基于重构协方差矩阵空时二维杂波抑制方法

针对杂波环境严重非均匀导致协方差矩阵估计失真、空时自适应处理性能下降的问题,提出了一种杂波协方差矩阵重构方法,并利用重构的杂波协方差矩阵实现杂波抑制。该方法充分利用了载机速度给定时杂波分布轨迹已知这一先验知识,考虑了天线方向图的调制,通过改变空时相关函数调节杂波带宽,可以实现任意杂波协方差矩阵重构。计算机仿真分析和实测数据处理结果表明,所提出的方法在非均匀杂波环境中,与小自由度的降维自适应算法相比具有更好的动目标检测性能。     

基于空时二维结构的低旁瓣宽带波束形成

在分析空时自适应处理结构的基础上,提出了一种工程上易于实现的低旁瓣波束形成算法.虚拟了不同幅度、不同频点、不同方位角的干扰源,使得空时二维方向图的主瓣指向期望信号来波方向的同时,在其他方向上形成低旁瓣,达到抑制干扰和噪声的目的.由于此算法权值是已知系数矩阵与当前时刻期望信号的空时二雏导向矢量的乘积,计算量简单,避免了传统线性约束算法中多次矩阵求逆的复杂运算,既保证了抗干扰性能又便于工程实现.     

机载预警雷达三维空时自适应处理及其降维研究

分析了AEW雷达系统中实际存在的阵元幅相误差对二维空时自适应处理(2D STAP)性能的影响。提出了一种更加稳健的三维空时自适应处理(3D STAP)技术,补偿因存在阵元幅相误差而导致各列子阵俯仰方向图的不一致性。为了降低计算量,还提出了一种三维先时后空自适应处理(3D T SAP)的准最优技术。对仿真数据和某实测数据的处理结果证明三维处理具有优良的性能和很强的误差容错能力。     

宽带相控阵雷达信号空时自适应仿真分析

常用的宽带雷达信号有线性调频脉冲信号、13位Barker码二相编码信号和频率步进信号等。在分析以上三种信号模糊函数的基础上，基于相控阵雷达空时自适应信号处理系统，针对以上三种不同信号的空时自适应处理进行仿真分析；在自适应处理过程中采用对角加戴SMI算法，使得自适应系统具有稳健性．仿真结果表明频率步进信号在距离高分辨应用场合中更具优势．     

空时自适应处理对GPS信号影响与补偿方法

通过推导C/A码全球定位系统(global positioning system, GPS)信号分别经空域自适应处理和空时自适应处理的相关函数,研究了阵列信号处理对C/A码GPS信号捕获性能的影响。理论分析表明,空域自适应处理不影响捕获;而空时自适应处理在抑制干扰的同时使信号产生了畸变,降低了同步和码跟踪精度。针对空时自适应处理造成的信号失真,利用维纳滤波对失真的相关函数进行补偿。仿真结果表明补偿后相关函数的波形有了明显改善,空时自适应处理带来的信号失真也得到有效校正。     

基于DPCA的机载雷达杂波抑制系统

介绍了基于相位中心偏置天线(DisplacedPhaseCenterAntenna，DPCA)的机载战场侦察雷达主杂波的抑制系统的设计与实现。该系统具有一定空-时二维处理能力，是空-时自适应处理(Space-TimeAdaptiveProcessing，STAP)的一种简易工程实现。其对地杂波的抑制能力优于AMTI，特别是在动目标速度较慢时，优越性更为明显。通过对回波仿真数据的实际处理，证明了该系统工作的有效性。     

GPS信号空时处理后码跟踪误差的补偿方法

全球卫星定位系统（global position system, GPS）接收机使用空时自适应处理能够增益信号并抑制干扰,但空时自适应处理结构影响GPS信号的定位精度。首先推导空时自适应处理后,GPS信号与参考信号的相关波形,分析了空时处理对码跟踪的影响。然后在码跟踪过程中改进本地参考信号以补偿相关波形的误差,减少空时自适应处理后的码跟踪精度损失。仿真和实测结果验证了算法的有效性。     

空时自适应处理中基于知识的训练 样本选择策略

针对空时自适应处理中样本协方差矩阵受干扰目标污染时检测性能下降的问题，提出了一种基于知识的空时自适应处理（knowledge aided space time adaptive processing, KA STAP）方法。该方法将待测距离单元杂波的先验知识与广义内积非同态检测器（general inner product nonhomogeneity detector, GIP NHD）结合，对训练样本进行有效选择。通过仿真证明该方法能有效剔除存在干扰目标的样本，提高训练样本被干扰目标污染时空时自适应处理的检测性能。     

