatic spaceborne High Resolution Wide Swath SAR (HRWS-SAR) system, the Digital Beam Forming (DBF) technique is employed to achieve the coherent combination of multi-channel signals in the fast time domain, and its performance is affected by the bistatic configuration. For a point target, the relationship between receiver aspect angle and the sum of transmitter distance and receiver distance is approximated by a linear function, and the echo signal model in elevation channel is built. Further, a DBF processing scheme is proposed, which combines the time-variant weighting and Finite Impulse Response (FIR) filtering, and the implementation block diagram is presented. The DBF processing is simulated in several typical bistatic configuration spaceborne HRWS-SAR systems. Simulation results show that the higher DBF performance can be achieved in the spaceborne HRWS-SAR system with rational bistatic configuration.
The past decades have witnessed an increasing attention for detailed observation with developing of Synthetic Aperture Radar (SAR). The higher the resolutions of SAR images are, the more detailed information can be obtained from the images. At the present stage, the stepped frequency chirp signal waveform is a technology with high practicality to achieve the high range resolution imaging. The system can use the stepped frequency relationships between sub-band signals and then the sub-band synthesizing technique is applied to synthesize a large bandwidth signal. However, the sub-band synthesizing technique is very sensitive to the baseband signal error. A signal pre-distortion scheme is firstly proposed based on regression and statistics in this manuscript. After compensating the error in sub-band, the problems of errors between sub-bands are transformed into a multi-variable optimization problem. Finally, the effectiveness of the proposed method is verified by the X band airborne SAR system.
Passive radar makes full use of non-cooperative illumination source to detect targets. Reference signal purification and clutter suppression of the surveillance signal are two key steps to achieve target signal detection. To solve the problem of the sampling frequency offset in digital TV based passive radar, the signal model is first established, and the impact of the sampling frequency offset on the performance of the reference signal reconstruction is analyzed. Then how the sampling frequency offset affects the clutter suppression and the target correlation matching is mainly investigated. The simulation results show that improving the sampling frequency can reduce the symbol error rate of the reconstructed signal. Different temporal clutter suppression algorithms perform differently under the existence of sampling frequency offset. The sampling frequency offset has a greater impact on the temporal clutter rejection performance and less effect on correlation matching. Finally, the analysis results are verified by the measurement data, which also provide theoretical basis on improving the target detection performance of digital TV based passive radar.
Particle Swarm Optimization (PSO) is an efficient technology to synthesize desired pattern of antenna arrays. But in some complicated cases, the optimizer will fail because the optimum particles fall into local best solutions and the convergence is so bad, especially for nonlinear optimization of large planar arrays pattern. To solve this problem, a modified optimizer is presented to improve the convergence of traditional PSO by means of initializing the particles with analytical values rather than random values. For any given desired pattern, the corresponding aperture weights can be derived by matrix operations and these weights are then used as a particle’s initial values while other particles are still initialized randomly. By this initialization, an efficient estimation of the optimum particle’s initial values can be achieved before the beginning of all particles searching process. After that the standard PSO iterations work as usual. The simulation results prove that this modified optimizer converges more rapidly and deeply than the traditional PSO and more satisfying global solutions and desired pattern could be obtained.
To avoid the track coalescence of the Joint Integrated Probabilistic Data Association (JIPDA), a modified version of JIPDA is proposed by modelling targets as Random Finite Set (RFS). The JIPDA first generates the original Probability Density Function (PDF) and then makes an approximation of the PDF to estimate target states. To maximize the similarity between the state estimate PDF and the original PDF, the original PDF is optimized when target label is irrelevant. Using the KL divergence as a measure of the similarity, the cost function is developed. The experimental results show that the proposed method can effectively avoid the track coalescence.
Polarization calibration is an important prerequisite for practical application such as target detection, classification recognition and quantitative inversion. The attitude variance of carrier platform can cause variance of polarization orientation angle, thus reducing the polarization calibration precision. Improved algorithms based on Whitt algorithm is proposed for this problem. In this paper, the problem of these improved algorithms is analyzed in detail, and on this basis, a polarimatric calibration algorithm, which takes channel unbalance into consider and compensates the time-varying attitude pulse by pulse, is proposed, and it can effectively suppress the influence of attitude change on polarization calibration. Simulation results and experimental data processing verify the effectiveness of the proposed algorithm.
