定向探测引信技术分析

从提高引信与战斗部配合效率出发 ,根据定向战斗部的战术技术性能 ,介绍了一种能够识别弹体周围 4个方向目标的定向探测引信 .分析了定向探测器的工作原理和目标方位识别的关键技术 ,并对探测天线的设计做了详细的分析和计算 .     

基于放大器非线性拟合的自适应预失真技术

本文提出了一种新的有效的大功率放大器非线性失真的数字自适应预失真设计方法。该方法基于有理双线函数的非线性及其逆变换易于求解的特性，设计大功率放大器的估计器以逼近大功率放大器的非线性特性，预失真器的非线性参数由估计器的逼近函数逐段取逆变换来调节，以达到预失真补偿的目的。计算机仿真结果表明：该方法用于正交频分复用（OFDM）系统中具有收敛速度快、系统性能稳定、补偿效果好等特点。     

用于千兆以太网基带铜缆接收器均衡的甚高频自适应 连续时间Gm-C 二阶带通滤波器的研究和设计

本文设计了用于千兆以太网基带铜缆接收器均衡的甚高频自适应连续时间Gm-C二阶带通滤波器。基于最陡梯度下降算法，带通滤波器的零点在57－340MHz的频率范围内可以自适应地调节，中心频率为1.278GHz。通过外接电阻伺服环路，滤波器中跨导转换器的跨导值不受工艺偏差和温度变化的影响，采用CSMC－HJ0.6μm CMOS工艺器件模型，用Cadence Spectres仿真器仿真了设计的自适应滤波器电路，仿真结果验证了设计原理和设计的电路。系统的最长学习时间为880个参考时钟周期。     

An EM‐based adaptive multiple target tracking filter

Tracking targets of interest is one of the major research areas in radar surveillance systems. We formulate the problem as incomplete data estimation and apply EM to the MAP estimate. The resulting filter has a recursive structure analogous to the Kalman filter. The advantage is that the measurement‐update deals with multiple measurements in parallel and the parameter‐update estimates the system parameters on the fly. Experiments tracking separate targets in parallel show that tracking maintenance ratio of the proposed system is better than that of NNF and RMS position error is smaller than that of PDAF. Also, the system parameters are correctly obtained even from incorrect initial values. Copyright © 2001 John Wiley & Sons, Ltd.     

Comparative Study of Different Adaptive Window Protocols

We study an Adaptive Window Protocol (AWP) with general increase and decrease profiles in the presence of window dependent random losses. We derive a steady-state Kolmogorov equation, and then obtain its solution in analytic form for particular TCP versions proposed for high speed networks, such as Scalable TCP and HighSpeed TCP. We also relate window evolution under an AWP to workload process in queueing systems; this observation gives us a way to compare various AWP protocols.     

INDIRECT ACCELERATED ADAPTIVE FUZZY CONTROLLER

According to a type of normal nonlinear system, an indirect adaptive fuzzy （IAF） controller has been applied to those systems where no accurate mathematical models of the systems under control are available. To satisfy with system performance, an indirect accelerated adaptive fuzzy （IAAF） controller is proposed, and its general form is presented. The general form IAAF controller ensures necessary control criteria and system＇s global stability using Lyapunov Theorem. It has been proved that the close-loop system error converges to a small neighborhood of equilibrium point. The optimal IAAF controller is derived to guarantee the process＇s shortest settling time. Simulation results indicate the IAAF controller make the system more stable, accurate, and fast.     

An adaptive tracking problem for a family of retarded time-delay plants

Most of the existing switching control techniques are developed specifically for finite-dimensional linear time-invariant (LTI) systems. In many practical applications, however, it is essential to take time delay into consideration in the modelling as the control system can be highly sensitive to delay. In this paper, a multi-model switching control algorithm is proposed for retarded time-delay systems. It is assumed that the plant is represented by a family of known multi-input multi-output, observable, LTI models with multiple delays in the states, and that corresponding to each model in the known family, there exists a high-performance finite-dimensional LTI controller. In addition, it is supposed that a bound on the magnitude of the external inputs and disturbances is available. It is then shown that the proposed switching controller can stabilize the uncertain system, and that under some mild conditions, output tracking can be achieved in the given problem setting. Copyright © 2007 John Wiley & Sons, Ltd.     

高分辨率合成孔径雷达图像舰船检测方法

寻找针对高分辨率SAR图像的舰船目标检测算法。利用KSW双阈值分割技术，其效果比传统检测方法好，有利于进一步的目标分类和识别。且必须根据SAR图像分辨率来选择舰船检测算法。     

Kernel Spectral Matched Filter for Hyperspectral Imagery

In this paper a kernel-based nonlinear spectral matched filter is introduced for target detection in hyperspectral imagery, which is implemented by using the ideas in kernel-based learning theory. A spectral matched filter is defined in a feature space of high dimensionality, which is implicitly generated by a nonlinear mapping associated with a kernel function. A kernel version of the matched filter is derived by expressing the spectral matched filter in terms of the vector dot products form and replacing each dot product with a kernel function using the so called kernel trick property of the Mercer kernels. The proposed kernel spectral matched filter is equivalent to a nonlinear matched filter in the original input space, which is capable of generating nonlinear decision boundaries. The kernel version of the linear spectral matched filter is implemented and simulation results on hyperspectral imagery show that the kernel spectral matched filter outperforms the conventional linear matched filter.