分布阵列雷达

分布阵列雷达是一种很有发展前途的新的雷达体制构想。它利用多个小雷达相干联网实现有效地搜索与跟踪目标。本文论述了它的一些突出特点和优点,讨论了空时信号处理与天线方向图的一些问题,最后介绍了利用自适应波束形成获得自相干的方法。尽管实现这种体制的雷达还有不少技术难题,但它肯定能得到迅速的发展。     

机载前视阵列雷达俯仰慢时间空时自适应处理

针对机载前视平面阵列雷达杂波抑制问题,提出了一种利用阵列俯仰维自由度和慢时间自由度进行空时二维自适应处理的方法。分析了该模型中杂波在俯仰多普勒平面的分布规律,通过采用多普勒频移方法补偿了杂波的距离非均匀性,与常规方位多普勒空时二维自适应处理方法不同,所提方法可以同时补偿杂波的主瓣和旁瓣。最后通过仿真实验将所提方法与三维空时自适应处理、传统方位慢时间二维自适应处理作了对比,证明了所提方法的优越性。     

一种改进的自适应平方根传递对准滤波算法

提出了一种解决时变噪声条件下传递对准的改进自适应平方根滤波算法。该算法将状态方差调节因子阵、自适应调节的系统噪声和量测噪声方差以及噪声有限记忆尺度融入平方根滤波结构中,采用序列算法滤波解算。算法通过一步控制和多步自适应调节过程,以较少的计算量从数值计算、噪声抑制及自适应调节方面提高滤波性能。仿真结果表明,该算法滤波稳定性强,能够根据实际噪声快速调整,且滤波精度高,为机载导弹提供了一种有效的快速精确传递对准方法。     

Improved filtered- adaptive inverse control and its application on nonlinear ship maneuvering

The drawbacks of common nonlinear Filtered- adaptive inverse control (AIC) method, such as the unreliability due to the change of delay time and the faultiness existing in its disturbance control loop, are discussed. Based on it, the diagram of AIC is amended to accommodate with the characteristic of nonlinear object with time delay. The corresponding Filtered- adaptive algorithm based on RTRL is presented to identify the parameters and design the controller. The simulation results on a nonlinear ship model of “The R.O.V Zeefakkel” show that compared with the previous scheme and adaptive PID control, the improved method not only keeps the same dynamic response performance, but also owns higher robustness and disturbance rejection ability, and it is suitable for the control of nonlinear objects which have higher requirement to the maneuverability under complex disturbance environment.     

Survey on nonlinear reconfigurable flight control

An overview on nonlinear reconfigurable flight control approaches that have been demonstrated in flight-test or highfidelity simulation is presented. Various approaches for reconfigurable flight control systems are considered, including nonlinear dynamic inversion, parameter identification and neural network technologies, backstepping and model predictive control approaches. The recent research work, flight tests, and potential strength and weakness of each approach are discussed objectively in order to give readers and researchers some reference. Finally, possible future directions and open problems in this area are addressed.     

Adaptive link layer security architecture for telecommand communications in space networks

Impressive advances in space technology are enabling complex missions, with potentially significant and long term impacts on human life and activities. In the vision of future space exploration, communication links among planets, satel ites, spacecrafts and crewed vehicles wil be designed according to a new paradigm, known as the disruption tolerant networking. In this scenario, space channel peculiarities impose a massive reengineering of many of the protocols usually adopted in terrestrial networks; among them, security solutions are to be deeply reviewed, and tailored to the specific space requirements. Security is to be provided not only to the payload data exchanged on the network, but also to the telecommands sent to a spacecraft, along possibly differentiated paths. Starting from the secure space telecommand design developed by the Consultative Committee for Space Data Systems as a response to agency-based requirements, an adaptive link layer security architecture is proposed to address some of the chal enges for future space networks. Based on the analysis of the communication environment and the error diffusion properties of the authentication algorithms, a suitable mechanism is proposed to classify frame retransmission requests on the basis of the originating event （error or security attack） and reduce the impact of security operations. An adaptive algorithm to optimize the space control protocol, based on estimates of the time varying space channel, is also presented. The simulation results clearly demonstrate that the proposed architecture is feasible and efficient, especially when facing malicious attacks against frame transmission.     

Underdetermined DOA estimation and blind separation of non-disjoint sources in time-frequency domain based on sparse representation method

This paper deals with the blind separation of nonstation-ary sources and direction-of-arrival （DOA） estimation in the under-determined case, when there are more sources than sensors. We assume the sources to be time-frequency （TF） disjoint to a certain extent. In particular, the number of sources presented at any TF neighborhood is strictly less than that of sensors. We can identify the real number of active sources and achieve separation in any TF neighborhood by the sparse representation method. Compared with the subspace-based algorithm under the same sparseness assumption, which suffers from the extra noise effect since it can-not estimate the true number of active sources, the proposed algorithm can estimate the number of active sources and their cor-responding TF values in any TF neighborhood simultaneously. An-other contribution of this paper is a new estimation procedure for the DOA of sources in the underdetermined case, which combines the TF sparseness of sources and the clustering technique. Sim-ulation results demonstrate the validity and high performance of the proposed algorithm in both blind source separation （BSS） and DOA estimation.     