Accurate micro-motion characteristics analysis is an important technique to extract the geometrical characteristics of ballistic targets. In this paper, a multi-frequency Chirp sparse decomposition method for time- frequency analysis of micro-motion targets is proposed. First, the echo micro-Doppler expression of the equivalent scattering center model with the procession motion is deduced. After the translation compensation process, the multi-frequency echo are segmented and used to estimate the micro-motion characteristics with the Chirp sparse decomposition algorithm. While the sparseness of multi-frequency echo are effectively utilized, and the joint multi-frequency estimation improves the robustness of the algorithm. Finally, target,s micro-motion curve are obtained by stitching the segments’ estimation results. The validity and robustness of the algorithm are verified by electromagnetic simulation data and can be applied to time-frequency analysis and recognition of ballistic target.
This paper focuses on Two-Dimensional (2D) geometric feature inversion method of midcourse targets, serving for the target recognition problem of ballistic missile defense system. Based on the figuration characteristic of midcourse targets, the regular pattern of radial dimension is analyzed with respect to different intervals, the mapping relationships between HRRP under various target attitudes and the 2D geometric feature of target are established, then an 2D geometric feature inversion method for midcourse targets based on HRRPs is proposed accordingly. The method can estimate stably the 2D geometric feature of midcourse targets during the midcourse flight, which is verified by the simulation experiments with electromagnetic computed data and measured data in anechoic chamber.
The conventional Compressed Sensing (CS) based remote sensing image fusion algorithm does not consider the correlation between the source images. In this paper, a novel Distributed CS (DCS) based remote sensing image fusion algorithm is proposed to address the correlation between the source images. The proposed algorithm extracts the common part and the unique part of the low frequency information of the source images, in the framework of Joint Sparsity Model-1 (JSM-1). The Unique Feature Addition (UFA) rule is then used to improve the fusion performance. In the experiments, the QuickBird images are utilized to evaluate the performance of the proposed algorithm. The experimental results demonstrate that the fusion performance is significantly improved using the proposed algorithm, compared with several classical fusion algorithms.
Considering the distribution characteristic of the matching points of non-collinear multiple Charge- Coupled Device (CCD) remote sensing images, a new method based on clustering to eliminate the mismatching points is proposed. First, the multi-dimensionality feature vector of matching points is obtained on the basis of the disparity curve in along-track direction. Second, all points are clustered to one cluster. Finally, the points are marked off according to the variation trend of the semi-diameter of the cluster. The experiment results running on the panchromatic image of mapping satellite 1-02 show that the method has better performance on eliminating the mismatched points and keeping the matched points.
Due to the application requirement of adaptive-adaptive dimension reduction method in large-scale passive sonar array, a blind separation adaptive-adaptive beamforming algorithm is presented, which combines the Independent Component Analysis (ICA) with the traditional adaptive-adaptive approach. The ICA approach is firstly used to obtain the steering vectors of signal sources without prior information of signal direction. Then, the adaptive-adaptive approach is utilized to reduce signal dimension in beam space and robust adaptive approach is finally used for beamforming. The simulation studies verify the effectiveness of the proposed algorithm for computation burden reduction of large-scale array adaptive processing. Furthermore, the beamforming results and robustness of the proposed algorithm is similar to that of full- array adaptive method.
Under the condition of working in sun-synchronous orbit, the error correction strategy is put forward due to the error caused by the communication error. According to the complementary error characteristics between satellite clock and local clock, a Digital Phase Locked Loop (DPLL) is designed, which is applied to the low frequency input signal and the large frequency multiplication factor. The local clock tracks the satellite clock pulse phase fluctuations and eliminates the accumulate error constantly. The complete design is developed with Field Programmable Gate Array (FPGA) devices and the detailed theoretical analysis and experimental results are presented. Experiments show that the design of the clock source can correct the abnormal flip or lose of second pulse and jump or lose of broadcast time package constantly. It can enter the lock state in 5 input clock cycles, and the cumulative error is less than 100 μs. It can be used as the local clock source of satellite borne equipment.
An efficient approach based on Just Noticeable Difference (JND) model is proposed to solve the seamline problem caused by illuminate difference. The seamline optimization method operates via a three-stage approach. In the first stage, the image is transformed into Hue, Saturation, Value (HSV) color space, the V weight is selected for the follow-up stage, and the width of correct region is conformed self-adaptively. The second stage is using JND model to calculate the optimize V weight. Last stage is transforming HSV into RGB to get final optimized image. Extensive experimental results using natural images indicate that the proposed method can remove the seamline efficiently. The effect of seamline removal is better even the brightness difference is great. Meanwhile, the proposed method can avoid the color cast problem caused by RGB color space.
