Abstract:Radar active jamming brings a huge challenge for radar target detection, tracking and identification. This makes the development of radar electronic counter-counter measurements(ECCM) against active jamming urgent. The ECCM techniques concern about every procedure of Radar. This paper reviews the ECCM techniques including system design, waveform design, antenna design, signal processing and data processing. The latest developments of relative algorithms are discussed, and several major problems as well as the existing obstacles in Radar ECCM are introduced. The performance evaluation methods and their shortage are also briefly introduced. The feasible solutions to overcome these obstacles are given accordingly. In the end, the perspective and application of radar ECCM against active jamming are proposed.

Abstract:As an effective technology for earth remote sensing, synthetic aperture radar (SAR) provides high-resolution, wide-swath, day-and-night and weather-independent images for observation region, which has been widely used for military reconnaissance, environmental monitoring, geological mapping, et al. With the developments of radar technology and geoscience, more target characteristics are expected to be acquired. Conventonal single-polarimetric SAR has been difficult to meet diversified requirements for practical applications. Based on multi-polarization, polarimetric synthetic aperture radar (PolSAR) can obtain target characteristics under different polarization state. PolSAR enriches information of SAR images, whose applications are further expanded. Scattering mechanism interpretation is an important bridge between the collected data and real applications. In this paper, the research progress of PolSAR is firstly reviewed, with emphasis on polarimetric target decomposition. Processing results of real data is presented. Finally, some possible directions for future development are proposed.

Abstract:Sampling is the key procedure for the digital processing of analog signal. In recent years, signal processing methods employing traditional sampling mechanism have faced tremendous challenges due to the rapid growth of signal bandwidth and information transmission rate, and some new signal processing technology such as the wavelet transform and the compressed sensing emerged at the right moment. On this occasion, it is necessary to reexamine the classical Shannon-Nyquist sampling theorem in theory, and study universal expression, sampling and reconstitution theory of signal. The nature of signal expression is analyzed from the point of view of signal projection and function representation. Firstly, the Shannon traditional sampling and reconstruction theory, and the generalized sampling and reconstruction theory proposed by Papoulis and extended by Unser are introduced. Then, the consistency between modern signal processing and transforming methods (wavelet transform, compressed sensing) and generalized sampling theory is investigated mathematically. Meanwhile, the chirp signal is taken as a simulation example to illustrate the relationship between signal sampling and reconstruction, as well as the similarities and differences in each method.

Abstract:Dihedral corner effect is an important characteristic of SAR images, especially for the strong scatters generated by buildings and other human-made objects. Changing the opening angle of the dihedrals is a popular method to reduce radar cross section(RCS). Bistatic radar, which has flexible geometric configuration, is a good way to detect stealth targets. This paper analyzes the bistatic scattering characteristic of different dihedral corner reflectors using geometrical optics methods. As for the multiple scattering effect in the dihedral corners, the general term formula is given to describe the multiple scattering times and echo angles. Besides, the RCS calculation formula of dihedral corner is extended to bistatic condition. Both theory and simulation results show that bistatic radar has the ability to detect changed dihedral corners and its bistatic SAR image formation is a strong point at the corner point under a certain bistatic angle.

Abstract:In order to characterize the radar resolution, the original two dimensional ambiguity function is proposed to provide a conservation estimate for the joint distance-velocity resolution. In this paper, the ambiguity function is extended to three dimensions. So that, the array radar system can evaluate the radar resolution of the waveform in three dimensional of distance,velocity and azimuth angle. So the ambiguity function can represent the resolution ability between the interference and the target. The simulation experiment and performance analysis prove the validity and rationality of three dimensional ambiguity function.

Abstract:A method dealing with direction of arrival(DOA) of coherent signals in the presence of mutual coupling is proposed. Firstly, the generalized steering vector is obtained through DOA matrix method based on the second-order statistics. Secondly, subspace smoothing is applied to generalized steering vector and a linear constrained programming problem is obtained by matrix transform. So DOA and coefficients of mutual coupling can be estimated sequentially. Compared with the fourth-order cumulant method, the computation load is reduced with the second-order statistics. And there is no loss of array elements, which means less array elements are needed in this method.Computer simulations prove the effectivity of the proposed method.

