Abstract:In post-Doppler processing methods of STAP, the correlation between the channels is ill advised, and leads to the covariance matrix of clutter becomes inaccuracy. Aim at the problem, a new post-Doppler processing STAP method is proposed in this paper. The method firstly reduces the degree of freedom (DOF) of clutter by the time-delay filtering. Secondly, the method makes full use of the correlation among the adjacent Doppler bins to construct a new covariance matrix, which improve the ability of adaptive in the domain of time and release the burden of clutter suppression in the domain of spacing, eliminate the influence of diffuse information on covariance matrix and combine the multi-Doppler bins carry through the clutter suppression. Thereby, the new method would suppress clutter effectively. Lastly, the measure data is processed by the new method. Its analysis shows that approach improved processing of effectiveness.

Abstract:Almost all the conventional InSAR interferometric phase estimation methods are based on interferogram filtering. The problem here is that when the quality of an interferogram is very poor due to a large coregistration error, it is very difficult for these methods to retrieve the true terrain interferometric phases. In this paper we propose a method to estimate the InSAR interferometric phase based on the model of weight joint steering vector. In the method the optimal joint data vector and the weight joint steering vector are constructed, and then the beamforming technique with the steering vector is used to estimate the InSAR interferometric phase. The method takes advantage of the coherence information of neighboring pixel pairs to auto-coregister the SAR images. Theoretical analysis and computer simulation results show that the method can provide accurate estimate of the terrain interferometric phase (interferogram) even if the coregistration error reaches one pixel. The effectiveness of the method is verified via simulated data and real data.

Abstract:The sparse scene recovery performance of compressed sensing radar (CSR) requires that the coherence parameter of the sensing matrix should be as small as possible. Based on this thought, a new optimal sensing matrix design method is proposed. To minimize the coherence parameter of the sensing matrix, the waveform and measurement matrix are designed separately and simultaneously using simulated annealing (SA). Simulation results demonstrate the algorithm can improve recovery accuracy, enhance noise immunity and increase the maximum permissible sparsity of CSR, and that the joint optimization algorithm can achieve a better result than the algorithms that optimize waveform or measurement matrix separately.

Abstract:Because of the space-diversity and waveform-diversity, large virtual transmit-receive channels are established in statistic MIMO radar. Various methods are used to cope with the channel coefficients of MIMO radar in different papers when estimating the parameters of the targets. To evaluate the channel parameters’effects on MIMO radar estimation performance, two most popular methods are summarized in the paper, and three topical models of channel coefficients are proposed according to the two methods. We derive the MLE and CRLB for the three models respectively. The estimation performance of the three models are compared through simulation. Simulation results show that we can get higher estimation accuracy if the channel coefficients are jointly estimated with the target parameters. But if the other method is used, the estimation accuracy will become worse,the divergence of estimation accuracy between the two methods will increase with the SNR.

Abstract:Because of strong speckle noise in SAR image,the Chan-Vese model level set segmentation method produced a lot of false segmentation.Meanwhile,the level set had disadvantages of large amount of computation and slow segmentation velocity.Therefore,the paper added a new internal force term—distance regularized term,got an improved curve evolution model, based on the Chan-Vese model.we got an improved model of the curve evolution.the model avoided the periodic updates of leve set function and had a greater time step.thus the segmentation speed was speeded up,the anti-noise capability was enhanced. We tested the model by processing the synthetic image and real SAR images.by comparison,the improved model had a higher numerical accuracy and faster division speed. As for image with strong noise,used the enhanced LEE filter,can further improve the speed and effect of the segmentation model. The result show that improved Chan-Vese model can rapid and efficient completion of SAR image segmentation with high robustness.

Abstract:Since multiple-input multiple-output (MIMO) radar uses multiple antennas to simultaneously transmit diverse waveforms and utilizes multiple antennas to receive the reflected signals, it has many potential advantages over conventional phased-array radar. In this paper, we consider propagator method (PM) for 2-dimensional direction of arrival (DOA) estimation with a low computational load based on cross array in monostatic multiple-input multiple-output (MIMO) radar. The proposed algorithm does not require construction of the cross correlation matrix and its eigen-value decomposition, and also avoids peak searching which requires much computation,so we can greatly reduce the computational complexity; and meanwhile our algorithm can obtain automatically paired the azimuth and elevation angle estimation. Our algorithm has very close estimation performance to estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm in the high signal to noise ratio (SNR) situation, which has much higher complexity than our algorithm. And we also derived the mean square error of the azimuth and elevation angle estimation. The simulation results demonstrate its effectiveness and robustness．

Abstract:In the downlink Multiuser Multiple Input Multiple Output (MU-MIMO) system with limited feedback, the system performance will become interference power limited in the high SNR regime. In this paper an improved PU2RC multi-mode transmission scheme is proposed. Channel Quantization Quality Indicator is feedback to the BS and the corresponding mode selection method is provided. Simulation shows that in practical system with small number of active users, the proposed scheme can adaptively choose the best transmission mode to maximize the sum rate of the system. With identical amount of feedback bits the proposed multi-mode transmission scheme outperforms the traditional PU2RC scheme and provides better reference for application of practical MU-MIMO systems.