Knowledge-based adaptive polarimetric detection in heterogeneous clutter

The detection performance and the constant false alarm rate behavior of the conventional adaptive detectors are severely degraded in heterogeneous clutter. This paper designs and analyses a knowledge-based （KB） adaptive polarimetric detector in het-erogeneous clutter. The proposed detection scheme is composed of a data selector using polarization knowledge and an adaptive polarization detector using training data. A polarization data selector based on the maximum likelihood estimation is proposed to remove outliers from the heterogeneous training data. This selector can remove outliers effectively, thus the training data is purified for estimating the clutter covariance matrix. Consequently, the performance of the adaptive detector is improved. We assess the performance of the KB adaptive polarimetric detector and the adaptive polarimetric detector without a data selector using simulated data and IPIX radar data. The results show that the KB adaptive polarization detector outperforms its non-KB counterparts.     

FBFN-based adaptive repetitive control of nonlinearly parameterized systems

An adaptive repetitive control scheme is presented for a class of nonlinearly parameterized systems based on the fuzzy basis function network （FBFN）. The parameters of the fuzzy rules are tuned with adaptive schemes. To attenuate chattering effectively, the discontinuous control term is approximated by an adaptive PI control structure. The bound of the discontinuous control term is assumed to be unknown and estimated by an adaptive mechanism. Based on the Lyapunov stability theory, an adaptive repetitive control law is proposed to guarantee the closed-loop stability and the tracking performance. By means of FBFNs, which avoid the nonlinear parameterization from entering into the adaptive repetitive control, the controller singularity problem is solved. The proposed approach does not require an exact structure of the system dynamics, and the proposed controller is utilized to control a model of permanent-magnet linear synchronous motor subject to significant disturbances and parameter uncertainties. The simulation results demonstrate the effectiveness of the proposed method.     

Test selection and optimization for PHM based on failure evolution mechanism model

The test selection and optimization （TSO） can improve the abilities of fault diagnosis, prognosis and health-state evalua- tion for prognostics and health management （PHM） systems. Traditionally, TSO mainly focuses on fault detection and isolation, but they cannot provide an effective guide for the design for testability （DFT） to improve the PHM performance level. To solve the problem, a model of TSO for PHM systems is proposed. Firstly, through integrating the characteristics of fault severity and propa- gation time, and analyzing the test timing and sensitivity, a testability model based on failure evolution mechanism model （FEMM） for PHM systems is built up. This model describes the fault evolution- test dependency using the fault-symptom parameter matrix and symptom parameter-test matrix. Secondly, a novel method of in- herent testability analysis for PHM systems is developed based on the above information. Having completed the analysis, a TSO model, whose objective is to maximize fault trackability and mini- mize the test cost, is proposed through inherent testability analysis results, and an adaptive simulated annealing genetic algorithm （ASAGA） is introduced to solve the TSO problem. Finally, a case of a centrifugal pump system is used to verify the feasibility and effectiveness of the proposed models and methods. The results show that the proposed technology is important for PHM systems to select and optimize the test set in order to improve their performance level.     

Direct adaptive control for a class of MIMO nonlinear discrete-time systems

This paper considers the problem of adaptive con-trol for a class of multiple input multiple output （MIMO） nonlinear discrete-time systems based on input-output model with unknown interconnections between subsystems. Based on the Taylor ex-pand technology, an equivalent model in affine-like form is derived for the original nonaffine nonlinear system. Then a direct adap-tive neural network （NN） control er is implemented based on the affine-like model. By finding an orthogonal matrix to tune the NN weights, the closed-loop system is proven to be semiglobal y uni-formly ultimately bounded. The σ-modification technique is used to remove the requirement of persistence excitation during the adaptation. The control performance of the closed-loop system is guaranteed by suitably choosing the design parameters.     

图像自适应分块单幅超分辨率算法

在单幅超分辨率图像重建算法中,基于最大后验估计(maximum a posteriori,MAP)算法重建效果和抗噪性能较好,但时空复杂度较高。为了提高模板卷积MAP(template convolution based MAP,TC-MAP)算法的运行效率,降低内存消耗,提出了基于图像内容的自适应分块TC-MAP新算法,研究了图像分块的最佳尺寸,并根据子块图像的平均梯度,对平滑区域的多个子块进行合并降低分块边界效应的影响,同时采用边界延长进一步抑制分块效应。实验结果表明,算法有效减少了TC-MAP算法的运行时间和内存开销,同时保持重建图像质量与原TC-MAP算法差别不大。