基于NMPC的战斗机末端机动规避策略

利用非线性模型预测控制（nonlinear model predictive control,NMPC）的思想建立了战机末端规避导弹的机动策略求解方法。根据导弹与战机的空战态势,建立了导弹与战机的相对运动微分方程;将导弹的导引律引入到导弹运动模型中,与飞机模型一起构建了系统预测模型,并对飞机和导弹的运动约束进行了分析。通过对导弹结构限制和战术特性的分析,给出了飞机机动规避导弹的性能指标,进而建立了机动规避导弹的最优控制模型。利用高斯伪谱法对模型进行求解,采用滚动优化策略实现了对机动规避策略的闭环求解。针对导弹气动参数和导航比未知以及相对测量量具有噪声的问题,利用极大似然法对导弹的气动参数和导航比进行估计,实现了对系统预测模型的反馈校正。仿真结果表明,此方法能够实现对导弹的机动规避。     

基于图像对比度最优的频率步进ISAR成像方法

在频率步进高分辨ISAR成像中,目标的径向运动会带来距离多普勒耦合,从而对ISAR图像有较大的影响。提出了一种基于图像对比度最优的运动参数估计方法。该法分析了径向速度和径向加速度对多普勒像对比度函数的影响。通过构造相位补偿因子,在多普勒像中基于多普勒像对比度最优估计径向加速度。在径向加速度补偿后,在距离像中基于距离像对比度最优估计径向速度。进行运动补偿,利用RD算法实现了目标的高分辨ISAR成像。该方法具有运动参数估计精度高和计算量小的优点。仿真结果验证了方法的有效性。     

SAR微动目标的稀疏贝叶斯成像方法

SAR微动信息能够反映出目标的属性信息,其微动图像可作为雷达目标识别的一种重要手段。基于SAR微动目标回波的稀疏特性,建立了在过完备词典下的稀疏表示模型,提出一种新的稀疏贝叶斯重构方法——方差成分扩张压缩,该方法仅赋予有重要意义的信号元素不同的方差分量,拥有更少的参数。仿真结果表明,方差成分扩张压缩方法能较精确地估计出SAR目标微动参数,同时能够获得低信噪比条件下较好的微动目标像。     

基于相关性的OFDM盲识别与参数估计方法

针对非协作通信领域OFDM信号识别难的问题,提出一种基于信号循环前缀相关性的盲识别和时域参数估计算法。通过对OFDM信号循环自相关函数进行分析,在时域上求出延时自相关运算函数的波形,利用循环前缀的相关波峰的特性,以区别OFDM信号和单载波信号,实现对OFDM信号的盲识别。在此基础上,通过采用多个OFDM信号构造循环前缀相关性的累加函数所形成的波峰完成对符号和循环前缀长度等参数的估计。     

复杂系统维修性综合评估技术

分析了复杂系统使用期间维修性水平动态变化特点,研究了复杂系统在使用中维修性数据特点、类型和分布确定方法,建立了复杂系统使用期间维修性指标综合评估模型,提出了维修性增长极大似然评估方法,为复杂系统维修性指标计算和评估提供参考。     

科氏流量计信号频率的变步长直接估计算法

研究科氏流量计频率估计方法对提高其测量精度具有重要意义.分析了变步长直接频率估计( VS-DFE)方法的原理,归纳出一种基于VS-DFE的科氏流量计信号频率估计算法,给出了算法基本思想、流程和实现步骤.分别针对两种常见流量状态下科氏流量计的固定频率信号和时变信号进行仿真分析,并与离散频谱校正法进行了比较.仿真结果表明,...     

基于OFD-LFM信号的MIMO雷达微多普勒效应分析

多输入多输出(MIMO)雷达系统利用空间分集技术,能够在不同视角接收目标回波信号,与传统单基雷达系统相比,可以提取更丰富的目标特征信息,因而大大提高了雷达的目标运动参数估计精度。基于MIMO雷达系统,详细推导了正交频分线性调频信号体制下目标部件旋转引入的微多普勒效应,通过取多组观测数据的数学期望对旋转半径进行了参数估计,最后的仿真实验验证了理论分析的结果,得到的旋转半径估值与真实值非常接近。     

非平稳噪声背景下DOA分步估计方法研究

针对相干和非相干信源同时存在的情况,结合斜投影理论和互相关矢量Toeplitz矩阵重构(CVTR)的方法,提出一种新的信源DOA分步估计方法。该方法把相干信源和非相干信源分开分辨,对相干信源用CVTR方法来恢复为满秩,避免了常规平滑算法阵列孔径损失大、运算量大的缺点;用斜投影算子而非差分方法对信源进行分离,不受相关矩阵须为Toeplitz结构的限制,使得算法可适用于任意阵列结构形式。这种分步分辨思路可有效增强信源过载能力,同时在互相关矢量Toeplitz矩阵重构过程中,可把非平稳噪声协方差矩阵转换成白噪声结构,使得算法对非平稳噪声有较好的适应能力。     

Signal‐to‐interference‐plus‐noise ratio estimation for wireless communication systems: Methods and analysis

The Signal‐to‐Interference‐plus‐Noise Ratio (SINR) is an important metric of wireless communication link quality. SINR estimates have several important applications. These include optimizing the transmit power level for a target quality of service, assisting with handoff decisions and dynamically adapting the data rate for wireless Internet applications. Accurate SINR estimation provides for both a more efficient system and a higher user‐perceived quality of service. In this paper, we develop new SINR estimators and compare their mean squared error (MSE) performance. We show that our new estimators dominate estimators that have previously appeared in the literature with respect to MSE. The sequence of transmitted bits in wireless communication systems consists of both pilot bits (which are known both to the transmitter and receiver) and user bits (which are known only by the transmitter). The SINR estimators we consider alternatively depend exclusively on pilot bits, exclusively on user bits, or simultaneously use both pilot and user bits. In addition, we consider estimators that utilize smoothing and feedback mechanisms. Smoothed estimators are motivated by the fact that the interference component of the SINR changes relatively slowly with time, typically with the addition or departure of a user to the system. Feedback estimators are motivated by the fact that receivers typically decode bits correctly with a very high probability, and therefore user bits can be thought of as quasipilot bits. For each estimator discussed, we derive an exact or approximate formula for its MSE. Satterthwaite approximations, noncentral F distributions (singly and doubly) and distribution theory of quadratic forms are the key statistical tools used in developing the MSE formulas. In the case of approximate MSE formulas, we validate their accuracy using simulation techniques. The approximate MSE formulas, of interest in their own right for comparing the quality of the estimators, are also used for optimally combining estimators. In particular, we derive optimal weights for linearly combining an estimator based on pilot bits with an estimator based on user bits. The optimal weights depend on the MSE of the two estimators being combined, and thus the accurate approximate MSE formulas can conveniently be used. The optimal weights also depend on the unknown SINR, and therefore need to be estimated in order to construct a useable combined estimator. The impact on the MSE of the combined estimator due to estimating the weights is examined. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004     

基于正则化的多平台多传感器导航定位和探测偏差估计

针对影响舰载雷达探测误差主要为平台导航定位误差和传感器自身探测误差,对这两类误差的影响因素进行了分析,首次综合考虑这两种系统误差,提出了一种基于正则化的多平台导航定位及探测偏差估计方法,利用不同舰艇上传感器对公共目标探测的迭合条件,建立系统误差观测方程,利用LS对两种系统误差同时进行了估计,并在偏差估计中采用正则化估计方法,提高估计结果的稳定性,最后进行了仿真验证,仿真效果验证了算法的有效性,具有较高的工程应用价值。