Currently, FH signal parameter estimation methods based on compressed sensing are mostly under the assumption of Gaussian noise background. In non-Gaussian α stable distribution noise conditions, the algorithms based on Gaussian noise model suffer undesirable performance degradation. In this paper, it is analyzed and concluded that the spike pulses of the α stable noise approximately meet sparse conditions. By using the differences of the characteristics in the time domain, the FH signal and the noise can be easily separated, and the goal of suppressing noise can be achieved. Under the framework of compressed sensing, the three-parameter dictionary is constructed based on the characteristics of FH signals, then the Optimal Match (OM) for adaptive FH signal decomposition is used to obtain the matching atoms and the FH signal parameters are estimated based on the information contained by these time frequency atoms. Simulation results show that compared with the conventional FH signal parameter estimation methods, the proposed Sparsity-OM (SOM), which uses noise sparsity to suppress the noise and then adopts the OM algorithm, improves the estimation accuracy of FH signal parameters and it is more robust to the α stable distribution noise.
The Root MUltiple SIgnal Classification (Root-MUSIC) algorithm uses polynomial rooting instead of spectral search to reduce the computational complexity of Direction-Of-Arrival (DOA) estimation. However, when large numbers of sensors are exploited, this algorithm is still time-consuming. To further reduce the complexity, a novel Reduced-Dimension Root-MUSIC (RD-Root-MUSIC) algorithm based on spectral factorization is proposed, in which the dimension of polynomial involved in the rooting step is efficiently reduced to half. A companion matrix whose eigenvalues correspond to the roots of the reduced-dimension polynomial is further constructed, and the Arnoldi iteration is finally used to calculate only the L largest eigenvalues containing DOA information, where L is the number of signals. Simulation results show that RD-Root-MUSIC has a similar performance with much lower complexity as compared to Root-MUSIC.
The TL1 norm is applied to propose a new optimization model for the study of subspace clustering. Although the optimization is nonconvex, in the case of non-noise, it proves that the optimal solution of the proposed model is the coefficient matrix with block-diagonal structure, which provides the theoretical guarantee for the latter spectral clustering. In the case of dealing with noise, the constraint condition of this model is presented to be equivalent with the optimal model using the corrected data as the dictionary, which contributes to improving the clustering accuracy. Then, the alternating direction method of Lagrangian multipliers is applied to solving the unknown matrices. Experimental results show that subspace clustering method based on TL1 norm not only enhances the sparsity of coefficient matrix, but also is superior to low-rank subspace clustering and sparse subspace clustering method in terms of clustering accuracy and robustness to noise.
To solve the large amount of computation, time-consuming problems of the FDK reconstruction algorithm for cone beam CT reconstruction, and different resolutions for different application environments of 3D medical image, this paper proposes a fast reconstruction algorithm of multi-resolution cone beam CT image based on wavelet transform. Firstly, the corresponding wavelet transform for projection images are obtained, and the corresponding scale wavelet coefficients are selected for FDK reconstruction. Thus, 3D image data of the low resolution are obtained. According to need, the high resolution 3D image data can also be obtained by the inverse wavelet transform of the radial images obtained from low resolution. The experimental data shows that this method can not only provide a different resolution of the 3D image data, but also increase the reconstruction speed more than one times when the same resolution and similar precision high resolution 3D image data is obtained compared with the traditional FDK algorithm.
A class of mutually orthogonal binary Zero Correlation Zone (ZCZ) sequence sets are constructed. Based on binary orthogonal matrices, numbers of mutually orthogonal aperiodic complementary sequence sets are constructed at first, then mutually orthogonal ZCZ sequence sets are obtained from these aperiodic complementary sequence sets. Each ZCZ sequence set is an optimal binary ZCZ sequence set. Compared with existing methods, the presented construction produces more mutually orthogonal ZCZ sequence sets. As a result, the problem of constructing more than two optimal mutually orthogonal sets of binary ZCZ sequences is sloved by the presented approach and more sequences are produced for Quasi-Synchronous Code Division Multiple Access (QS-CDMA) systems.
