The time-modulated array has higher flexibility than the traditional antenna array. However, the time-modulated array is sensitive to random errors. In this paper, the time-modulated uniform linear array with element position errors is discussed. Position errors can not only lower the performance of the array but also lift the side-lobe and increase the dynamic range of the side-lobe. Aimed at this issue, a robust pattern synthesis algorithm is proposed, and simulation results are given to illustrate the effectiveness and the robustness of the proposed algorithm.

Simultaneous feature selection and clustering is a major challenge in unsupervised learning. In particular, there has been significant research into saliency measures for features that result in good clustering. However, as datasets become larger and more complex, there is a need to adopt a finer-grained approach to saliency by measuring it in relation to a part of a model. Another issue is learning the feature saliency and advanced model parameters. We address the first by presenting a novel Gaussian mixture model, which explicitly models the dependency of individual mixture components on each feature giving a new component-based feature saliency measure. For the second, we use Markov Chain Monte Carlo sampling to estimate the model and hidden variables. Using a synthetic dataset, we demonstrate the superiority of our approach, in terms of clustering accuracy and model parameter estimation, over an approach using a model-based feature saliency with expectation maximisation. We performed an evaluation of our approach with six synthetic trajectory datasets obtaining an average clustering accuracy of 97 percent. To demonstrate the generality of our approach, we applied it to a network traffic flow dataset obtaining an accuracy of 93 percent for intrusion detection. Finally, we performed a comparison with state-of-the-art clustering techniques using three real-world trajectory datasets of vehicle traffic. Our approach achieved an average clustering accuracy of 96 percent compared to 77-95 percent for the other techniques. In conclusion, for the datasets considered, component based feature saliency measures gave improved clustering over those based on whole models.

In the era of big data, data contain great value and become important strategic resource in today??s information society. However, a large number of inconsistent data occur during the process of data update and management, which causes unpredictable side effects for enterprises. There are three repair methods based on functional dependencies. The first two methods strongly rely on the Master data or confidence value of given tuples provided by enterprises, which are hard to fulfill in real application. And the third kind of repair method based on the minimal deletion principle will cause the loss of information. Moreover, when solving the conflicts of [X→Y], existing methods only support modifying Y attribute. In view of the shortcomings mentioned above, with the situation of missing tuple confidence, this paper proposes an increased data repair with confidence value token, which can be divided into two parts: the first part is to generate confidence value token automatically by analyzing operator log and knowledge rules, and the second part includes an increased repair strategy which can determine the repair of X or Y attributes according to the confidence value token. Meanwhile, the target value is chosen to repair dirty data with the combination of conditional probability. Experimental results show that the proposed method has high reliability and scalability.

作为检测数据集中不一致数据的方法,函数依赖受到了广泛的关注.近年来,硬约束、等值约束、编辑规则、差分约束等被相继提出,用于发现更多的不一致数据.然而,这些约束规则仅适用于静态数据集中不一致数据的检测,而实际应用中,存在着大量随时间演化的动态数据,已有的规则忽略了具有时态语义数据的描述.该文首先提出了时态数据质量规则的形式化表达,为了提升检测效率,给出一套规则相关的性质,利用性质去除规则集中冗余规则;其次给出了不一致数据检测算法,并通过剪枝的策略对算法优化,再利用算法和不一致数据查询语言获取冲突数据;最后,通过实验验证,本文提出的方法能够检测出更多的不一致数据,经过优化后的算法执行效率较高.

SAR目标回波模拟器可以真实模拟SAR信号及不同环境下的回波数据,对SAR系统的研制和其性能参数的测试起着关键作用.通常SAR回波模拟器需要对各像素点进行计算,数据量和计算量都很大.本系统设计的合成孔径雷达SAR目标回波模拟器,利用多核DSP和高性能FPGA的信号处理阵列单元,采用等距离算法,将回波位于同一距离单元的像素点信息相加后再在距离维统一乘以距离向相位信息,大大减少了回波模拟算法的计算量,最终实现的SAR回波模拟器系统带宽与运行速度都有显著提高.

