Abstract:With the development of antenna manufacturing technology, ultra-wideband technology, synthetic aperture technology and signal processing technology, the radar volume is decreasing, and radar detection accuracy and imaging resolution are greatly improved. Furthermore, radar began to be active in the civilian field. New civil radars which are used in penetrating imaging, microwave remote sensing imaging, landslide monitoring and airport foreign object debris detection are developing rapidly. In order to make civil radars with higher and more stable performance in a complex natural environment, radar signal processing technology has been innovating. Here, we introduce the new trends and new technologies of civil radars, as well as the key issues and solutions of signal processing for the wallpenetrating radar, the small SAR, the slope radar and the foreign object debris(FOD) radar.

Abstract:With the increase of the number of channels, bandwidth and resolution, it makes greater demands on data handling capacity and speed. In some specific applications, the volume and power consumption are important factors to consider when designing a data processor. The radar signal processing mode, which relies solely on digital technology, has exposed limitations in computing power, computing speed and power consumption. Based on the research of new information processing theory and method, this paper puts forward the opticselectronics cooperative information processing. The technology combines optical computing operations with electronic logic control, utilizes the spatial optical information processing to complete the highdensity and massive data arithmetic operations, and applies the electronic processing to the transmission control, logic operations and other operations. Photoelectric co-processing has highspeed parallel processing characteristics and the processing relies heavily on optical passive devices with very low power consumption. This paper provides the examples of applications about the optics-electronics cooperative information processing in synthetic aperture radar image formation and array radar beamforming.

Abstract:Measuring non-stationary level is an important topic in non-stationary signal processing. Although the existing measurement parameters and methods have obvious differences in principle and are not derived from a unified theoretical framework and metrics, these measurements play an important role in the corresponding fields. This paper summarizes the measurement parameters and methods for the non-stationary signals in different research fields systematically, including the statistic and dynamic parameters in time domain, the parameters in time-frequency domain, signal modeling parameters and specific non-stationary signal measurements. Moreover, the applications of various measurement parameters and methods are given briefly. Finally, the future work on the non-stationary signal measurements is also deduced.

Abstract:Multi-model algorithm is the state-of-the-art approach to maneuvering target tracking. Due to the increasing types of targets and complication of motion environment, it is more and more difficult to meet the target tracking requirements by only using the position measurement. In addition to the position measurement, the knowledge about targets and their environment can be adopted to adaptively adjust the three key factors, i.e., the model set, transition probability matrix and model probability in the multi-model algorithm to achieve better performance. This paper analyzes the principles and methods of knowledge-aided multiple-model maneuvering target tracking algorithm. According to the subjects (model set, transition-probability matrix and model probability) that the knowledge being used to adjust, the adjustment manner (intelligent methods and non-intelligent methods), the principles and characteristics of adjustments are introduced, respectively. Finally, future research of knowledge-aided multi-model maneuvering target tracking algorithm is given.

Abstract:Passive synthetic aperture radar imaging uses illuminators of opportunity and moving receivers to image the scene of interest and has good electronic antagonism. It can also reduce system cost, benefit to system miniaturization and ease frequency band congestion. In this paper, we present a unified theory for passive synthetic aperture radar imaging based on inverse scattering and microlocal analysis. It is suitable for airborne receivers flying along arbitrary flight trajectories and static or moving illuminators of opportunity. Two types of waveforms are considered: narrowband continuous-wave (CW) waveforms and wideband pulsed waveforms. Our theory results in two novel synthetic aperture imaging modalities: Synthetic aperture hitchhiker (SAH) that uses wideband pulsed waveforms and Doppler synthetic aperture Hitchhiker (DSAH) that uses single-frequency or ultra-narrowband CW waveforms. First, we develop measurement models which are able to remove the transmitter-related terms in the phase of the correlated measurements. We use filtered backprojection (FBP) theory and microlocal analysis to develop approximate inversion formulas for SAH and DSAH. The inversion formulas involve backprojection of the correlated measurements onto iso-passive-range and iso-passive-Doppler contours for SAH and DSAH imaging, respectively. Detailed resolution analysis is given. Finally, we present numerical simulations to demonstrate our theoretical results.