Abstract:The non-ideal properties of a filter lead to channelization distortion, which affects signal detection. In order to achieve high precision signal parameters estimation and waveform recovery accurately, a method of channelized detection of WLFM signal based on basis pursuit is presented. The method uses l1 norm sparse regular least squares model and deduces the SOCP form of the model. It expands the output signals of each subband over complete Gabor atom dictionary and achieves the parameter estimation and decomposition and reconstruction of the original signal. Simulation results show that the proposed method realizes the accurate time-frequency analysis　and parameter estimation of the signal, reconstruct original wide band signal through sparse atoms (less data) and reduces the impact of channelization distortion to some extent.

Abstract:A new ESPRIT based direction of departure(DOD） and direction of arrival (DOA) estimation method for bistatic multi-input multi-output (MIMO) radar with unknown mutual coupling is proposed, in connection with the presence of unknown mutual coupling which severely affects the performance of DOA and DOD estimation. The proposed method can estimate the DOA and DOD accurately by utilizing the structural properties of antenna array matrix and the strip feature of mutual coupling matrix of uniform linear array (ULA). Moreover, the new method is free of search with a small amount of calculation. Even in low SNR environment or mutual coupling effect is significant, the performance of the proposed method is also stable. Simulation results illustrate the feasibility and correctness of the proposed method.

Abstract:In wireless sensor networks (WSN), the existing receiver-initiated MAC protocol based on asynchronous duty cycling, is almost using pseudo-random wakeup schedule of neighbor nodes, in predicting the receiving node wakeup time. In dynamic load conditions in the network, this method cannot change the wakeup interval of node dynamically, thus leading to higher data conflict rates while transmitting and larger data transmission delay. To solve this problem, the paper presents high energy efficiency and low delay MAC (HELD-MAC ), a new protocol that can ensure a different wakeup time between the nodes and predicate the wakeup time of receiving node accurately. The protocol can change the wakeup interval of node dynamically according to the load conditions. Meanwhile, in order to alleviate the problem of energy hole, the protocol can adaptively change the lowest node wakeup interval according to the residual energy of the node. The simulation show that, the HELD -MAC protocol has obvious advantages in the performance of data transmission delay, power consumption, network throughput and transmission collision, compared with RI -MAC and PW-MAC protocols.

Abstract:Ceramics appraisal is a hot topic in field of cultural relic collection. Traditionally, there are mainly two types of ceramics appraisal methods, which are experience-based and technology-based methods. In practice, both methods will cause high cost and time-consuming. A novel vision-based method, which is mainly inspired by the idea of biometrics recognition techniques, is proposed to achieve efficiently verification of the identity of a ceramics. In this method, the microscopic information of a ceramics captured by a digital microscope camera is used as the characteristics for verification. In technical detail, speeded up robust features(SURF) is first employed to align the probe image to the gallery images. Local binary patterns(LBP) features are then extracted from the two aligned images. Finally, Chi-square distance is calculated to measure the similarity between probe and gallery. Experiments on the dataset constructed by this paper demonstrate the state-of-the-art performance of proposed method.

Abstract:In order to discover and employ more spectrum opportunities for the secondary users in cognitive radio networks, a multi-channel spectrum sensing period optimization algorithm is proposed. The main idea of this method is to set unequal sensing period for each licensed channel, which has different usage pattern. We first construct the multi-channel states transition model by alternating renewal theory. Secondly, based on the continuous-time Markov chain, the choice of the multi-channel sensing period is modeled as a constrained multi-objective optimization problem. Finally, the multi-objective optimization problem is solved by genetic algorithms. The simulation results validate the performance of the derived objective function. When there are eight licensed channels in the target network, the proposed algorithm discovers 68.23% of free spectrum opportunities, which brings up to 17.68% more opportunities than the OFDM sensing method.

Abstract:With the increasingly fierce struggle of electronic countermeasures in the modern battlefield, the environment of radar falls under serious threat. Radio frequency(RF) stealth technology is an important approach to improve the viabilities of radar and its platform in the battlefield. In this paper, the problem of RF stealth in airborne radar system is investigated, and a novel optimal resource management algorithm with passive sensor cooperation in airborne radar system is proposed. The sequential filtering algorithm based on interacting multiple model (IMM) and extended Kalman filter (EKF) is employed for target tracking, and the passive sensor is utilized in preference to radar. In the proposed algorithm, the comparison of the predicted state estimation covariance and the predefined threshold of covariance is used to control radar under an intermittent-working state. Furthermore, the radiation energy is adaptively adjusted to improve the RF stealth performance according to the target motion. Numerical simulation results demonstrate that the proposed algorithm can effectively configure radar working parameters and improve RF stealth performance.