Abstract:A new scheme based on discrete cosine transform and its inverse is proposed to realize the discrete multitone modulation (DCT-Based-DMT) system. We use the IDCT/DCT to construct the DMT system by replacing the IFFT/FFT transform to complete multi-carrier modulation, and then analyze the peak to average power ratio (PAPR) performance of the proposed system. Based on these results, an energy compressional DCT algorithm is proposed to reduce the PAPR of the DCT-Based-DMT system. The comparison with the selected mapping algorithm based DMT system (SLM-FFT-DMT) demonstrates that the proposed DCT based DMT system (DCT-Based-DMT) with DCT algorithm provides 1.5dB PAPR reduction and lower complexity.

Abstract:The nonsubsampled contourlet transform(NSCT) is based on a nonsubsampled pyramid structure and nonsubsampled directional filter banks.NSCT is shift-invariant ,of high redundancy compared with contourlet transform ,and it can overcome Pseudo-Gibbs phenomenon. An image can be decomposed into multiscale and multidirectional details.These details represent image characteristics in different directions and different frequency bands,which simplify the relationship between coefficients. Learning-based super-resolution reconstruction algorithm has predictability of a whole image.Learning-based method combined with NSCT can improve training accuracy. Experiments with fingerprint image show that the algorithm has good performance and the reconstructed image can maintain the feature points possessed of the high resolution image and is closed to original image with good texture details.

Abstract:Based on analysis of the principles of blind detection techniques, a data hiding algorithm by modifying morphological wavelet high frequency coefficients is proposed. The quincunx sampling lifting scheme is used for image decomposition, then the subband coefficients above a certain threshold are chosen for data embedding and the embedding information table is built for amending algorithm, also the histogram adjustment strategy is introduced at the location of threshold coefficients to preserve the histogram of wavelet coefficients. Since most blind detection algorithms select classifying features according to the differences of statistical distributions between cover and stego images, the proposed method can resist the attack of blind detection techniques. Experimental results show that our method outperforms previous steganographic methods such as LSB matching and Pixel-value differencing on the capability of resisting current typical universal blind detecting methods.

Abstract:According to the sparsity of the channel response, a multipath time-delay estimation model based on compressed sensing(CS) was proposed in frequency domain, by which the time-delay estimation was converted into sparse vector estimation from undersampled data. Because partial DFT (Discrete Fourier Transform)matrix satisfied restricted isometry property(RIP) and the channel response was sparse, according to the CS theory, the required amount of data for the estimation was sharply decreased. The reason that the proposed method owned subchip multipath estimation ability and excellent anti-noise property was also analyzed. Then the time-delay estimation performance of CS method, MUSIC(Multiple Signal Classification) algorithm and ESPRIT(Estimation of Signal Parameters via Rotational Invariance Techniques) algorithm were compared. The simulation and analysis showed that the proposed method owned super-resolution performance in subchip multipath time-delay estimation without prediction of the number of multipath and was superior to MUSIC and ESPRIT algorithms on certain conditions.

Abstract:The Hilbert-Huang （HHT） boundary problem appeared when the signal is decomposed by empirical mode decomposition method (EMD) and the intrinsic mode functions (IMF) in Hilbert transform. In order to overcome the problem, the HHT boundary optimization method based on discrete uniform immune algorithm (DUIA) and support vector regression (SVR) is proposed in this paper. To effectively analyze the boundary problem of HHT, this scheme can use DUIA to optimize parameters of SVR, and then predict the signal by the trained optimally SVR model. For the sine superposition and practical signals, the corresponding simulation results demonstrate that the proposed algorithm can solve the boundary problem of HHT effectively, and its performance is better than prediction method by SVR.

Abstract:The frequency invariant beamformer (FIB) finds important applications such as in distortionless acoustic signal acquisition, et al. Conventional FIB design approaches are usually proposed with ideal microphone array models, i.e., there are no gain and phase mismatches in microphone array characteristics. Therefore, the performance of the conventional FIBs will degrade in practical applications. In this paper, a robust FIB design approach based on regularized constrained least squares are presented, which can work well in the presence of microphone mismatches. The proposed approach is applicable to arbitrary array geometries, and it also has a closed-form solution, so there is no need for iterative computation, which leads to a lower computational complexity in design. Several design examples are presented to show the effectiveness of the proposed approach as well as the correctness of the theoretical analysis.

Abstract:Blind source separation (BSS) is the approach taken to estimate original source signals using only the information of the mixed signals observed in each input channel, and much attention has been paid to BSS in many fields of signal processing. Two approaches have been widely studied and employed to solve the BSS problem: one is based on independent component analysis (ICA) and the other relies on the sparseness of source signals (TF-masking). In order to speed up the convergence rate and avoid permutation problems, this paper presents a method combining the advantages of the two methods uses the results of TF masking to initialize the FDICA. This paper also proposes a new post-processing method for FDICA: Local Minimum Ratio Control (LMRC) spectral subtraction, which is based on the sparse characteristics of speech. Compared with the conventional TF masking and Wiener filter post processing methods, the proposed method can more effectively control musical noise, and improve the separation performance. Experimental results with synthetic data and real data demonstrate the effectiveness of the proposed method.