Abstract:After investigating the structural features of burst signal, an improved sparse channel estimation algorithm is proposed based on compressive sensing. In the initial estimation, the autocorrelation property of preamble pseudo-random sequence is utilized to estimate the path delay of channel. Then the sparse recovery with the delay is initialized, which takes advantage of the prior information of channel estimation. In the follow-up channel estimation, the algorithm tracks the current channel information through the channel information estimated in the previous moment. Simulations indicate that the proposed algorithm improves channel estimation precision, and decreases the bit error rate of receiver system, when compared with the lease square estimation algorithm, orthogonal matching pursuit algorithm and sparse reconstruction by separable approximation algorithm.

Abstract:Interframe and intraframe copy-paste forgery are two typical video forgery means. Interframe manipulation is easily done with the video editing software. An algorithm based on nonnegative matrix factorization(NMF) and speed-up robust featuare(SURF) is proposed for detecting interframe copy-paste forgery. Frames are transformed via wavelet．And low-frequency band is further decomposed by NMF. The NMF coefficient matrix is served as the video frame feature descriptor. The variation of frame similarity is used for locating the forged first and last frames via measuring frame similarity. The starting and ending frames mark the suspicious frame sequence, and these frames are further investigated by SURF. The presented approach avoids frame-by-frame match and decreases the time complexity to O(n). The experiment demostrates that the proposed algorithm performs well for interframe video copy-paste forgery detection.

Abstract:Artificial bee colony algorithm is a novel bio-inspired intelligence optimization algorithm. Compared with other bio-inspired intelligence optimization algorithms, the optimization strategy of artificial bee colony(ABC) algorithm still need to be improved to enhance the convergence speed and the optimization precise.A simple and effective modified artificial bee colony algorithm based on normal distribution is proposed here. Firstly, the nectar source initialization strategy based on normal distribution is given. The purposiveness of the initialization process is improved and the search precise can be ensured. Then, the basic position and the zoom factor in the search equation are modified. The search range is enlarged and the purposiveness of the search is also improved. Therefore, the property of global convergence and the optimization precise are also improved in the proposed modified ABC algorithm. The optimization experimental results for high-dimensional benchmark functions indicate that the proposed modification strategies are simple and effective with better convergence speed and optimization precise.

Abstract:Rife algorithm is a classical algorithm for frequency estimation of sine wave. Its drawback is that the estimation performance is poor when the true frequency is close to the frequency quantization points or the SNR is low. After analyzing the theory of Zoom-FFT, its amplification function for spectrum is verified. Furthermore, an improved frequency estimation algorithm based on Zoom-FFT is proposed. After the Zoom-FFT process, spectrum of a narrowband centered on true frequency is amplified, and then Rife algorithm is used to finish the accurate estimation of signal frequency. Simulation results indicate that the improved Rife algorithm has higher estimation accuracy and better anti-noise performance than Rife algorithm and some other improved algorithms. Moreover, it is insensitive to the position of the true frequency in the spectrum. However, to a certain extent, the computation load is improved.

Abstract:For the problem of the small target and the weak contrast of UAV image, we propose a method for minimal target detection based on simple linear iterative clustering (SLIC) hierarchical segmentation. Firstly, pretreatment methods are utilized to improve the contrast of the original image, and Top-hat fusion is used as initial segmentation to detect the initial target area. Then SLIC s egmentation method is utilized to obtain the fine segmentation, and improved density-based spatial clustering of applications with noise(DBSCAN) is introduced to accomplish ultra-pixel classification according to the segmentation result. Finally, the target is detected through feature matching by extracting the neighborhood entropy of the target and other low-level features. Also a detection strategy combining global detection and local detection is proposed to accelerate the detection speed. The experimental results show that the proposed method can improve the detection performance for the minimal targets in UAV image and accelerate the detection speed.

Abstract:Speckle noises in medical ultrasound image would decrease the quality of image and bring difficulties to the analysis and diagnosis of the subsequent image. To reduce speckle, here we propose a speckle reduction algorithm based on the adaptive Gauss filter. The algorithm distinguished the image with speckle regions and characteristic regions by a similarity deprived from local characteristic matching between the processing window and a reference speckle area. According to the similarity, this algorithm adjustes adaptively the width of the Gauss filter. Ultrasound phantom testing and in vivo imaging show that the proposed method is effective. It can reduce the numbers of iteration significantly, as well as the speckle and preserve edge.