Abstract:To reduce the influence of aspect angle to synthetic aperture radar (SAR) object recognition and improve recognition rate of SAR distorted object, the algorithm of compressed sensing (CS) and support vector machine (SVM ) decision fusion for SAR object recognition is proposed. SAR object recognition is described as a sparse signal recovery problem in CS based on sparse representation theory, and an object classification result and an aspect angle are obtained through sparse coefficient separately. The classification results are obtained by SVM classifier using rectified and original samples after rectifying the pose of test sample. The final recognition result is obtained through fusion of the three above results based on majority vote. Experimental results demonstrate that, the algorithm of object aspect angle estimation based on compressed sensing result is effective, and the proposed decision fusion algorithm improves deformable object recognition rate significantly as the sample number increases .

Abstract:Conformal arrays, with more flexible placement than traditional array, are widely used in many areas. However, pattern synthesis of conformal array is more complex and some traditional pattern synthesis methods are not applicable. Applying virtual interference in the region of the side lobe is proved to be feasible. Then the critical issue is to find effective methods to determine the power of virtual interference in each azimuth through an iterative process. Generally, existing iterative methods have slow convergence rate or heavy computation. An improved method for conformal array pattern synthesis is proposed based on linearly constrained minimum variance (LCMV) criterion and the method is applicable to any array placement. Moreover, it can accelerate the convergence rate and reduce the dependence on the iteration coefficient for adaptive beam-formers. Numerical examples are provided to demonstrate the effectiveness of the method.

Abstract:Affinity propagation（AP） clustering algorithm considers all clustering objects as potential clustering centers, and messages of responsibility and availability are exchanged between objects until a highquality set of clustering centers and corresponding clusters gradually emerge. But it is not appropriate for subspace clustering. To solve this problem, an entropy weighting AP algorithm for subspace clustering based on asynchronous granulation of attributes and samples (EWAP) is put forward through introducing the idea of granular computing into the affinity propagation clustering method. It removes the redundant attributes first, and then a step of modifying attribute weights is added to the clustering procedure for obtaining the exact weights value. At the end of iteration, the attribute weights of each subspace, an accurate result of attributes granularity and the corresponding clusters will be produced. The theory and practice prove that EWAP preserves the advantages of AP clustering and overcomes its shortage of unsatisfying subspace clustering.

Abstract:Airborne radar with uniform circular array (UCA) antennas has the merits of all-orientation space scanning, joint elevation-azimuth estimation, and good beamform when the beam center deviates from the normal of the array. But the clutter distribution of UCA varied with ranges and received data in different range cells are not independently identically distributed (IID) samples for its geometry specialty, which results in the significant degradation in traditional space time adaptive processing (STAP) method. A new STAP method, namely exponent derivative based updating algorithm, is proposed. In the method, the change of adaptive weight is supposed as an exponential function and the range-dependency of UCA can be effectively reduced by extending the received data vectors. Simulation results verify that the proposed method outperforms significantly both localized processing and range-dependency compensation methods, as well as the traditional derivative-based updating algorithm.

Abstract:To validly detect the anomalies of parameters in the engine test, a fault detection algorithm of engine based on time series data mining is proposed. The parameter time series are transformed into symbolic strings by a representation method based on shape features. The stable states and transition states are extracted from the parameter time series according to symbolic semantics. Meanwhile, the detection algorithm of abnormal pattern from the stable states is realized by similarity measurement between time series based on statistic features, combined with the most unusual pattern discovery method. The results of numerical experiments show that the new method validly detects the fault of engine and has the better robustness than the traditional method.

Abstract:In order to keep the inherent higher order structure and correlation in the original data, reduce the influence of illumination in image recognition, and optimize the weight of the multi-scale feature, the method of image feature extracting based on weighted multi-scale tensor subspace is proposed to solve the problems. Firstly, multi-scale transform is used to characterize each place feature of the image, and uncertainty weighed is adopted on the role of each scale feature for image classification. And then a multi-scale tensor space is built using multiple linear principal component analysis and linear discriminant analysis algorithm to reduce the cost of processing, preserving the inherent structure and correlation of high-dimensional data. Finally, the extraction of the image features is completed. CAS-PEAL-R1 oriental face database is chosen for evaluation. The experimental results show that the algorithm performs better than some recent algorithms for image recognition with practical feasibility.