In order to realize the beamforming and suppress the side lobe of the sparse antenna array, a hybrid sparse antenna array optimization algorithm based on orthogonal perturbation method (OPM) and convex (CVX) optimization is proposed. The algorithm aims at the expected beam response of main lobe and the suppression of peak side lobe level (PSLL). The OPM is used to optimize the array layout, and it can improve the time performance of pattern synthesis with its deterministic numerical characteristic. Because it is a local optimization algorithm, its optimal effect depends on the initial array layout and element excitation coefficient. Therefore, considering that the local optimal solution of the CVX algorithm is the global optimal solution, the CVX algorithm is used to obtain the optimal element excitation coefficient to achieve the expected beam response of the main lobe. The simulation results show that the hybrid algorithm can achieve a good trade-off between the optimization time, beam response, and the suppression of the PSLL.

航拍视角下的地面交通车辆目标自主检测是智能交通系统中获取交通信息的新兴手段.近年来,随着深度学习在诸多领域应用取得显著的成功,卷积神经网络也开始应用于视频图像的目标检测中.针对航拍图像下的较小车辆目标,结合密集的拓扑结构和最优的池化策略,论文重构了深度卷积神经网络模型,重点强化网络的特征提取能力,用于提升小目标检测性能.论文提出的检测模型在NVIDIA 1080ti平台上,对航拍图像不同类型的车辆目标检测进行了实验仿真.实验结果表明,提出的检测方法对较小目标检测能力鲁棒性高,快速有效,并实现了实时检测.

This paper addresses the constrained multiobjective optimization problem of time-modulated sparse arrays. The synthesis objective is to find an optimal element arrangement and associated excitation strategy of sparse arrays, which realize the balance of radiation power and sideband suppression performance with minimum number of elements, and suppress side lobe level simultaneously. A novel hybrid algorithm based on orthogonal perturbation method and convex optimization (OPM-CVX) for the synthesis of time-modulated sparse antenna array is presented in this paper. In order to satisfy the main lobe beamforming and side lobe suppression of sparse arrays, the proposed method optimizes element positions with minimum array numbers by orthogonal perturbation method and optimizes excitations of array element with dynamic range ratio constraint by convex optimization. Furthermore, a trapezoidal pulse time-modulated switching function is proposed to find the balance of radiation power and sideband suppression performance. The numerical results indicate that the proposed algorithm can be an effective approach for synthesis problems of time-modulated sparse arrays.

In this paper, pattern synthesis through time-modulated linear array is studied, and a novel strategy for harmonic beamforming in time-modulated array is proposed. The peak side lobe level is designed as optimization objective function, and the switch-on time sequence of each element is selected as optimization variable. An improved invasive weed optimization (IWO) algorithm is developed in order to determine the optimal parameters describing the pulse sequence used to modulate the excitation weights of array elements. Representative results are reported and discussed to point out potentialities and advantages of the proposed approach, which can obtain lower objective function values.

An efficient pattern synthesis approach is proposed for the synthesis of a time-modulated sparse linear array (TMSLA) in this paper. Due to the introduction of time modulation, the low/ultralow side lobe level can be obtained with a low amplitude dynamic range ratio. Besides, it helps reduce the difficulty of antenna feeding system effectively. Based on particle swarm optimization (PSO) and convex (CVX) optimization, this paper proposes a hybrid optimization method to suppress the grating lobes of the sparse arrays, peak side lobe level (PSLL), and peak sideband level (PSBL). Firstly, the paper utilizes the CVX optimization as a local optimization algorithm to optimize the elements' switch-on duration time, which reduces the side lobe of the array. Secondly, with the PSBL as the objective function, the paper adopts the PSO as a global optimization algorithm to optimize the elements' positions and switch-on time instant, which helps reduce the loss of sideband power caused by time modulation. With respect to the time modulation model, variable aperture sizes (VAS) and more flexible pulse-shifting (PS) schemes are used in this paper. Owing to the introduction of time modulation and CVX optimization, the proposed method is much more feasible and efficient than conventional approaches. Furthermore, it has better array pattern synthesis performance. Numerical examples of the TMSLA and comparisons with the reference are presented to demonstrate the effectiveness of the proposed method.