Abstract:Spectrum of shortwave channels becomes less and less, audio interference has such relatively high density that SNR of received signal worsen badly in the wideband shortwave probe system. The traditional SVD filter loses the power of probe signal in the frequencies of interference, which results in disadvantageous influence to the high accuracy estimation of channel parameters. This paper analyses the internal mechanism of SVD filter, derives the analytical expression of SVD filter and estimates the useful power from signal plus noise subspace ,then presents a novel SVD filtering algorithm based equalization of power. The simulation and data processing of our probe system both demonstrate that the new algorithm not only realizes audio interference rejection, but also effectively reduces power loss of probe signal. Therefore, this algorithm has special important meaning to the high accuracy estimation of the shortwave ionosphere parameter.

Abstract:This paper presents a kernel-based LMS（KLMS） multi-user detection method. Kernel-based LMS detection projects the received signal of DS-CDMA system to high-dimensional character space by a Gaussian kernel function and then uses linear detection. By using the kernel trick, all calculation is in the low-dimensional space so that the complex calculation in high-dimensional is avoided. It is a nonlinear multi-user detector for original space data and the performance is closer to the performance of the optimum detector. Simulation results show that KLMS provides fast convergence and the same stable error compared with other LMS of variable step size by choosing a suitable kernel parameter.

Abstract:A novel double-layer tracking algorithm for turning maneuver target is proposed．It contains the inner layer and the outer layer．The inner layer is composed of function based current statistical (FB-CS) model, in which an activate function whose argument is the innovation variance’s trace were introduced, and then employ the activate function to dispose the error covariance of the acceleration and the frequency of the maneuver, the filter is to attain the direction angular as the input of the outer layer. The outer counter part is composed by curvilinear model, in which the direction angular, the angular velocity and the tangential acceleration were calculated based on the angular output of the inner. Because of the reasonable design of the models, the tracking precision for turn maneuver is improved significantly. Experiment demonstrates the efficiency of the proposed algorithm．

Abstract:For the purpose of diagnosing aircraft health states effectively, a new method based on Empirical Mode Decomposition(EMD) combined with D-S evidence theory is proposed in this paper. First, original acoustic emission(AE) signals of aircraft structural components (stabilizer) were decomposed into several intrinsic mode functions(IMF) using EMD. Use the IMF to construct feature vectors of AE signal, and then apply Fuzzy Neural Network, GRNN and Elman neural network respectively to classify these vectors. Finally, D-S evidence theory is used for decision fusion to determine the aircraft health states. When compared with methods that use single classifier, the effectiveness of the proposed method is demonstrated by higher health diagnosis accuracy from experimental tests on certain type of aircraft.

Abstract:A fault feature extraction method for hydraulic pump vibration signal based on singular value decomposition and wavelet packets analysis is presented. In the method, the noise signal is decomposed into noise distributes uniform vectors, and then through wavelet packets threshold de-noising on every vector, finally, reconstructing de-noised vectors. De-noised signal is decomposed by wavelet packets, and extracting every power of frequency bands. Take gear pump as an example, the feature of gear pump air pocket fault, gear abrasion, side plate abrasion, and normal state vibration signal is extracted by using the method. The result indicates that the method can extract the feature of gear pump effectively.

Abstract:This paper presents a novel approach to quantitatively analyze the error caused by neighbor decimation in the conversion between arbitrary sampling rates. Based on a fractional sampling rate conversion structure, we propose an arbitrary sampling rate conversion algorithm for software radio by neighbor decimation, realize the structure and deduce its computation complexity. Farther, we quantitatively analyze the error of neighbor decimation in detail. Treated as a kind of SNR reduction, the effects of neighbor decimation can be a guideline of parameter selection. The conclusion proved that this method has superiority both in currency and efficiency, the currency behaved as a uniform structure to realize all arbitrary sampling rate conversion, and the efficiency exhibited by the equivalent computation complexity to traditional methods.

Abstract:According to the single location equipment application in cellular networks, the principle of the novel passive location technology is presented in detail based on TSOA/TDOA in the paper firstly. Then the equation of observation is set up by the mathematical model of the TSOA/TDOA location system, and the speed and shift of the mobile are described with the uniform motion state model which is affected by the random acceleration. Finally the locating and tracking algorithm is put forward and derived based on square-root unscented kalman filter, and implements the estimation of the mobile position and speed at the same time. The result of simulation shows the algorithm proposed in the paper has superior performance by comparing with extended kalman filter algorithm and unscented kalman filter tracking algorithm.

Abstract:n this paper, outage probability and throughput over Nakagami-m of incremental relaying using opportunistic are investigated. We derive the closed-form expressions for the outage probability and throughput under the mode of selection combining (SC) including direct link at arbitrary SNR (signal to noise ratio) regime with decode and forward. Computer simulation result verifies our analysis. This paper compares the incremental relaying using opportunistic and opportunistic without incremental relaying. Simulation result shows that the throughput of the incremental relaying using opportunistic outperforms the traditional opportunistic relaying.