In the high-speed mobile communication system such as the radar, due to time delay and Doppler shift in the transmission process, it is needed to analyze Time-Frequency (TF) two-dimensional (2-D) Hamming correlation of the Frequency Hopping Sequence (FHS). Linear feedback shift register sequence (m-sequence) has good random and balance properties, so it is widely used to the construction of FHSs. In this paper, the TF 2-D Hamming correlation of FHS set constructed by m-sequence is analyzed, the distribution of its TF 2-D Hamming correlation is calculated, and an FHS set with new parameters is constructed. Under the same Doppler shift, the 2-D correlation of the new sequence set is more stable than the 2-D correlation of the existing ones.
Due to the characteristics of random, dynamic, and complex arrangement of dense heterogeneous cellular network nodes, the secrecy performance of the system is more seriously affected by the non-ideal channel estimation in the actual communication scenario. However, the research on the secrecy performance analysis of the dense heterogeneous cellular network mainly focuses on the ideal scene, but never involves non-ideal scene. To solve this problem, this paper considers the characteristics and actual deployment of the system，and analyzes the secrecy performance of the system when there exist channel estimation errors. First, based on stochastic geometry, the security outage probability of K-tier dense heterogeneous cellular networks is deduced. Then the influence of partial parameters on system security performance is analyzed. Finally, the validity of theoretical derivation is verified by simulation.
In mobile relay-aided Device-to-Device (D2D) communication, the co-channel interference between D2D links and the existing links is inevitable due to spectral reuse. Besides, considering the limited battery lifetime of mobile terminals, a joint Power Control (PC), Channel Assignment (CA), and MR-selection scheme is proposed to maximize the global energy efficiency of MR-aided D2D communication. By exploring the property of fraction programming, the original problem can be transferred into solving a sequence of parametric programming problems via the Dinkelbach method. Besides, each parametric programming problem can be decomposed into two subproblems, i.e., the PC subproblem and the joint CA and MR selection subproblem. Moreover, the former turns out to be the Difference-of-Concave (DC), programming which is generally NP-hard, but it can be well addressed by sequential convex optimization technique. Based on the above results, the latter reduces to the bipartite matching problem which can be optimally solved by the Hungarian algorithm in polynomial time. Simulation results verify the efficacy of the proposed scheme.
In order to address the problems of the low Spectrum Utilization (SU) and the high Bandwidth Blocking Probability (BBP), a survivable Multipath strategy based on Sharing Degree of protection bandwidth and Spectrum Availability-Aware (M-SDSAA) is proposed. Firstly, the single-path routing is used to transmit the request. The first fit and last fit methods are adopted according to the request duration for the working path Routing and Spectrum Assignment (RSA). A link weight value is designed in accordance with the size of sharable spectrum block. Secondly, the multipath RSA mechanism is used to transmit the request when the single-path RSA fails. The multipath RSA adaptively chooses multiple paths priority with less number of spectrum slots to transmit the request. A shared light-path protection mechanism is used for the multipath provision. Lastly, a reprovisioning mechanism is proposed to further reduce the BBP when the request is blocked. This mechanism reconfigures the blocked request from the sub-optimal protection path to the optimal path occupied minimum protection bandwidth. The simulation results show that the M-SDSAA can improve the SU and decrease the BBP.
SPECK is a family of lightweight block ciphers proposed in 2013 by researches from National Security Agency (NSA) of USA. The algorithm adopts a modified Feistel construction that applies a combination of addition, rotation and XORing (the so-called ARX structure). Up to now, nothing is done on the impossible differential cryptanalysis of the SPECK family except that some 6-round impossible differential characteristics are found by LEE et al. In this article, some 6-round impossible differential characteristics of SPECK 32/64 and SPECK 48/96 are found and a 10-round impossible differential cryptanalysis on these two ciphers is presented by adding one round forward and three rounds backward.
To overcome the common problem of low flexibility and much resource in Elliptic Curve Cryptographic (ECC) processor, a quantitative evaluation on Area-Time product (AT) for parallel processing architecture of ECC processor is proposed by statistics and modeling, and a conclusion that 3-way processing architecture is optimal can be drawn. Besides, a separated and hierarchical storage structure is exploited to strengthen the efficiency of data interaction. At the same time, a modular arithmetic unit is designed with a high level of resource reuse. Using 90 nm CMOS technology, the proposed processor occupied 1.62mm2 can perform the scalar multiplication in 2.26 ms/612.4 μJ over GF(2571) and 2.63 ms/665.4 μJ over GF(p521), respectively. Compared to other works, this processor is advantageous not only in flexibility and scalability but also in making a good compromise between the hardware and the speed.