This paper addresses the constrained multi-objective optimization problem of sparse conformal arrays designing. The objective of array synthesis is to find an optimal element arrangement on a conformal surface and its associated excitation strategy, which generate the main radiation beam along a pre-selected spatial direction with maximum gain and, simultaneously, suppress sidelobe levels elsewhere. A hybrid algorithm particle swarm optimization (PSO)-second-order cone programming (SOCP), comprising of PSO and SOCP, each for a dedicated purpose, is proposed to fulfill this task in this paper. More specifically, the PSO algorithm is introduced to optimize sparse conformal array element positions, whereas the SOCP is applied to seek optimal excitation coefficients for each array layout obtained. After extensive simulation with the examples of sparse circular array and sparse conical arrays, we can find that our proposed method can synthesize better radiation patterns with regard to peak sidelobe levels, compared with those obtained through other traditional algorithms.

Based on Particle Swarm Optimization (PSO) and Second-Order Cone Programming (SOCP) algorithm, this paper proposes a hybrid optimization method to suppress the grating lobes of sparse arrays and improve the robustness of array layout. With the peak side-lobe level (PSLL) as the objective function, the paper adopts the particle swarm optimization as a global optimization algorithm to optimize the elements' positions, the convex optimization as a local optimization algorithm to optimize the elements' weights. The effectiveness of the grating lobes suppression (as low as -32.13 dB) by this method is illustrated through its application to the sparse linear array when the actual steering vector is known. To enhance the robustness of the optimized array, a rebuilt robust convex optimization model is adopted in the optimization of both array excitations and layout. When the array manifold mismatch error is 1 cm, the PSLL by the robust algorithm can be compressed to -27 dB, compared to that of -24 dB by the ordinary optimization. Results of a set of representative numerical experiments show that the algorithm proposed in this paper can obtain a more robust array layout and matched elements' weight coefficients to avoid the huge degradation of the array pattern performance in the presence of array manifold mismatch errors. The good performance of pattern synthesis demonstrates the effectiveness of the proposed robust algorithm.

针对非均匀稀布圆环阵的旁瓣抑制问题提出了一种基于粒子群算法和二阶锥规划算法的混合算法.该混昆合算法结合两种算法的优势,将粒子群算法作为全局搜索器进行阵元位置的优化,二阶锥规划算法作为局部搜索器进行阵元权值的优化,能够获取较低的峰值旁瓣电平.该算法同时引入相邻阵元最小间距的约束,优化了算法的搜索空间,提高了寻优效率.最后,考虑到阵列天线系统的可实现性,给出了动态幅值比约束下的混合算法.与粒子群算法和参考文献方法的对比实验结果表明:本文算法可进一步降低稀布圆环阵的旁瓣电平,仿真数据验证了算法的有效性和天线系统的可实现性.

针对稀布天线阵列中带有阵元数目、阵列孔径、阵元间距上下限和峰值旁瓣电平抑制的多重约束问题,提出了一种基于改进和声搜索算法的综合方法.通过和声变量与阵元间距的矢量映射,原始的强约束优化问题可以转换为仅含双边约束的优化问题,从而在保持可行解空间不变的同时提高了问题的自由度.为了加速收敛过程,采用动态调整参数策略.仿真结果表明,与现有文献中的算例进行比较,所提出的方法能够得到更低的峰值旁瓣电平,且鲁棒性好,收敛速度快.