Abstract:Most of existing two-dimensional compressed sensing and reconstruction methods for images are complemented by utilizing one-dimensional signals compressed sensing and reconstruction algorithms, which is inefficient and increases memory requirements. Two-dimensional random projection theory and two-dimensional projected gradient algorithm can overcome these disadvantages. However, fixed threshold parameter used in two-dimensional projected gradient algorithm for different images at different sampling rate may lead to poor reconstruction quality. Here, we propose an adaptive two-dimensional projected gradient algorithm based on image texture property. The parameter η of bivariate shrinkage is calculated according to image texture information during the iterative reconstruction process. Experimental results show that compared with two-dimensional projected gradient algorithm, the proposed adaptive twodimension projected gradient algorithm provides superior performance on both the image reconstruction quality and visual effect.

Abstract:The high similarity between active deceptive jamming and echo of air surveillance radar results in the difficulty in identificating the active deception jamming. Based on jammer′s power amplifier nonlinear characters, a new active deceptive jamming identification method is proposed by extracting piecewise autocorrelation maximum variance and singular value spectral entropy of radar receiving signal. Simulation shows that the recognition probability for deceptive jamming is 100% with signal-to-noise ratio of 8 dB, meeting the actual engineering needs.

Abstract:When selecting method of flame′s colors and spatial-temporal features in general illumination in order to find the best feature adapting to the detection of the flame area, PCA feature selection method is used to reduce characteristics of each color channel in different color characteristics. The Relief feature selection method is used to determine the optimal classification for these candidate color and moving subsets. Moreover, contribution ratio of discrimination of all selected colors and moving features is validated by covariance matrix. In the course of the experiment, three sets of fusion features are used for analysis. Experimental results show that the flame detection system based on selected color and moving feature has higher accuracy and recognition efficacy.

Abstract:In the advantages of low memory requirement and high flexibility, sub-aperture method is effective in solving the problem of azimuth spectrum aliasing in sliding spotlight SAR imaging. Image quality and imaging efficiency are affected by sub-aperture partition. However, sub-aperture division usually relies on experience value, which cannot be given with a clear theory analysis at present. Therefore, spectrum gaps may appear in the squint imaging processing if sub-apertures are not properly divided. First, we introduce sliding spotlight SAR model and analyze its Doppler frequency history. A sub-aperture imaging algorithm for squinted sliding spotlight SAR is proposed based on azimuth frequency domain scaling. Then according to two-dimensional spectrum of the adjacent sub-apertures, we investigate the sub-aperture spectrum stitching principle and derive the two equations, thus determine sub-aperture length and overlap rate. Afterward the flow for partitioning sub-apertures is given. Finally, effectiveness of the sub-aperture partition method and squinted imaging algorithm is verified by simulation experiments and real data.

Abstract:A key factor degrading the performance of airborne weather radar is clutter. Here, the change amount of latitude and longitude is converted into the distance variation to compute electromagnetic scattering of different terrain using a digital elevation model (DEM). The calculation of depression and grazing angle is fixed. According to the meteorological radar equation, the ground clutter is modeled. Radar parameters are set in accordance with the actual parameters of WXR-2100, while flight parameters by the quick access recorder (QAR). The clutter maps for airborne weather radar in the different operating modes are simulated during take-off and cruise stages. The results can reflect actual operating conditions. Finally, the clutter map database is established.

Abstract:Orthogonal frequency division multiplexing (OFDM) could efficiently tackle the problem of frequency-selective fading by adopting multiple subcarriers to be transmitted upon different subbands. Subcarriers with weak channel qualities suffer severe symbol errors, which limits the overall system performance. Suppressing subcarriers with bad channel qualities would solve the problem. Here, a joint subcarriers suppression and opportunistic network coding (JSSONC) scheme is proposed for OFDM data broadcasting systems, where subcarriers suppression technique is combined with network coding (NC) technique. By using JSSONC, the base station suppresses the subcarriers being in bad channel conditions. Consequently, retransmission packets used to recover lost packets are produced by adopting network coding to reduce further the number of transmissions. Simulation results show that the proposed JSSONC in OFDM data broadcasting systems reduces the average transmission times of every data package (ATT-E), compared with the traditional OFDM broadcasting systems,thus improving the system transmission efficiency.