Abstract:A tracking before detection(TBD) tracking method for minimal targets tracking in unmanned aerial vehicle (UAV) visible image based on improved Kalman filter is presented. Firstly, detected target obtained by detecting algorithm is used as the measurement value of Kalman filter. Parameters of matching similarity in the detection process is used as an important reference for measurement noise covariance matrix of Kalman filter. Secondly, in the tracking module, tracking framework based on Kalman filter is established to predict the target position in next frame. Finally, targets are searched by detection module in local area accor ding to predictive position. In addition, in order to improve the tracking efficiency, accumulation error between detection position and predictive position is calculated to choose detection mode. Global detection mode is taken if accumulation error is greater than the given threshold and accumulation error is set to be zero, or local detection mode is done. The strategy can effectively reduce computational complexity of the TBD tracking method. Simulation experiment results show that the method can obtain the better performance of detection and tracking than that of classic Kalman filter.

Abstract:To improve the accuracy of lie detection and make the lie detection conclusion as the approved evidence in criminal proceedings, a low-cost scheme which uses eye movement as a cue for lie detection is proposed. First, a low-cost eye movement recording system is used to record eye movement signals, a segmented weighting-annular hough transform is applied to track the iris, a gradient integral projection algorithm is used to detect blink, and the width of eye opening is estimated by the difference image. Then, a relational model associated the eye movement features and the lying is built. The results of the eye movement feature detection show that the low-cost eye movement recording system is suitable for lie detection, and the results of lie detection show that the proposed model is effective for lie detection or assistant lie detection.

Abstract:A robust wideband constant beamwidth beamforming algorithm is proposed to solve the problem of performance degrade of traditional wideband beamforminers when there exists steering vector error. With the spatial response variation (SRV) constrain, a wideband constant beamwidth beamforming algorithm can be obtained by simplifying the response at different frequencies as a reference frequency. Then the robustness of beamformer can be improved by imposing the quadratic constraint for the weight vector. Finally, the optimal weight with a closed form can be solved on the boundary of uncertainty set using the Lagrangian method. Experiments illustrate that the algorithm obtains a desired result to against the look direction estimation error, and achieves a preferable constant beamwidth performance and a high output signal to interference plus noise ratio.

Abstract:In order to realize low distortion acquisition and processing for wideband radar signal in the wideband radar system, a wideband and low spurious sampling system with amplitude and phase correction is studied and designed. The system uses the implementation scheme of wideband analog to digital converter (ADC) device and high performance field programmable gate array (FPGA) device, which is researched from low jitter sampling clock, low noise power, anti crosstalk for the optimal low spurious performance. To improve the band transmission characteristics of the system, a finite impulse response(FIR) filter is designed based on optimization algorithm. Finally, the designed system is tested in lab, and the experiments results prove that the spurious-free-dynamic-range (SFDR) of the system is characterized as -50 dBc worst-case over an instantaneous bandwidth of more than 800 MHz, sampling rate of 1.8 GS/s, quantization length of 8 bits. System performance levels meet the application requirements of wideband radar signal acquisition, wideband radar target imaging and wideband radar target echo reconstruction.

Abstract:Global energy consumption in wireless sensor networks restricts the application of the entire networks, including the impact of limited energy capacity of a single node to the system fundamentally. This paper presents a systemic modeling approach for wireless sensor network based on radial basis function neural networks and status-sphere expression. In consideration about the topology and hierarchical structure of WSN, it introduces real-time adjusting of radial basis function neural networks, and establishes matrix model for systematic energy consumption adaptively. Results prove that this model performs effective global optimization by adjusting parameters according to real application circumstances.

Abstract:Relief algorithm is a series of feature selection method. It includes the basic principle of Relief algorithm and its later extensions reliefF algotithm. Its core concept is to weight more on features that have essential contributions to classification. Relief algorithm is simple and efficient, thus being widely used. However, algorithm performance is not satisfied when applying the algorithm to noisy and unbalanced datasets. In this paper, based on the Relief algorithm, a feature selection method is proposed, called threshold-Relief algorithm, which eliminates the influence of noisy data on classification results. Combining with the K-means algorithm, two unbalanced datasets feature selection methods are proposed, called K-means-ReliefF algorithm and K-means-relief sampling algorithm, respectively, which can compensate for the poor performance of Relief algorithm in unbalanced datasets. Experiments show the effectiveness of the proposed algorithms.