The servo control system is very important for the tracking and flight safety of the aircraft, and the control algorithm determines the performance of the servo control system. In order to improve the performance of the control system, this paper proposes a three closed-loop sliding mode variable structure control method. In this method, the third-order sliding mode variable structure algorithm is proposed by using the current, rotational speed and position feedback of the system as input. This method overcomes the problem of step response integral overshoot and poor response to system parameters perturbation and load disturbance of the traditional PID control. Simulation and experimental results show that the step response overshoot of the system based on the three closed-loop sliding mode control is obviously reduced, the static error under the constant disturbance is eliminated, and the settling time is obviously shortened. This indicates that the control algorithm has strong robustness and can significantly improve the dynamic characteristics and steady-state accuracy of the system.

This paper presents a novel low probability of intercept (LPI) optimization framework in radar network by minimizing the Schleher intercept factor based on minimum mean-square error (MMSE) estimation. MMSE of the estimate of the target scatterer matrix is presented as a metric for the ability to estimate the target scattering characteristic. The LPI optimization problem, which is developed on the basis of a predetermined MMSE threshold, has two variables, including transmitted power and target assignment index. We separated power allocation from target assignment through two sub-problems. First, the optimum power allocation is obtained for each target assignment scheme. Second, target assignment schemes are selected based on the results of power allocation. The main problem of this paper can be considered in the point of views based on two cases, including single radar assigned to each target and two radars assigned to each target. According to simulation results, the proposed algorithm can effectively reduce the total Schleher intercept factor of a radar network, which can make a great contribution to improve the LPI performance of a radar network.

The army manifold mismatch caused by array element position jitter in the practical use of array radar transmitter often results in the reduction of the radiation pattern performances.In order to solve this problem,we presented a robust grating lobes suppression to resist array manifold mismatch based on particle swarm optimization (PSO) and convex optimization to improve the robustness of sparse array layout.With the peak side-lobe level as the objective function,the particle swarm optimization was considered as a global optimization algorithm to optimize the elements'positions while the convex optimization was considered as a local optimization algorithm to optimize the elements' weights.The array manifold mismatch error was introduced to reconstruct a robust convex optimization model,and then the model was used to optimize the robust array layout on the basis of the robust array elements' weights.The simulation results show that the array layout and coefficients after optimization have strong robustness,which can enhance the tolerance of the optimized array against array manifold mismatch while achieving pattern synthesis in demanded direction and suppressing the grating lobes simultaneously.The efficiency of the proposed algorithm has been proved by numerical results.

The Iterative Back Projection(IBP) Super Resolution Reconstruction(SRR) algorithm based on improved Keren registration method uses bilinear interpolation method to get the initial estimations of high-resolution images,which leads to the sawtooth in the edge of the reconstructed image.To solve this problem,an IBP super resolution reconstruction based on New Edge Directed Interpolation(NEDI) is proposed.The NEDI method computes local covariance coefficients of a low-resolution image and uses these covariance estimations to adapt the interpolation at a higher resolution based on the geometric duality between the local covariance of the low-resolution image and that of the high-resolution image.Experimental result indicates that the proposed method can reduce the edge sawtooth,increase Peak Signal to Noise Ratio(PSNR),reduce Root Mean Squared Error(RMSE) and improve the subjective visual effect of the image.

针对道路视频监控中局部特征车辆品牌和型号识别率低的问题,提出了一种离散粒子群优化的识别算法。用形态学定位法提取视频中车前脸区域,能够快速获得识别的感兴趣区域。提取车前脸的SURF特征作为识别局部特征,对视角变化和光线变化有较好的鲁棒性。在离散环境下定义粒子的位置和速度,设计粒子的更新规则,利用离散粒子群优化获得待识别图像特征点在标准图像中的最佳覆盖,提高特征点匹配的正确率,从而提高车型识别的正确率。最后利用具有对应关系的特征的相似度进行对比识别。建立了15种车系76种车型的车前脸图像库进行实验,实验结果表明改进方法的车型正确识别率为93.6%。