Brain-Computer Interface (BCI) are expected to play a major role in field of medical-health monitoring in near future. Unfortunately, an increasing number of attacks to BCI applications underline the existence of security and privacy related issues, which gains tremendous attention amongst researchers. In this paper, a communication architecture is proposed for BCI applications, and an access control scheme is designed by employing Ciphertext-Policy Attribute Based Encryption (CP-ABE). The proposed scheme supports fully fine-grained attribute revocation by proxy re-encryption. The proposed scheme can efficiently and feasibly reduce the challenges of privacy preservation, and it works excellent in energy consumption and communication/ computation overhead.
Predicting program popularity is a key issue for design and optimization of Internet TV system. Existing prediction methods usually need large quantity of samples and long training time, while the prediction accuracy is poor for the burst hot programs. This paper introduces an Internet TV Program Popularity Prediction model based on viewing Behavioral Dynamics features (BD3P). 6 billion view behavior records from 2.8 million subscribers of a certain Internet TV platform are measured, and the evolution process of program popularity is divided into 4 types based on behavioral dynamics features, which is endogenous, internal subcritical, exogenous and exogenous subcritical. The prediction models of Internet TV program popularity are constructed for each type using Least Squares Support Vector Machines (LSSVM) with double population Particle Swarm Optimization (PSO), and these models are applied to the actual data test. The experimental results show that, compared to the existing prediction model, the prediction accuracy can be increased by more than 17%, and the forecast period can be effectively shortened.
Traditional Compressive Sensing (CS)-based localization methods assume all targets fall on a pre- sampled and fixed grid. There will be mismatch between the adopted and actual sparsifying dictionaries when some targets fall off the grid, leading these methods to perform poorly. To address this problem, an efficient dynamic dictionary algorithm is developed for CS-based localization. To achieve this, the actual sparsifying dictionary is modeled as a parameterized dictionary with the grid viewed as adjustable parameters. By doing so, the localization problem is formulated as a joint sparse reconstruction and parameter estimation problem. Additionally, the non-convex parameter optimization problem is transformed into a tractable convex problem by approximating the actual sparsifying dictionary with its first Taylor expansion. Extensive simulation results show that the proposed dynamic dictionary algorithm provides better performance than the state-of-the-art fixed dictionary algorithms.
A novel C-element connect method is proposed. The gate of P-type/N-type transistor is modified from the top/bottom of conventional C-element to connect to output, which takes advantage of the transistor,s own feedback mechanism to form a feedback path to achieve the self-recovery function. Therefore, the dynamic performance and hardware overhead are significant reduced. The node-enhanced C-element is used as the output stage circuit and optimized, making the circuit more resistant to single event upset. Based on the above description, a novel soft error-tolerant latch is proposed. Due to the only transmission gate in the shortest route between input and output, the delay in signal transmission is reduced. The critical charge can be further enhanced by using feedback comparison mechanism. Compared with latches in literature at 22 nm CMOS process, the results show that the proposed latch performs greater in reliability and the power delay products improvement of proposed latch achieves 26.74%~97.50%.
In the missile borne subaperture SAR mode, the complicate motion of the platform and the strong coupling between range and azimuth present substantial difficulty for echo data processing. To solve this problem, the range migration characteristics of the echo are analyzed and a secondary frequency function is introduced for uniform migration correction. During the azimuth processing, the azimuth frequency domain projection is applied to compensating the Doppler frequency modulation variation. Compared with the traditional algorithm, this proposed algorithm can focus the echo more effectively. Simulation results show the correctness and validity of the proposed algorithm.
For a classical architecture consisting of a digital channelized receiver followed by frequency measurement using phase difference, the existing precision equation of frequency measurement does not consider the impact of waveform complex envelop on measurement performance. A new precision equation of frequency measurement considering the impact mentioned above is derived in this paper. The consistency of analytical results by the proposed precision equation with simulation results is verified. The simulation results indicate that the proposed equation can exhibit the impact of waveform on frequency measurement performance.
MFSK signal is widely used in military and civil communication systems. For the issues of its modulation parameters’ estimation, wavelet transform theory is mostly adopted in current algorithms. However, the anti-noise performance is poor and parameters are difficult to choose. On the research of indeterminacy theory of spectrogram, a strategy for spectrograms modifying is proposed and the waveform transforming theory is drawn into to upgrade the duty cycle. As the result, the anti-noise performance is improved and the self adaption blind parameter estimation is achieved. The simulation results indicate a better performance of proposed algorithm than current algorithms in anti-noise and frequency offset performance and robustness of parameters which makes it a better choice for engineering practice.
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