Abstract:How to automatically discover salient objects in video and further perform accurate object segmentation is a challenging problem in computer vision. Here, fast salient object segmentation method based on edge-preserving filtering is proposed. Firstly, the salient object discovery is formulated as an energy minimization problem, which fuses the appearance and motion features. Then, a Markov random field (MRF) model, integrating the Gaussian mixture model (GMM) of appearance, the location prior, and the spatial-temporal smoothness, is constructed for accurate segmentation, and is efficiently optimized by graph cut. Moreover, an edge-preserving-based method is presented to improve the segmentation efficiency with a little loss of accuracy. Finally, extensive experiments on two datasets suggest that the proposed method performance is better than that of other five methods, and the accelerated version can speed up to 2 times of the original one.

Abstract:The defocusing of moving target and the reduction of signal clutter noise ratio (SCNR) caused by long coherent time in high-resolution SAR lead to increasing difficulty of moving target detection. In view of this situation, we propose an SAR/GMTI moving target detection algorithm based on autofocus and variable threshold constant false alarm rate(CFAR) detection. To increase SCNR of moving targets, this algorithm makes use of the fixed relationship between range phase error and azimuth phase error to estimate and compensate two-dimensional phase error of moving target. CFAR detection with different thresholds is also realized to detect moving target and exclude the false alarm, respectively. Experimental results verify the feasibility of this algorithm that it can detect moving target and reduce false alarm ratio sufficiently.

Abstract:Remote sensing image fusion is one of the effective ways to solve multi-source remote sensing data integrated performance. ASTER multi-spectral image is better at spectral information than TM and CBERS-1, except for the lack of spatial resolution. The fusion of SPOT pan and ASTER multispectral is significant. Here, we choose the PCA transform, Brovey transform, Gram-Schmidt (GS) transform and wavelet transform fusion method to compare. The results show that the PCA transform and GS transform are suitable for the fusion of SPOT5 pan and ASTER multi-spectral image.

Abstract:A new noise reduction algorithm based on acoustic scene classification is proposed. Three acoustic scenes of pure speech, noise, noisy speech are classified by modulation filter. The parameters of noise reduction algorithm are adjusted by the result of scene classification. Different attenuation coefficient is adopted according to the different acoustic scene. Satisfied experimental results are achieved in the digital hearing aid testing system. Better than 95% accuracy is acquired in acoustic scene classification experiment. In the environment of different kinds of noises input, the signal-noise ratio (SNR) and MOS score are increased apparently. The quality of output speech in digital hearing aids is improved effectively.

Abstract:Facial expression synthesis is important for affective computing. Here, we investigate various facial and expressions features. By abstracting specific organ changes, several principles for the mesh partition of a human face are provided. A new mesh partition technique is then introduced for the synthesis of human expression . By simply positioning, the new technique divides organs of the human faces with polynomial curves. In consequence, every facial organ can be adjusted separately. The existing definition of six basic facial expressions are simplified. With this mesh generation technique, a new method to generate facial expression is obtained. At the same time, we try a step to add skin texture so that the generated expression can be enhanced. Compared with the existing facial expression synthesis methods, the proposed method is simpler in mesh partition, more flexible in parameter setting, and easier to be coded with less computational cost. Subjective evaluation shows that the generated facial expressions have a good visual effect.

Abstract:The study on extraction of Vietnamese news event elements is rare, while Vietnam is a significant neighboring country with political, military and economic cooperation, which is just at a distance of a river with us. According to the Vietnamese characteristics, this paper puts forward a method of Vietnamese news event element extraction based on maximum entropy model. This method selects the context, adjacent trigger words and neighboring entities as features, delimits feature templates, trains Vietnamese news events model and achieves the extraction of news event elements of Vietnamese on the basis of the characteristics of the Vietnamese sentence structure and lexical semantic using the maximum entropy algorithm. The experimental result of the extraction shows that the accuracy of the news event elements extracted by the method proposed in this paper reaches more than 80%.

Abstract:As the premise and basis of text sentimental analysis, the emotion polarity discrimination of lexicons is particularly important. Existing methods of select basic sentimental lexicons in the study of semantic tendency are mostly based on artificial discrimination and lexicons frequency. Those ways suffer the defects of randomness and subjectivity. And it is difficult to ensure the full coverage of the semantic relations in the dictionary. In the paper, we present a method that treats the candidate basic sentimental lexicons as the vertex and the HowNet acquaintance as edge weight to build sentimental lexicons undirected graph. The betweeness-centrality value of nodes in the graph is used as the reference of basic lexicons selecting. Thus we can ensure the reliability of the selected basic lexicons. Experiments show our method has a high accuracy in the classification of emotional tendencies.